CAI 1.1.5 release (#455)

2026-06-05 13:43:19 +02:00 · 2026-06-05 13:43:19 +02:00 · 1c79507140
parent 2963eb2d22
commit 1c79507140
1220 changed files with 270351 additions and 33336 deletions
--- a/.devcontainer/docker-compose.yml
+++ b/.devcontainer/docker-compose.yml
@ -1,4 +1,3 @@
 version: '3'
 #################
 # SERVICES
--- a/.env.example
+++ b/.env.example
@ -3,4 +3,21 @@ ANTHROPIC_API_KEY=""
 OLLAMA=""
 PROMPT_TOOLKIT_NO_CPR=1
 CAI_STREAM=false
-CAI_MODEL="alias1"
+CAI_MODEL="alias1"
 # Model sampling parameters (optional - defaults shown)
 # CAI_TEMPERATURE=0.7
 # CAI_TOP_P=1.0
 # Alias API key (used for alias1, alias2, and alias2-mini)
 ALIAS_API_KEY="sk-your-alias-key-here"
 # Open-source mode. Set to 1/true to:
 #   - bypass the startup license check (no ALIAS_API_KEY required to start),
 #   - route update checks and `cai --update` to public PyPI instead of the
 #     private Alias package index.
 # CAI_LICENSE_OFF=1
 # Set to true ONLY when running an authorised internal pentest. When false (default), fetch_url blocks loopback / RFC1918 / link-local / cloud-metadata hosts and non-http(s) schemes to prevent SSRF via prompt injection.
 CAI_FETCH_ALLOW_INTERNAL=false
 # Override the User-Agent used by fetch_url (OPSEC).
 # CAI_FETCH_USER_AGENT="Mozilla/5.0 ..."
--- a/.github/workflows/gitleaks.yml
+++ b/.github/workflows/gitleaks.yml
@ -0,0 +1,23 @@
 name: gitleaks
 on:
  push:
    branches: [main]
  pull_request:
 jobs:
  scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
      - name: Install gitleaks
        run: |
          GITLEAKS_VERSION=8.21.2
          curl -sSfL "https://github.com/gitleaks/gitleaks/releases/download/v${GITLEAKS_VERSION}/gitleaks_${GITLEAKS_VERSION}_linux_x64.tar.gz" \
            | tar -xz -C /tmp gitleaks
          sudo mv /tmp/gitleaks /usr/local/bin/gitleaks
          gitleaks version
      - name: Run gitleaks
        run: gitleaks detect --config=.gitleaks.toml --no-banner --redact --verbose --no-git
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@ -4,9 +4,11 @@ on:
  push:
    branches:
      - main
      - cai-v1.0.5
  pull_request:
    branches:
      - main
  workflow_dispatch:
 env:
  UV_FROZEN: "1"
@ -40,21 +42,39 @@ jobs:
  #     - name: Run typecheck
  #       run: make mypy
-  # tests:
+  # Lightweight prompt / layering checks (no refusals suite; mirrors GitLab smoke slice)
-  #   runs-on: ubuntu-latest
+  cyber-prompt-smoke:
-  #   env:
+    runs-on: ubuntu-latest
-  #     OPENAI_API_KEY: fake-for-tests
+    env:
-  #   steps:
+      OPENAI_API_KEY: fake-for-tests
-  #     - name: Checkout repository
+    steps:
-  #       uses: actions/checkout@v4
+      - name: Checkout repository
-  #     - name: Setup uv
+        uses: actions/checkout@v4
-  #       uses: astral-sh/setup-uv@v5
+      - name: Setup uv
-  #       with:
+        uses: astral-sh/setup-uv@v5
-  #         enable-cache: true
+        with:
-  #     - name: Install dependencies
+          enable-cache: true
-  #       run: make sync
+      - name: Install dependencies
-  #     - name: Run tests with coverage
+        run: make sync
-  #       run: make coverage
+      - name: Run cyber layering and prompt bundle load tests
        run: uv run pytest tests/util/test_cyber_prompt_layering.py tests/prompts/test_prompt_bundles_load.py -q --timeout=300
  # Heavier: full selection_agent import graph (optional in local runs: pytest -m "not slow")
  selection-agent-smoke:
    runs-on: ubuntu-latest
    env:
      OPENAI_API_KEY: fake-for-tests
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
      - name: Setup uv
        uses: astral-sh/setup-uv@v5
        with:
          enable-cache: true
      - name: Install dependencies
        run: make sync
      - name: Run selection agent smoke tests
        run: uv run pytest tests/agents/test_selection_agent_smoke.py -q --timeout=600
  build-docs:
    runs-on: ubuntu-latest
--- a/.gitignore
+++ b/.gitignore
@ -1,6 +1,8 @@
 # macOS Files
 .DS_Store
 cai_env/
 # DEMOs
 DEMO/
 # Agent Framework Files
 AGENTS.md
 CLAUDE.md
@ -107,6 +109,8 @@ celerybeat.pid
 # Environments
 .env
 .env*
 .env.backup*
 .venv
 env/
 venv/
@ -159,6 +163,9 @@ workspaces/
 # benchmarks
 benchmarks/outputs
 #CTR offline output
 tools/cut_the_rope/ctr_cai/output
 # other
 nohup.out
 .aider*
@ -169,7 +176,22 @@ pentestperf
 venv*
 .claude
 .cursor/
 ova_extract/
 # flows
 agents.yml*
 .env.backup
 # dataset
 data/
-# ignore dev files
+# app
-cai-*
+cai-app-ios/
 learn-claude-code
 gemini-cli
 # runtime artifacts (recon outputs, bench results, refusal traces)
 bench_results/
 network_scan/
 network_scan.*
 refusal_test_output.log
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@ -1,37 +0,0 @@
 stages:
  - build
  - setup
  - test                # unit tests validation
  - benchmarks
  - ctf
 variables:
  DOCKER_HOST: tcp://docker:2375
  DOCKER_DRIVER: overlay2
  DOCKER_TLS_CERTDIR: ""
  VERSION: "0.1"
  DISTRO: ubuntu:22.04
  # CI_DEBUG_TRACE: "true"
  # GIT_FETCH_TIMEOUT: 300
 services:
  - name: docker:dind
    alias: docker
 include:
  - project: 'aliasrobotics/alias_research/cai'
    ref: $CI_COMMIT_REF_NAME
    file:
      # - 'ci/build/.build.yml'                # build
      #- 'ci/setup/.setup.yml'               # setup
      - 'ci/test/.test.yml'
      #- 'ci/benchmarks/.benchmarks.yml'
      # - 'ci/ctfs/.ctf.yml'                  # ctf
  # - project: 'aliasrobotics/alias_research/cai'
  #   ref: $CI_COMMIT_REF_NAME
  #   file: 'ci/test/.test.yml'
  #   rules:
  #     - if: $CI_COMMIT_BRANCH == "main"
  #       when: never
  #     - if: $CI_COMMIT_BRANCH
--- a/.gitleaks.toml
+++ b/.gitleaks.toml
@ -0,0 +1,40 @@
 # Gitleaks configuration for cai-framework.
 #
 # Extends the upstream default ruleset and adds allowlist entries for the
 # placeholder API keys used in .env.example, docs, and test fixtures so the
 # scanner does not red-X every push on known non-secrets.
 #
 # The patterns here mirror the residual-secret filter in prepare-cai-public.sh.
 [extend]
 useDefault = true
 [allowlist]
 description = "Known non-secret placeholders and test fixtures"
 paths = [
  # Test fixtures intentionally contain fake/synthetic keys.
  '''(^|/)tests/.+\.(py|jsonl|json|yaml|yml)$''',
  # Examples reference placeholder values, not real keys.
  '''(^|/)examples/.+''',
  # Documentation describes env vars with placeholder defaults.
  '''(^|/)docs/.+\.(md|txt)$''',
  '''(^|/)README\.md$''',
  '''(^|/)CHANGELOG\.md$''',
  '''(^|/)\.env\.example$''',
  # CAIBench cyber ranges plant fake credentials as the CTF challenge itself.
  # Containers run on isolated docker networks; IPs and secrets are decoys.
  '''(^|/)src/cai/caibench/.+''',
  # CyberPII / CTI / SecEval benchmarks contain synthetic PII and tokens used
  # as detection targets — not real secrets.
  '''(^|/)benchmarks/.+''',
 ]
 # Regex allowlist for known placeholder API key formats. Mirrors the filter
 # in prepare-cai-public.sh so OSS scans don't flag obvious dummies.
 regexes = [
  '''sk-(alias|live|test|proj|openai|anthropic)-(1234567890|0123456789|your[-_].*|placeholder|example|xxx+|fake|dummy|key[-_]?here|planner|mock|fixture|stub|noop|sample)''',
  '''sk-alias-1234567890''',
  # Alias private package index URL (publicly visible, not a secret).
  '''packages\.aliasrobotics\.com''',
 ]
--- a/README.md
+++ b/README.md
@ -1,21 +1,15 @@
 # Cybersecurity AI (`CAI`)
-<div align="center">
+ 
-  <p>
+ 
-    <a align="center" href="" target="https://github.com/aliasrobotics/CAI">
+ 
-      <img
+ 
-        width="100%"
+ 
-        src="https://github.com/aliasrobotics/cai/raw/main/media/cai.png"
+ 
      >
    </a>
  </p>
 <a href="https://trendshift.io/repositories/14317" target="_blank"><img src="https://trendshift.io/api/badge/repositories/14317" alt="aliasrobotics%2Fcai | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
 <a href="https://defiant.vc/api/european-open-source/badge?domain=aliasrobotics.com&style=most-starred-top-3" target="_blank"><img src="https://defiant.vc/api/european-open-source/badge?domain=aliasrobotics.com&style=most-starred-top-3" alt="European Open Source - Most Starred Top 3" style=" height: 75px;" height="75"/></a>
 <a href="https://defiant.vc/api/european-open-source/badge?domain=aliasrobotics.com&style=most-forked-top-3" target="_blank"><img src="https://defiant.vc/api/european-open-source/badge?domain=aliasrobotics.com&style=most-forked-top-3" alt="European Open Source - Most Forked Top 3" style="height: 75px;" height="75"/></a>
 [![version](https://badge.fury.io/py/cai-framework.svg)](https://badge.fury.io/py/cai-framework)
 [![downloads](https://static.pepy.tech/badge/cai-framework)](https://pepy.tech/projects/cai-framework)
@ -33,81 +27,103 @@
 [![arXiv](https://img.shields.io/badge/arXiv-2510.17521-b31b1b.svg)](https://arxiv.org/pdf/2510.17521)
 [![arXiv](https://img.shields.io/badge/arXiv-2510.24317-b31b1b.svg)](https://arxiv.org/pdf/2510.24317)
-</div>
+ 
-<!-- CAI PRO - Professional Edition Banner -->
+ 
-<div align="center">
+ 
-  <a href="https://aliasrobotics.com/cybersecurityai.php" target="_blank">
+ Professional Edition with unlimited alias1 tokens | 📊 View Benchmarks | 🚀 Learn More 
    <img src="media/cai-banner.svg" alt="CAI - Community and Professional Editions" width="100%" style="max-width: 900px;">
  </a>
-  <sub><i>Professional Edition with unlimited <code>alias1</code> tokens</i> | <a href="https://aliasrobotics.com/alias1.php#benchmarking">📊 View Benchmarks</a> | <a href="https://aliasrobotics.com/cybersecurityai.php">🚀 Learn More</a></sub>
+ 
-  <table style="border-collapse: collapse; width: 100%">
+ 
-    <tr>
+ 
-      <td width="50%" align="center" style="padding: 0; border: none;">
+ 
-        <img src="media/cai_poc.gif" alt="CAI Community Edition Demo" width="100%">
+ 
-      </td>
+ 
-      <td width="50%" align="center" style="padding: 0; border: none;">
+ 🔓 Community Edition 
-        <img src="media/caipro_poc.gif" alt="CAI PRO Professional Edition Demo" width="100%">
+ Research & Learning · Perfect for Researchers & Students 
-      </td>
+ pip install cai-framework 
-    </tr>
+ 
-  </table>  
+ ✅ Free for research 
-</div>
+ 🤖 300+ AI models 
-
+ 🌍 Community driven 
-<!-- Alternative HTML version (kept as comment for reference) -->
+ 📚 Open source 
-<!--
+ 🔧 Extensible framework 
-<div align="center">
+ 
-  <table style="border-collapse: collapse; width: 100%; max-width: 900px; box-shadow: 0 4px 12px rgba(82, 157, 134, 0.15);">
+ 
-    <tr>
+ 
-      <td align="center" width="50%" style="padding: 20px; border: 3px solid #529d86; border-right: 1.5px solid #529d86; border-radius: 10px 0 0 10px; background: linear-gradient(135deg, #f0f8f6 0%, #ffffff 100%);">
+ 🚀 Professional Edition 
-        <h3 style="color: #3d7b6b;">🔓 Community Edition</h3>
+ Enterprise & Production · €350/month · Unlimited alias1 Tokens 
-        <sub style="color: #529d86;"><b>Research & Learning · Perfect for Researchers & Students</b></sub><br><br>
+ 
-        <code style="background: linear-gradient(135deg, #e8f5f1 0%, #d4ede5 100%); padding: 8px 16px; border-radius: 6px; font-size: 14px; border: 1px solid #529d86; color: #2d5a4d;">pip install cai-framework</code><br><br>
+ → Upgrade to PRO 
-        <div align="left" style="margin: 10px auto; max-width: 200px; color: #2d2d2d;">
+ 
-          ✅ <b style="color: #529d86;">Free</b> for research<br>
+ 
-          🤖 <b style="color: #529d86;">300+</b> AI models<br>
+ ⚡ alias1 model - ∞ unlimited tokens 
-          🌍 <b style="color: #529d86;">Community</b> driven<br>
+ 🚫 Zero refusals - Unrestricted AI 
-          📚 <b style="color: #529d86;">Open</b> source<br>
+ 🏆 Beats GPT-5 in CTF benchmarks 
-          🔧 <b style="color: #529d86;">Extensible</b> framework<br>
+ 🛡️ Professional support included 
-        </div>
+ 🇪🇺 European data sovereignty 
-      </td>
+ 
-      <td align="center" width="50%" style="padding: 20px; border: 3px solid #529d86; border-left: 1.5px solid #529d86; border-radius: 0 10px 10px 0; background: linear-gradient(135deg, #529d86 0%, #6bb09a 100%); position: relative; box-shadow: inset 0 0 30px rgba(255, 255, 255, 0.1);">
+ 
-        <h3 style="color: #ffffff; text-shadow: 0 2px 4px rgba(0, 0, 0, 0.2);">🚀 <a href="https://aliasrobotics.com/cybersecurityai.php" style="text-decoration: none; color: #ffffff;">Professional Edition</a></h3>
+ 
-        <sub style="color: #e8f5f1;"><b>Enterprise & Production · €350/month · Unlimited <code style="background: rgba(255, 255, 255, 0.2); padding: 2px 6px; border-radius: 3px; color: #ffffff;">alias1</code> Tokens</b></sub><br><br>
+ 
-        <a href="https://aliasrobotics.com/cybersecurityai.php">
+ 
-          <code style="background: linear-gradient(135deg, #ffffff 0%, #f0f8f6 100%); color: #529d86; padding: 10px 20px; border-radius: 6px; font-size: 14px; font-weight: bold; border: 2px solid #ffffff; box-shadow: 0 2px 8px rgba(0, 0, 0, 0.2);">→ Upgrade to PRO</code>
+ 
-        </a><br><br>
+ 
-        <div align="left" style="margin: 10px auto; max-width: 280px; color: #ffffff;">
+ CAI PRO w/ alias1 model outperforms GPT-5 in AI vs AI cybersecurity benchmarks | View Full Benchmarks → 
-          ⚡ <b><a href="https://aliasrobotics.com/alias1.php#benchmarking" style="color: #ffffff; text-decoration: underline;">alias1</a></b> model - ∞ unlimited tokens<br>
+ 
-          🚫 <b>Zero refusals</b> - Unrestricted AI<br>
+ 
-          🏆 <b>Beats GPT-5</b> in CTF benchmarks<br>
+ 
-          🛡️ <b>Professional</b> support included<br>
+ 
-          🇪🇺 <b>European</b> data sovereignty<br>
+ 
        </div>
      </td>
    </tr>
    <tr>
      <td colspan="2" align="center" style="padding: 10px; background: #f6f8fa;">
        <sub>
          <a href="https://aliasrobotics.com/cybersecurityai.php"></a><br>
          <i>CAI PRO w/ <code>alias1</code> model outperforms GPT-5 in AI vs AI cybersecurity benchmarks</i> | <a href="https://aliasrobotics.com/alias1.php#benchmarking">View Full Benchmarks →</a>
        </sub>
      </td>
    </tr>
  </table>
 </div>
 -->
 Cybersecurity AI (CAI) is a lightweight, open-source framework that empowers security professionals to build and deploy AI-powered offensive and defensive automation. CAI is the *de facto* framework for AI Security, already used by thousands of individual users and hundreds of organizations. Whether you're a security researcher, ethical hacker, IT professional, or organization looking to enhance your security posture, CAI provides the building blocks to create specialized AI agents that can assist with mitigation, vulnerability discovery, exploitation, and security assessment.
 ## 🔓 Run CAI without an Alias API Key (`CAI_LICENSE_OFF`)
 CAI can run **without an `ALIAS_API_KEY`** (i.e. without an Alias Robotics license) by setting the environment variable **`CAI_LICENSE_OFF=1`**.
 When `CAI_LICENSE_OFF` is set to `1`, `true`, or `yes`:
 - The startup license check is **bypassed** — no `ALIAS_API_KEY` is required.
 - CAI runs in **open-source mode** and update operations target the public PyPI `cai-framework` package instead of the private Alias package index.
 - You can use any other supported model provider (OpenAI, Anthropic, DeepSeek, Ollama, etc.) by configuring `CAI_MODEL` and the corresponding provider API key.
 Quick start without a license:
 ```bash
 export CAI_LICENSE_OFF=1
 cai
 ```
 Or inline:
 ```bash
 CAI_LICENSE_OFF=1 cai
 ```
 > Note: The `alias1` model still requires a valid `ALIAS_API_KEY`. `CAI_LICENSE_OFF` only bypasses the framework's license gate — it does not grant access to Alias-hosted models.
 **Key Features:**
 - 🤖 **300+ AI Models**: Support for OpenAI, Anthropic, DeepSeek, Ollama, and more
- 🔧 **Built-in Security Tools**: Ready-to-use tools for reconnaissance, exploitation, and privilege escalation  
+- 🔧 **Built-in Security Tools**: Ready-to-use tools for reconnaissance, exploitation, and privilege escalation 
 - 🏆 **Battle-tested**: Proven in HackTheBox CTFs, bug bounties, and real-world security [case studies](https://aliasrobotics.com/case-studies-robot-cybersecurity.php)
 - 🎯 **Agent-based Architecture**: Modular framework design to build specialized agents for different security tasks
 - 🛡️ **Guardrails Protection**: Built-in defenses against prompt injection and dangerous command execution
@ -118,8 +134,6 @@ Cybersecurity AI (CAI) is a lightweight, open-source framework that empowers sec
 >
 > For further readings, refer to our [impact](#-impact) and [CAI citation](#citation) sections.
 | [`Robotics` - CAI and alias1 on: Unitree G1 Humanoid Robot](https://aliasrobotics.com/case-study-humanoid-robot-g1.php) | [`OT` - CAI and alias1 on: Dragos OT CTF 2025](https://aliasrobotics.com/case-study-dragos-CTF.php) |
 |------------------------------------------------|---------------------------------|
 | CAI uncovers vulnerabilities and privacy violations in Unitree G1 humanoid robots including unauthorized telemetry transmission to China-related servers, exposed RSA keys with world-writable permissions, and potential surveillance capabilities violating GDPR and international privacy laws. | CAI powered by alias1, demonstrates exceptional performance in operational technology cybersecurity by achieving a Top-10 ranking in the Dragos OT CTF 2025. The AI agent reached Rank 1 during competition hours 7-8, completed 32 of 34 challenges, and maintained a 37% velocity advantage over top human teams. |
@ -127,75 +141,71 @@ Cybersecurity AI (CAI) is a lightweight, open-source framework that empowers sec
 | [`IT` (Bug Bounty) - CAI on: HackerOne Platform](https://aliasrobotics.com/case-study-hackerone.php) | [`OT` - CAI and alias0 on: Ecoforest Heat Pumps](https://aliasrobotics.com/case-study-ecoforest.php) |
 |------------------------------------------------|---------------------------------|
-| HackerOne's top engineers leverage CAI to explore next-gen agentic AI architectures and build their own security products. CAI's Retester agent directly inspired HackerOne's AI-powered Deduplication Agent, now deployed in production to handle millions of vulnerability reports at scale. | CAI discovers critical vulnerability in Ecoforest heat pumps allowing unauthorized remote access and potential catastrophic failures. AI-powered security testing reveals exposed credentials and DES encryption weaknesses affecting all of their deployed units across Europe.  |
+| HackerOne's top engineers leverage CAI to explore next-gen agentic AI architectures and build their own security products. CAI's Retester agent directly inspired HackerOne's AI-powered Deduplication Agent, now deployed in production to handle millions of vulnerability reports at scale. | CAI discovers critical vulnerability in Ecoforest heat pumps allowing unauthorized remote access and potential catastrophic failures. AI-powered security testing reveals exposed credentials and DES encryption weaknesses affecting all of their deployed units across Europe. |
 | [![](docs/assets/images/case-study-hackerone.png)](https://aliasrobotics.com/case-study-hackerone.php) | [![](https://aliasrobotics.com/img/case-study-portada-ecoforest.png)](https://aliasrobotics.com/case-study-ecoforest.php) |
 | [`Robotics` - CAI and alias0 on: Mobile Industrial Robots (MiR)](https://aliasrobotics.com/case-study-cai-mir.php) | [`IT` (Web) - CAI and alias0 on: Mercado Libre's e-commerce](https://aliasrobotics.com/case-study-mercado-libre.php) |
 |------------------------------------------------|---------------------------------|
-| CAI-powered security testing of MiR (Mobile Industrial Robot) platform through automated ROS message injection attacks. This study demonstrates how AI-driven vulnerability discovery can expose unauthorized access to robot control systems and alarm triggers.  |  CAI-powered API vulnerability discovery at Mercado Libre through automated enumeration attacks. This study demonstrates how AI-driven security testing can expose user data exposure risks in e-commerce platforms at scale.  |
+| CAI-powered security testing of MiR (Mobile Industrial Robot) platform through automated ROS message injection attacks. This study demonstrates how AI-driven vulnerability discovery can expose unauthorized access to robot control systems and alarm triggers. | CAI-powered API vulnerability discovery at Mercado Libre through automated enumeration attacks. This study demonstrates how AI-driven security testing can expose user data exposure risks in e-commerce platforms at scale. |
 | [![](https://aliasrobotics.com/img/case-study-portada-mir-cai.png)](https://aliasrobotics.com/case-study-cai-mir.php) | [![](https://aliasrobotics.com/img/case-study-portada-mercado-libre.png)](https://aliasrobotics.com/case-study-mercado-libre.php) |
 | [`OT` - CAI and alias0 on: MQTT broker](https://aliasrobotics.com/case-study-cai-mqtt-broker.php) | [`IT` (Web) - CAI and alias0 on: PortSwigger Web Security Academy](https://aliasrobotics.com/case-study-portswigger-1.php) |
 |------------------------------------------------|---------------------------------|
-|  CAI-powered testing exposed critical flaws in an MQTT broker within a Dockerized OT network. Without authentication, CAI subscribed to temperature and humidity topics and injected false values, corrupting data shown in Grafana dashboards. | CAI-powered race condition exploitation in file upload vulnerability. This study demonstrates how AI-driven security testing can identify and exploit timing windows in web applications, successfully uploading and executing web shells through automated parallel requests. |
+| CAI-powered testing exposed critical flaws in an MQTT broker within a Dockerized OT network. Without authentication, CAI subscribed to temperature and humidity topics and injected false values, corrupting data shown in Grafana dashboards. | CAI-powered race condition exploitation in file upload vulnerability. This study demonstrates how AI-driven security testing can identify and exploit timing windows in web applications, successfully uploading and executing web shells through automated parallel requests. |
 | [![](https://aliasrobotics.com/img/case-study-portada-mqtt-broker-cai.png)](https://aliasrobotics.com/case-study-cai-mqtt-broker.php) | [![](docs/assets/images/portada-portswigger-web-1.jpg)](https://aliasrobotics.com/case-study-portswigger-1.php) |
 > [!WARNING]
 > :warning: CAI is in active development, so don't expect it to work flawlessly. Instead, contribute by raising an issue or [sending a PR](https://github.com/aliasrobotics/cai/pulls).
 >
-> Access to this library and the use of information, materials (or portions thereof), is **<u>not intended</u>, and is <u>prohibited</u>, where such access or use violates applicable laws or regulations**. By no means the authors encourage or promote the unauthorized tampering with running systems. This can cause serious human harm and material damages.
+> Access to this library and the use of information, materials (or portions thereof), is ** not intended, and is prohibited, where such access or use violates applicable laws or regulations**. By no means the authors encourage or promote the unauthorized tampering with running systems. This can cause serious human harm and material damages.
 >
-> *By no means the authors of CAI encourage or promote the unauthorized tampering with compute systems. Please don't use the source code in here for cybercrime. <u>Pentest for good instead</u>*. By downloading, using, or modifying this source code, you agree to the terms of the [`LICENSE`](LICENSE) and the limitations outlined in the [`DISCLAIMER`](DISCLAIMER) file.
+> *By no means the authors of CAI encourage or promote the unauthorized tampering with compute systems. Please don't use the source code in here for cybercrime. Pentest for good instead *. By downloading, using, or modifying this source code, you agree to the terms of the [`LICENSE`](LICENSE) and the limitations outlined in the [`DISCLAIMER`](DISCLAIMER) file.
 ## :bookmark: Table of Contents
 - [Cybersecurity AI (`CAI`)](#cybersecurity-ai-cai)
-  - [:bookmark: Table of Contents](#bookmark-table-of-contents)
+ - [:bookmark: Table of Contents](#bookmark-table-of-contents)
-  - [🎯 Impact](#-impact)
+ - [🎯 Impact](#-impact)
-    - [🏆 Competitions and challenges](#-competitions-and-challenges)
+ - [🏆 Competitions and challenges](#-competitions-and-challenges)
-    - [📊 Research Impact](#-research-impact)
+ - [📊 Research Impact](#-research-impact)
-    - [📚 Research products: `Cybersecurity AI`](#-research-products-cybersecurity-ai)
+ - [📚 Research products: `Cybersecurity AI`](#-research-products-cybersecurity-ai)
-  - [PoCs](#pocs)
+ - [PoCs](#pocs)
-  - [Motivation](#motivation)
+ - [Motivation](#motivation)
-    - [:bust\_in\_silhouette: Why CAI?](#bust_in_silhouette-why-cai)
+ - [:bust\_in\_silhouette: Why CAI?](#bust_in_silhouette-why-cai)
-    - [Ethical principles behind CAI](#ethical-principles-behind-cai)
+ - [Ethical principles behind CAI](#ethical-principles-behind-cai)
-    - [Closed-source alternatives](#closed-source-alternatives)
+ - [Closed-source alternatives](#closed-source-alternatives)
-  - [Learn - `CAI` Fluency](#learn---cai-fluency)
+ - [Learn - `CAI` Fluency](#learn---cai-fluency)
-  - [:nut\_and\_bolt: Install](#nut_and_bolt-install)
+ - [:nut\_and\_bolt: Install](#nut_and_bolt-install)
-    - [OS X](#os-x)
+ - [OS X](#os-x)
-    - [Ubuntu 24.04](#ubuntu-2404)
+ - [Ubuntu 24.04](#ubuntu-2404)
-    - [Ubuntu 20.04](#ubuntu-2004)
+ - [Ubuntu 20.04](#ubuntu-2004)
-    - [Windows WSL](#windows-wsl)
+ - [Windows WSL](#windows-wsl)
-    - [Android](#android)
+ - [Android](#android)
-    - [:nut\_and\_bolt: Setup `.env` file](#nut_and_bolt-setup-env-file)
+ - [:nut\_and\_bolt: Setup `.env` file](#nut_and_bolt-setup-env-file)
-    - [🔹 Custom OpenAI Base URL Support](#-custom-openai-base-url-support)
+ - [🔹 Custom OpenAI Base URL Support](#-custom-openai-base-url-support)
-  - [:triangular\_ruler: Architecture:](#triangular_ruler-architecture)
+ - [:triangular\_ruler: Architecture:](#triangular_ruler-architecture)
-    - [🔹 Agent](#-agent)
+ - [🔹 Agent](#-agent)
-    - [🔹 Tools](#-tools)
+ - [🔹 Tools](#-tools)
-    - [🔹 Handoffs](#-handoffs)
+ - [🔹 Handoffs](#-handoffs)
-    - [🔹 Patterns](#-patterns)
+ - [🔹 Patterns](#-patterns)
-    - [🔹 Turns and Interactions](#-turns-and-interactions)
+ - [🔹 Turns and Interactions](#-turns-and-interactions)
-    - [🔹 Tracing](#-tracing)
+ - [🔹 Tracing](#-tracing)
-    - [🔹 Guardrails](#-guardrails)
+ - [🔹 Guardrails](#-guardrails)
-    - [🔹 Human-In-The-Loop (HITL)](#-human-in-the-loop-hitl)
+ - [🔹 Human-In-The-Loop (HITL)](#-human-in-the-loop-hitl)
-  - [:rocket: Quickstart](#rocket-quickstart)
+ - [:rocket: Quickstart](#rocket-quickstart)
-    - [Environment Variables](#environment-variables)
+ - [Environment Variables](#environment-variables)
-    - [OpenRouter Integration](#openrouter-integration)
+ - [OpenRouter Integration](#openrouter-integration)
-    - [Azure OpenAI](#azure-openai)
+ - [Azure OpenAI](#azure-openai)
-    - [MCP](#mcp)
+ - [MCP](#mcp)
-  - [Development](#development)
+ - [Development](#development)
-    - [Contributions](#contributions)
+ - [Contributions](#contributions)
-    - [Optional Requirements: caiextensions](#optional-requirements-caiextensions)
+ - [Optional Requirements: caiextensions](#optional-requirements-caiextensions)
-    - [:information\_source: Usage Data Collection](#information_source-usage-data-collection)
+ - [:information\_source: Usage Data Collection](#information_source-usage-data-collection)
-    - [Reproduce CI-Setup locally](#reproduce-ci-setup-locally)
+ - [Reproduce CI-Setup locally](#reproduce-ci-setup-locally)
-  - [FAQ](#faq)
+ - [FAQ](#faq)
-  - [Citation](#citation)
+ - [Citation](#citation)
-  - [Acknowledgements](#acknowledgements)
+ - [Acknowledgements](#acknowledgements)
-    - [Academic Collaborations](#academic-collaborations)
+ - [Academic Collaborations](#academic-collaborations)
 ## 🎯 Impact
@ -217,32 +227,27 @@ Cybersecurity AI (CAI) is a lightweight, open-source framework that empowers sec
 - Demonstrated **3,600× performance improvement** over human penetration testers in standardized CTF benchmark evaluations [![arXiv](https://img.shields.io/badge/arXiv-2504.06017-63bfab.svg)](https://arxiv.org/pdf/2504.06017)
 - Identified **CVSS 4.3-7.5 severity vulnerabilities** in production systems through automated security assessment [![arXiv](https://img.shields.io/badge/arXiv-2504.06017-63bfab.svg)](https://arxiv.org/pdf/2504.06017)
 - **Democratization of AI-empowered vulnerability research**: CAI enables both non-security domain experts and experienced researchers to conduct more efficient vulnerability discovery, expanding the security research community while empowering small and medium enterprises to conduct autonomous security assessments [![arXiv](https://img.shields.io/badge/arXiv-2504.06017-63bfab.svg)](https://arxiv.org/pdf/2504.06017)
- **Systematic evaluation of large language models** across both proprietary and open-weight architectures, revealing <u>substantial gaps</u> between vendor-reported capabilities and empirical cybersecurity performance metrics [![arXiv](https://img.shields.io/badge/arXiv-2504.06017-63bfab.svg)](https://arxiv.org/pdf/2504.06017)
+- **Systematic evaluation of large language models** across both proprietary and open-weight architectures, revealing substantial gaps between vendor-reported capabilities and empirical cybersecurity performance metrics [![arXiv](https://img.shields.io/badge/arXiv-2504.06017-63bfab.svg)](https://arxiv.org/pdf/2504.06017)
 - Established the **autonomy levels in cybersecurity** and argued about autonomy vs automation in the field [![arXiv](https://img.shields.io/badge/arXiv-2506.23592-7dd3c0.svg)](https://arxiv.org/abs/2506.23592)
 - **Collaborative research initiatives** with international academic institutions focused on developing cybersecurity education curricula and training methodologies [![arXiv](https://img.shields.io/badge/arXiv-2508.13588-52a896.svg)](https://arxiv.org/abs/2508.13588)
 - **Contributed a comprehensive defense framework against prompt injection in AI security agents**: developed and empirically validated a multi-layered defense system that addresses the identified prompt injection issues [![arXiv](https://img.shields.io/badge/arXiv-2508.21669-85e0d1.svg)](https://arxiv.org/abs/2508.21669)
 - Explord the Cybersecurity of Humanoid Robots with CAI and identified new attack vectors showing how it `(a)` operates simultaneously as a covert surveillance node and `(b)` can be purposed as an active cyber operations platform [![arXiv](https://img.shields.io/badge/arXiv-2509.14096-3e8b7a.svg)](https://arxiv.org/abs/2509.14096) [![arXiv](https://img.shields.io/badge/arXiv-2509.14139-6bc7b5.svg)](https://arxiv.org/abs/2509.14139)
 ### 📚 Research products: `Cybersecurity AI`
-|  CAI, An Open, Bug Bounty-Ready Cybersecurity AI [![arXiv](https://img.shields.io/badge/arXiv-2504.06017-63bfab.svg)](https://arxiv.org/pdf/2504.06017) |  The Dangerous Gap Between Automation and Autonomy [![arXiv](https://img.shields.io/badge/arXiv-2506.23592-7dd3c0.svg)](https://arxiv.org/abs/2506.23592) |  CAI Fluency, A Framework for Cybersecurity AI Fluency [![arXiv](https://img.shields.io/badge/arXiv-2508.13588-52a896.svg)](https://arxiv.org/abs/2508.13588) | Hacking the AI Hackers via Prompt Injection [![arXiv](https://img.shields.io/badge/arXiv-2508.21669-85e0d1.svg)](https://arxiv.org/abs/2508.21669) |
+| CAI, An Open, Bug Bounty-Ready Cybersecurity AI [![arXiv](https://img.shields.io/badge/arXiv-2504.06017-63bfab.svg)](https://arxiv.org/pdf/2504.06017) | The Dangerous Gap Between Automation and Autonomy [![arXiv](https://img.shields.io/badge/arXiv-2506.23592-7dd3c0.svg)](https://arxiv.org/abs/2506.23592) | CAI Fluency, A Framework for Cybersecurity AI Fluency [![arXiv](https://img.shields.io/badge/arXiv-2508.13588-52a896.svg)](https://arxiv.org/abs/2508.13588) | Hacking the AI Hackers via Prompt Injection [![arXiv](https://img.shields.io/badge/arXiv-2508.21669-85e0d1.svg)](https://arxiv.org/abs/2508.21669) |
 |---|---|---|---|
-| [<img src="https://aliasrobotics.com/img/paper-cai.png" width="350">](https://arxiv.org/pdf/2504.06017) | [<img src="https://aliasrobotics.com/img/cai_automation_vs_autonomy.png" width="350">](https://www.arxiv.org/pdf/2506.23592) | [<img src="https://aliasrobotics.com/img/cai_fluency_cover.png" width="350">](https://arxiv.org/pdf/2508.13588) | [<img src="https://aliasrobotics.com/img/aihackers.jpeg" width="350">](https://arxiv.org/pdf/2508.21669) |
+| [](https://arxiv.org/pdf/2504.06017) | [](https://www.arxiv.org/pdf/2506.23592) | [](https://arxiv.org/pdf/2508.13588) | [](https://arxiv.org/pdf/2508.21669) |
-
+ | Humanoid Robots as Attack Vectors [![arXiv](https://img.shields.io/badge/arXiv-2509.14139-6bc7b5.svg)](https://arxiv.org/abs/2509.14139) | The Cybersecurity of a Humanoid Robot [![arXiv](https://img.shields.io/badge/arXiv-2509.14096-3e8b7a.svg)](https://arxiv.org/abs/2509.14096) | Evaluating Agentic Cybersecurity in Attack/Defense CTFs [![arXiv](https://img.shields.io/badge/arXiv-2510.17521-b31b1b.svg)](https://arxiv.org/abs/2510.17521) | CAIBench: A Meta-Benchmark for Evaluating Cybersecurity AI Agents [![arXiv](https://img.shields.io/badge/arXiv-2510.24317-b31b1b.svg)](https://arxiv.org/abs/2510.24317) |
 | Humanoid Robots as Attack Vectors [![arXiv](https://img.shields.io/badge/arXiv-2509.14139-6bc7b5.svg)](https://arxiv.org/abs/2509.14139) | The Cybersecurity of a Humanoid Robot [![arXiv](https://img.shields.io/badge/arXiv-2509.14096-3e8b7a.svg)](https://arxiv.org/abs/2509.14096) |   Evaluating Agentic Cybersecurity in Attack/Defense CTFs [![arXiv](https://img.shields.io/badge/arXiv-2510.17521-b31b1b.svg)](https://arxiv.org/abs/2510.17521) | CAIBench: A Meta-Benchmark for Evaluating Cybersecurity AI Agents [![arXiv](https://img.shields.io/badge/arXiv-2510.24317-b31b1b.svg)](https://arxiv.org/abs/2510.24317) |
 |---|---|---|---|
-|  [<img src="https://aliasrobotics.com/img/humanoids-cover.png" width="350">](https://arxiv.org/pdf/2509.14139) | [<img src="https://aliasrobotics.com/img/humanoid.png" width="350">](https://arxiv.org/pdf/2509.14096) | [<img src="https://aliasrobotics.com/img/cai_ad.png" width="350">](https://arxiv.org/pdf/2510.17521) | [<img src="https://aliasrobotics.com/img/caibench_banner2.png" width="350">](https://arxiv.org/pdf/2510.24317) |
+| [](https://arxiv.org/pdf/2509.14139) | [](https://arxiv.org/pdf/2509.14096) | [](https://arxiv.org/pdf/2510.17521) | [](https://arxiv.org/pdf/2510.24317) |
 ## PoCs
 | CAI with `alias0` on ROS message injection attacks in MiR-100 robot | CAI with `alias0` on API vulnerability discovery at Mercado Libre |
 |-----------------------------------------------|---------------------------------|
 | [![asciicast](https://asciinema.org/a/dNv705hZel2Rzrw0cju9HBGPh.svg)](https://asciinema.org/a/dNv705hZel2Rzrw0cju9HBGPh) | [![asciicast](https://asciinema.org/a/9Hc9z1uFcdNjqP3bY5y7wO1Ww.svg)](https://asciinema.org/a/9Hc9z1uFcdNjqP3bY5y7wO1Ww) |
 | CAI on JWT@PortSwigger CTF — Cybersecurity AI | CAI on HackableII Boot2Root CTF — Cybersecurity AI |
 |-----------------------------------------------|---------------------------------|
 | [![asciicast](https://asciinema.org/a/713487.svg)](https://asciinema.org/a/713487) | [![asciicast](https://asciinema.org/a/713485.svg)](https://asciinema.org/a/713485) |
@ -273,11 +278,10 @@ CAI is built on the following core principles:
 - **Tool-integration**: CAI integrates already built-in tools, and allows the user to integrate their own tools with their own logic easily.
 - **Logging and tracing integrated**: using [`phoenix`](https://github.com/Arize-ai/phoenix), the open source tracing and logging tool for LLMs. This provides the user with a detailed traceability of the agents and their execution.
 - **Multi-Model Support**: more than 300 supported and empowered by [LiteLLM](https://github.com/BerriAI/litellm). The most popular providers:
-  - **Anthropic**: `Claude 3.7`, `Claude 3.5`, `Claude 3`, `Claude 3 Opus`
+ - **Anthropic**: `Claude 3.7`, `Claude 3.5`, `Claude 3`, `Claude 3 Opus`
-  - **OpenAI**: `O1`, `O1 Mini`, `O3 Mini`, `GPT-4o`, `GPT-4.5 Preview`
+ - **OpenAI**: `O1`, `O1 Mini`, `O3 Mini`, `GPT-4o`, `GPT-4.5 Preview`
-  - **DeepSeek**: `DeepSeek V3`, `DeepSeek R1`
+ - **DeepSeek**: `DeepSeek V3`, `DeepSeek R1`
-  - **Ollama**: `Qwen2.5 72B`, `Qwen2.5 14B`, etc
+ - **Ollama**: `Qwen2.5 72B`, `Qwen2.5 14B`, etc
 ### Closed-source alternatives
 Cybersecurity AI is a critical field, yet many groups are misguidedly pursuing it through closed-source methods for pure economic return, leveraging similar techniques and building upon existing closed-source (*often third-party owned*) models. This approach not only squanders valuable engineering resources but also represents an economic waste and results in redundant efforts, as they often end up reinventing the wheel. Here are some of the closed-source initiatives we keep track of and attempting to leverage genAI and agentic frameworks in cybersecurity AI:
@ -307,44 +311,35 @@ Cybersecurity AI is a critical field, yet many groups are misguidedly pursuing i
 - [Zynap](https://www.zynap.com)
 - [7ai](https://7ai.com)
 ## Learn - `CAI` Fluency
-<div align="center">
+ 
-  <p>
+ 
-    <a align="center" href="" target="https://github.com/aliasrobotics/CAI">
+ 
-      <img
+ 
-        width="100%"
+ 
-        src="https://github.com/aliasrobotics/cai/raw/main/media/caiedu.PNG"
+ 
-      >
+ 
    </a>
  </p>
 </div>
 > [!NOTE]
 >
 > CAI Fluency technical report ([arXiv:2508.13588](https://arxiv.org/pdf/2508.13588)) establishes formal educational frameworks for cybersecurity AI literacy.
-
+| | Description | English | Spanish |
 |       |   Description  | English | Spanish |
 |-------|----------------|---------|---------|
-| **Episode 0**: What is CAI? | Cybersecurity AI (`CAI`) explained  |  [![Watch the video](https://img.youtube.com/vi/nBdTxbKM4oo/0.jpg)](https://www.youtube.com/watch?v=nBdTxbKM4oo) | [![Watch the video](https://img.youtube.com/vi/FaUL9HXrQ5k/0.jpg)](https://www.youtube.com/watch?v=FaUL9HXrQ5k) |
+| **Episode 0**: What is CAI? | Cybersecurity AI (`CAI`) explained | [![Watch the video](https://img.youtube.com/vi/nBdTxbKM4oo/0.jpg)](https://www.youtube.com/watch?v=nBdTxbKM4oo) | [![Watch the video](https://img.youtube.com/vi/FaUL9HXrQ5k/0.jpg)](https://www.youtube.com/watch?v=FaUL9HXrQ5k) |
-| **Episode 1**: The `CAI` Framework | Vision & Ethics - Explore the core motivation behind CAI and delve into the crucial ethical principles guiding its development. Understand the motivation behind CAI and how you can actively contribute to the future of cybersecurity and the CAI framework. | [![Watch the video](https://img.youtube.com/vi/QEiGdsMf29M/0.jpg)](https://www.youtube.com/watch?v=QEiGdsMf29M&list=PLLc16OUiZWd4RuFdN5_Wx9xwjCVVbopzr&index=3) |  |
+| **Episode 1**: The `CAI` Framework | Vision & Ethics - Explore the core motivation behind CAI and delve into the crucial ethical principles guiding its development. Understand the motivation behind CAI and how you can actively contribute to the future of cybersecurity and the CAI framework. | [![Watch the video](https://img.youtube.com/vi/QEiGdsMf29M/0.jpg)](https://www.youtube.com/watch?v=QEiGdsMf29M&list=PLLc16OUiZWd4RuFdN5_Wx9xwjCVVbopzr&index=3) | |
-| **Episode 2**: From Zero to Cyber Hero | Breaking into Cybersecurity with AI - A comprehensive guide for complete beginners to become cybersecurity practitioners using CAI and AI tools. Learn how to leverage artificial intelligence to accelerate your cybersecurity learning journey, from understanding basic security concepts to performing real-world security assessments, all without requiring prior cybersecurity experience. | [![Watch the video](https://img.youtube.com/vi/hSTLHOOcQoY/0.jpg)](https://www.youtube.com/watch?v=hSTLHOOcQoY&list=PLLc16OUiZWd4RuFdN5_Wx9xwjCVVbopzr&index=14) |  |
+| **Episode 2**: From Zero to Cyber Hero | Breaking into Cybersecurity with AI - A comprehensive guide for complete beginners to become cybersecurity practitioners using CAI and AI tools. Learn how to leverage artificial intelligence to accelerate your cybersecurity learning journey, from understanding basic security concepts to performing real-world security assessments, all without requiring prior cybersecurity experience. | [![Watch the video](https://img.youtube.com/vi/hSTLHOOcQoY/0.jpg)](https://www.youtube.com/watch?v=hSTLHOOcQoY&list=PLLc16OUiZWd4RuFdN5_Wx9xwjCVVbopzr&index=14) | |
 | **Episode 3**: Vibe-Hacking Tutorial | "My first Hack" - A Vibe-Hacking guide for newbies. We demonstrate a simple web security hack using a default agent and show how to leverage tools and interpret CAI output with the help of the CAI Python API. You'll also learn to compare different LLM models to find the best fit for your hacking endeavors. | [![Watch the video](https://img.youtube.com/vi/9vZ_Iyex7uI/0.jpg)](https://www.youtube.com/watch?v=9vZ_Iyex7uI&list=PLLc16OUiZWd4RuFdN5_Wx9xwjCVVbopzr&index=1) | [![Watch the video](https://img.youtube.com/vi/iAOMaI1ftiA/0.jpg)](https://www.youtube.com/watch?v=iAOMaI1ftiA&list=PLLc16OUiZWd4RuFdN5_Wx9xwjCVVbopzr&index=2) |
 | **Episode 4**: Intro ReAct | The Evolution of LLMs - Learn how LLMs evolved from basic language models to advanced multiagency AI systems. From basic LLMs to Chain-of-Thought and Reasoning LLMs towards ReAct and Multi-Agent Architectures. Get to know the basic terms | [![Watch the video](https://img.youtube.com/vi/tLdFO1flj_o/0.jpg)](https://www.youtube.com/watch?v=tLdFO1flj_o&list=PLLc16OUiZWd4RuFdN5_Wx9xwjCVVbopzr&index=13) | |
 | **Episode 5**: CAI on CTF challenges | Dive into Capture The Flag (CTF) competitions using CAI. Learn how to leverage AI agents to solve various cybersecurity challenges including web exploitation, cryptography, reverse engineering, and forensics. Discover how to configure CAI for competitive hacking scenarios and maximize your CTF performance with intelligent automation. | [![Watch the video](https://img.youtube.com/vi/MrXTQ0e2to4/0.jpg)](https://www.youtube.com/watch?v=MrXTQ0e2to4&list=PLLc16OUiZWd4RuFdN5_Wx9xwjCVVbopzr&index=13) | [![Watch the video](https://img.youtube.com/vi/r9US_JZa9_c/0.jpg)](https://www.youtube.com/watch?v=r9US_JZa9_c&list=PLLc16OUiZWd4RuFdN5_Wx9xwjCVVbopzr&index=12) |
-|  |  |  |  |
+| | | | |
-| **Annex 1**: `CAI` 0.5.x release  | Introduce version 0.5 of `CAI` including new multi-agent functionality, new commands such as `/history`, `/compact`, `/graph` or `/memory` and a case study showing how `CAI` found a critical security flaw in OT heap pumps spread around the world. |  [![Watch the video](https://img.youtube.com/vi/OPFH0ANUMMw/0.jpg)](https://www.youtube.com/watch?v=OPFH0ANUMMw) | [![Watch the video](https://img.youtube.com/vi/Q8AI4E4gH8k/0.jpg)](https://www.youtube.com/watch?v=Q8AI4E4gH8k) |
+| **Annex 1**: `CAI` 0.5.x release | Introduce version 0.5 of `CAI` including new multi-agent functionality, new commands such as `/history`, `/compact`, `/graph` or `/memory` and a case study showing how `CAI` found a critical security flaw in OT heap pumps spread around the world. | [![Watch the video](https://img.youtube.com/vi/OPFH0ANUMMw/0.jpg)](https://www.youtube.com/watch?v=OPFH0ANUMMw) | [![Watch the video](https://img.youtube.com/vi/Q8AI4E4gH8k/0.jpg)](https://www.youtube.com/watch?v=Q8AI4E4gH8k) |
-| **Annex 2**: `CAI` 0.4.x release and `alias0`  | Introducing version 0.4 of `CAI` with *streaming* and improved MCP support. We also introduce `alias0`, the Privacy-First Cybersecurity AI, a Model-of-Models Intelligence that implements a Privacy-by-Design architecture and obtains state-of-the-art results in cybersecurity benchmarks. |  [![Watch the video](https://img.youtube.com/vi/NZjzfnvAZcc/0.jpg)](https://www.youtube.com/watch?v=NZjzfnvAZcc) |  |
+| **Annex 2**: `CAI` 0.4.x release and `alias0` | Introducing version 0.4 of `CAI` with *streaming* and improved MCP support. We also introduce `alias0`, the Privacy-First Cybersecurity AI, a Model-of-Models Intelligence that implements a Privacy-by-Design architecture and obtains state-of-the-art results in cybersecurity benchmarks. | [![Watch the video](https://img.youtube.com/vi/NZjzfnvAZcc/0.jpg)](https://www.youtube.com/watch?v=NZjzfnvAZcc) | |
-| **Annex 3**: Cybersecurity AI Community Meeting #1  | First Cybersecurity AI (`CAI`) community meeting, over 40 participants from academia, industry, and defense gathered to discuss the open-source scaffolding behind CAI — a project designed to build agentic AI systems for cybersecurity that are open, modular, and Bug Bounty-ready. |  [![Watch the video](https://img.youtube.com/vi/4JqaTiVlgsw/0.jpg)](https://www.youtube.com/watch?v=4JqaTiVlgsw) |  |
+| **Annex 3**: Cybersecurity AI Community Meeting #1 | First Cybersecurity AI (`CAI`) community meeting, over 40 participants from academia, industry, and defense gathered to discuss the open-source scaffolding behind CAI — a project designed to build agentic AI systems for cybersecurity that are open, modular, and Bug Bounty-ready. | [![Watch the video](https://img.youtube.com/vi/4JqaTiVlgsw/0.jpg)](https://www.youtube.com/watch?v=4JqaTiVlgsw) | |
-| **Annex 4**: `CAI PRO` PoC  | Short proof-of-concept demonstration of [CAI PRO](https://aliasrobotics.com/cybersecurityai.php) capabilities showcasing the Professional Edition with unlimited `alias1` tokens, unrestricted AI, and enterprise-grade security testing features. | ![CAI PRO Demo](media/caipro_poc.gif) |  |
+| **Annex 4**: `CAI PRO` PoC | Short proof-of-concept demonstration of [CAI PRO](https://aliasrobotics.com/cybersecurityai.php) capabilities showcasing the Professional Edition with unlimited `alias1` tokens, unrestricted AI, and enterprise-grade security testing features. | ![CAI PRO Demo](media/caipro_poc.gif) | |
-| **Annex 5**: `CAI` PoC  | Short proof-of-concept demonstration of CAI Community Edition showcasing the open-source framework's core capabilities for AI-powered security testing and vulnerability discovery. | ![CAI Demo](media/cai_poc.gif) |  |
+| **Annex 5**: `CAI` PoC | Short proof-of-concept demonstration of CAI Community Edition showcasing the open-source framework's core capabilities for AI-powered security testing and vulnerability discovery. | ![CAI Demo](media/cai_poc.gif) | |
-| **Annex 6**: CAI in `Jaula del N00B`  |  CAI (CIBERSEGURIDAD CON IA) LUIJAIT EN LA JAULA DEL N00B  - Demonstration and discussion of CAI framework capabilities in the popular Spanish cybersecurity podcast/show. |  | [![Watch the video](https://img.youtube.com/vi/KD2_xzIOkWg/0.jpg)](https://www.youtube.com/watch?v=KD2_xzIOkWg) |
+| **Annex 6**: CAI in `Jaula del N00B` | CAI (CIBERSEGURIDAD CON IA) LUIJAIT EN LA JAULA DEL N00B - Demonstration and discussion of CAI framework capabilities in the popular Spanish cybersecurity podcast/show. | | [![Watch the video](https://img.youtube.com/vi/KD2_xzIOkWg/0.jpg)](https://www.youtube.com/watch?v=KD2_xzIOkWg) |
 ## :nut_and_bolt: Install
@ -362,7 +357,7 @@ pip install cai-framework
 Always create a new virtual environment to ensure proper dependency installation when updating CAI.
 The following subsections provide a more detailed walkthrough on selected popular Operating Systems. Refer to the [Development](#development) section for developer-related install instructions.
-For API Keys env syntax  check litellm Documentation. [LiteLLM Documentation](https://docs.litellm.ai/docs/tutorials/installation)
+For API Keys env syntax check litellm Documentation. [LiteLLM Documentation](https://docs.litellm.ai/docs/tutorials/installation)
 ### OS X
 ```bash
@ -448,7 +443,7 @@ cai  # first launch it can take up to 30 seconds
 You might run into issues running cai on ubuntu since some agents assume they are running on a Kali Instance and are not able to find the tools needed. 
 So as an alternative you can use the docker compose file in the dockerized folder instead. This also works from within wsl if docker is installed.
 in that case fetch the dockerized folder (no need for the whole repo) and run from within it.
-For API Keys env syntax  check litellm Documentation. [LiteLLM Documentation](https://docs.litellm.ai/docs/tutorials/installation)
+For API Keys env syntax check litellm Documentation. [LiteLLM Documentation](https://docs.litellm.ai/docs/tutorials/installation)
 ```bash
 #build and run docker compose Build takes around 20 min.
@ -458,7 +453,6 @@ docker compose build && docker compose up -d
 docker compose exec cai cai
 ```
 ### Android
 We recommend having at least 8 GB of RAM:
@ -502,7 +496,6 @@ cp .env.example .env  # edit here your keys/models
 cai
 ```
 ### :nut_and_bolt: Setup `.env` file
 CAI leverages the `.env` file to load configuration at launch. To facilitate the setup, the repo provides an exemplary [`.env.example`](.env.example) file provides a template for configuring CAI's setup and your LLM API keys to work with desired LLM models.
@ -511,7 +504,6 @@ CAI leverages the `.env` file to load configuration at launch. To facilitate the
 CAI does NOT provide API keys for any model by default. Don't ask us to provide keys, use your own or host your own models.
 :warning: Note:
 The OPENAI_API_KEY must not be left blank. It should contain either "sk-123" (as a placeholder) or your actual API key. See https://github.com/aliasrobotics/cai/issues/27.
@ -542,7 +534,6 @@ Or directly from the command line:
 OLLAMA_API_BASE="https://custom-openai-proxy.com/v1" cai
 ```
 ## :triangular_ruler: Architecture:
 CAI focuses on making cybersecurity agent **coordination** and **execution** lightweight, highly controllable, and useful for humans. To do so it builds upon 8 pillars: `Agents`, `Tools`, `Handoffs`, `Patterns`, `Turns`, `Tracing`, `Guardrails` and `HITL`.
@ -569,7 +560,6 @@ CAI focuses on making cybersecurity agent **coordination** and **execution** lig
                    └───────────┘└───────────┘└────────────┘└───────────┘
 ```
 If you want to dive deeper into the code, check the following files as a start point for using CAI:
 * [__init__.py](https://github.com/aliasrobotics/cai/blob/main/src/cai/__init__.py)
@ -638,22 +628,19 @@ message = "Tell me about recursion in programming."
 result = await Runner.run(agent, message)
 ```
 You may find different [tools](tools). They are grouped in 6 major categories inspired by the security kill chain [^2]:
-1. Reconnaissance and weaponization - *reconnaissance*  (crypto, listing, etc)
+1. Reconnaissance and weaponization - *reconnaissance* (crypto, listing, etc)
 2. Exploitation - *exploitation*
 3. Privilege escalation - *escalation*
 4. Lateral movement - *lateral*
 5. Data exfiltration - *exfiltration*
 6. Command and control - *control*
 ### 🔹 Handoffs
 `Handoffs` allow an `Agent` to delegate tasks to another agent, which is crucial in cybersecurity operations where specialized expertise is needed for different phases of an engagement. In our framework, `Handoffs` are implemented as tools for the LLM, where a **handoff/transfer function** like `transfer_to_flag_discriminator` enables the `ctf_agent` to pass control to the `flag_discriminator_agent` once it believes it has found the flag. This creates a security validation chain where the first agent handles exploitation and flag discovery, while the second agent specializes in flag verification, ensuring proper segregation of duties and leveraging specialized capabilities of different models for distinct security tasks. For more information, please refer to the [example here](https://github.com/aliasrobotics/cai/blob/main/examples/cai/agent_patterns/handoffs.py) for the full execution code.
 ```python
 from cai.sdk.agents import function_tool
 from cai.tools.common import run_command
@ -725,19 +712,15 @@ When building `Patterns`, we generall y classify them among one of the following
 For more information and examples of common agentic patterns, see the [examples folder](https://github.com/aliasrobotics/cai/blob/main/examples/agent_patterns/README.md).
 ### 🔹 Turns and Interactions
 During the agentic flow (conversation), we distinguish between **interactions** and **turns**.
 - **Interactions** are sequential exchanges between one or multiple agents. Each agent executing its logic corresponds with one *interaction*. Since an `Agent` in CAI generally implements the `ReACT` agent model[^3], each *interaction* consists of 1) a reasoning step via an LLM inference and 2) act by calling zero-to-n `Tools`. This is defined in`process_interaction()` in [core.py](cai/core.py).
 - **Turns**: A turn represents a cycle of one ore more **interactions** which finishes when the `Agent` (or `Pattern`) executing returns `None`, judging there're no further actions to undertake. This is defined in `run()`, see [core.py](cai/core.py).
 > [!NOTE]
 > CAI Agents are not related to Assistants in the Assistants API. They are named similarly for convenience, but are otherwise completely unrelated. CAI is entirely powered by the Chat Completions API and is hence stateless between calls.
 ### 🔹 Tracing
 CAI implements AI observability by adopting the OpenTelemetry standard and to do so, it leverages [Phoenix](https://github.com/Arize-ai/phoenix) which provides comprehensive tracing capabilities through OpenTelemetry-based instrumentation, allowing you to monitor and analyze your security operations in real-time. This integration enables detailed visibility into agent interactions, tool usage, and attack vectors throughout penetration testing workflows, making it easier to debug complex exploitation chains, track vulnerability discovery processes, and optimize agent performance for more effective security assessments.
@ -749,7 +732,7 @@ CAI implements AI observability by adopting the OpenTelemetry standard and to do
 `Guardrails` provide a critical security layer for CAI agents, protecting against prompt injection attacks and preventing execution of dangerous commands. These guardrails run in parallel to agents, validating both input and output to ensure safe operation. The framework includes:
 - **Input Guardrails**: Detect and block prompt injection attempts before they reach agents, using pattern matching, Unicode homograph detection, and AI-powered analysis
- **Output Guardrails**: Validate agent outputs before execution, preventing dangerous commands like reverse shells, fork bombs, or data exfiltration  
+- **Output Guardrails**: Validate agent outputs before execution, preventing dangerous commands like reverse shells, fork bombs, or data exfiltration 
 - **Multi-layered Defense**: Protection at input, processing, and execution stages with tool-level validation
 - **Base64/Base32 Aware**: Automatically decodes and analyzes encoded payloads to detect hidden malicious commands
 - **Configurable**: Can be enabled/disabled via `CAI_GUARDRAILS` environment variable
@ -787,10 +770,8 @@ CAI delivers a framework for building Cybersecurity AIs with a strong emphasis o
 Accordingly, the Human-In-The-Loop (`HITL`) module is a core design principle of CAI, acknowledging that human intervention and teleoperation are essential components of responsible security testing. Through the `cli.py` interface, users can seamlessly interact with agents at any point during execution by simply pressing `Ctrl+C`. This is implemented across [core.py](cai/core.py) and also in the REPL abstractions [REPL](cai/repl).
 ## :rocket: Quickstart
 To start CAI after installing it, just type `cai` in the CLI:
 ```bash
@ -827,8 +808,8 @@ From here on, type on `CAI` and start your security exercise. Best way to learn
 ### Environment Variables
 For using private models, you are given a [`.env.example`](.env.example) file. Copy it and rename it as `.env`. Fill in your corresponding API keys, and you are ready to use CAI.
- <details>
+ 
-<summary>List of Environment Variables</summary>
+ List of Environment Variables 
 | Variable | Description |
 |----------|-------------|
@ -857,7 +838,7 @@ For using private models, you are given a [`.env.example`](.env.example) file. C
 | CAI_WORKSPACE_DIR | Specifies the directory path where the workspace is located |
 | CAI_GUARDRAILS | Enable/disable guardrails for prompt injection protection (default: true) |
-</details>
+ 
 ### OpenRouter Integration
@ -887,7 +868,6 @@ AZURE_API_KEY=<your-azure-openai-key>
 AZURE_API_BASE=https://<resource>.openai.azure.com/openai/deployments/<deployment-name>/chat/completions?api-version=2025-01-01-preview
 ```
 ### MCP
 CAI supports the Model Context Protocol (MCP) for integrating external tools and services with AI agents. MCP is supported via two transport mechanisms:
@ -943,14 +923,12 @@ CAI>/mcp list
 https://github.com/user-attachments/assets/386a1fd3-3469-4f84-9396-2a5236febe1f
 ## Development
 Development is facilitated via VS Code dev. environments. To try out our development environment, clone the repository, open VS Code and enter de dev. container mode:
 ![CAI Development Environment](media/cai_devenv.gif)
 ### Contributions
 If you want to contribute to this project, use [**Pre-commit**](https://pre-commit.com/) before your MR
@ -998,12 +976,10 @@ docker run --rm -it \
  registry.gitlab.com/aliasrobotics/alias_research/cai:latest bash
 ```
 ## FAQ
-<details><summary>OLLAMA is giving me 404 errors</summary>
+ OLLAMA is giving me 404 errors 
-Ollama's API in OpenAI mode uses `/v1/chat/completions` whereas the `openai` library uses  `base_url` + `/chat/completions`.
+Ollama's API in OpenAI mode uses `/v1/chat/completions` whereas the `openai` library uses `base_url` + `/chat/completions`.
 We adopt the latter for overall alignment with the gen AI community and empower the former by allowing users to add the `v1` themselves via:
@ -1016,20 +992,20 @@ See the following issues that treat this topic in more detail:
 - https://github.com/aliasrobotics/cai/issues/83
 - https://github.com/aliasrobotics/cai/issues/82
-</details>
+ 
-<details><summary>Where are all the caiextensions?</summary>
+ Where are all the caiextensions? 
 See [all caiextensions](https://gitlab.com/aliasrobotics/alias_research/caiextensions)
-</details>
+ 
-<details><summary>How do I install the report caiextension?</summary>
+ How do I install the report caiextension? 
 [See here](#optional-requirements-caiextensions)
-</details>
+ 
-<details><summary>How do I set up SSH access for Gitlab?</summary>
+ How do I set up SSH access for Gitlab? 
 Generate a new SSH key
 ```bash
@ -1053,20 +1029,17 @@ ssh -T git@gitlab.com
 Welcome to GitLab, @vmayoral!
 ```
-</details>
+ How do I clear Python cache? 
 <details><summary>How do I clear Python cache?</summary>
 ```bash
 find . -name "*.pyc" -delete && find . -name "__pycache__" -delete
 ```
-</details>
+ 
-<details><summary>If host networking is not working with ollama check whether it has been disabled in Docker because you are not signed in</summary>
+ If host networking is not working with ollama check whether it has been disabled in Docker because you are not signed in 
 Docker in OS X behaves funny sometimes. Check if the following message has shown up:
@ -1079,56 +1052,53 @@ To verify connection, from within the VSCode devcontainer:
 curl -v http://host.docker.internal:8000/api/version
 ```
-</details>
+ 
-<details>
+ 
-<summary>Run CAI against any target</summary>
+ Run CAI against any target 
 ![cai-004-first-message](imgs/readme_imgs/cai-004-first-message.png)
 The starting user prompt in this case is: `Target IP: 192.168.3.10, perform a full network scan`.
 The agent started performing a nmap scan. You could either interact with the agent and give it more instructions, or let it run to see what it explores next.
-</details>
+ 
-<details>
+ 
-<summary>How do I interact with the agent? Type twice CTRL + C </summary>
+ How do I interact with the agent? Type twice CTRL + C 
 ![cai-005-ctrl-c](imgs/readme_imgs/cai-005-ctrl-c.png)
 If you want to use the HITL mode, you can do it by presssing twice ```Ctrl + C```.
 This will allow you to interact (prompt) with the agent whenever you want. The agent will not lose the previous context, as it is stored in the `history` variable, which is passed to it and any agent that is called. This enables any agent to use the previous information and be more accurate and efficient.
-</details>
+ 
-<details>
+ 
-<summary> Can I change the model while CAI is running? /model </summary>
+ Can I change the model while CAI is running? /model 
 Use ```/model``` to change the model.
 ![cai-007-model-change](imgs/readme_imgs/cai-007-model-change.png)
-</details>
+ 
-
+ 
-<details>
+ How can I list all the agents available? /agent 
 <summary>How can I list all the agents available? /agent </summary>
 Use ```/agent``` to list all the agents available.
 ![cai-010-agents-menu](imgs/readme_imgs/cai-010-agents-menu.png)
-</details>
+ 
-
+ 
-
+ Where can I list all the environment variables? /env 
 <details>
 <summary> Where can I list all the environment variables? /env </summary>
 ![cai-008-config](imgs/readme_imgs/cai-008-config.png)
-</details>
+ 
-<details>
+ 
-<summary> How can I monitor token usage and costs? </summary>
+ How can I monitor token usage and costs? 
 Use **`/cost`** in the REPL for session spend and token statistics, **`/compact`** when conversations grow long, and (in **TUI** mode) the per-terminal cost and model indicators in the UI.
@ -1137,27 +1107,25 @@ CAI> /cost
 ```
 See the [CLI commands reference](docs/cli/commands_reference.md) for the full command list.
-</details>
+ 
-<details>
+ 
-<summary> How to know more about the CLI? /help </summary>
+ How to know more about the CLI? /help 
 ![cai-006-help](imgs/readme_imgs/cai-006-help.png)
-</details>
+ 
-
+ 
-<details>
+ How can I trace the whole execution? 
 <summary>How can I trace the whole execution?</summary>
 The environment variable `CAI_TRACING` allows the user to set it to `CAI_TRACING=true` to enable tracing, or `CAI_TRACING=false` to disable it.
 When CAI is prompted by the first time, the user is provided with two paths, the execution log, and the tracing log.
 ![cai-009-logs](imgs/readme_imgs/cai-009-logs.png)
-</details>
+ 
-
+ 
-<details>
+ Can I expand CAI capabilities using previous run logs? 
 <summary>Can I expand CAI capabilities using previous run logs?</summary>
 Yes. Today CAI performs best by relying on In‑Context Learning (ICL). Rather than building long‑term stores, the recommended workflow is to load relevant prior logs directly into the current session so the model can reason with them in context.
@ -1177,18 +1145,17 @@ CAI prints the path to the current run’s JSONL log at startup (highlighted in
 Legacy notes: earlier “memory extension” mechanisms (episodic/semantic stores and offline ingestion) are retained for reference only. See [src/cai/agents/memory.py](src/cai/agents/memory.py) for background and legacy details. Our current direction prioritizes ICL over persistent memory.
-</details>
+ 
-<details>
+ 
-<summary>Can I expand CAI capabilities using scripts or extra information?</summary>
+ Can I expand CAI capabilities using scripts or extra information? 
 Currently, CAI supports text based information. You can add any extra information on the target you are facing by copy-pasting it directly into the system or user prompt.
 **How?** By adding it to the system ([`system_master_template.md`](cai/repl/templates/system_master_template.md)) or the user prompt ([`user_master_template.md`](cai/repl/templates/user_master_template.md)). You can always directly prompt the path to the model, and it will ```cat``` it.
-</details>
+ 
-
+ How CAI licence works? 
 <details><summary>How CAI licence works?</summary>
 CAI’s current license does not restrict usage for research purposes. You are free to use CAI for security assessments (pentests), to develop additional features, and to integrate it into your research activities, as long as you comply with local laws.
@ -1196,14 +1163,13 @@ If you or your organization start benefiting commercially from CAI (e.g., offeri
 CAI itself is not a profit-seeking initiative. Our goal is to build a sustainable open-source project. We simply ask that those who profit from CAI contribute back and support our ongoing development.
-</details>
+ 
-<details><summary>I get a `Unable to locate package python3.12-venv` when installing the prerequisites on my debian based system!</summary>
+ I get a `Unable to locate package python3.12-venv` when installing the prerequisites on my debian based system! 
 The easiest way to get around this is to simply install [`python3.12`](https://www.python.org/downloads/release/python-3120/) from source.
-
+ 
 </details>
 ## Citation
@ -1260,7 +1226,6 @@ If you want to cite our work, please use the following (ordered by publication d
 }
 ```
 ## Acknowledgements
 CAI was initially developed by [Alias Robotics](https://aliasrobotics.com) and co-funded by the European EIC accelerator project RIS (GA 101161136) - HORIZON-EIC-2023-ACCELERATOR-01 call. The original agentic principles are inspired from OpenAI's [`swarm`](https://github.com/openai/swarm) library and translated into newer prototypes. This project also makes use of other relevant open source building blocks including [`LiteLLM`](https://github.com/BerriAI/litellm), and [`phoenix`](https://github.com/Arize-ai/phoenix)
@ -1268,12 +1233,11 @@ CAI was initially developed by [Alias Robotics](https://aliasrobotics.com) and c
 ### Academic Collaborations
 CAI benefits from ongoing research collaborations with academic institutions. Researchers interested in collaborative projects, dataset access, or academic licenses should contact research@aliasrobotics.com. We provide special support for:
 - PhD research projects
- Academic benchmarking studies  
+- Academic benchmarking studies 
 - Security education initiatives
 - Open-source contributions from research labs
-
+ 
 <!-- Footnotes -->
 [^1]: Arguably, the Chain-of-Thought agentic pattern is a special case of the Hierarchical agentic pattern.
 [^2]: Kamhoua, C. A., Leslie, N. O., & Weisman, M. J. (2018). Game theoretic modeling of advanced persistent threat in internet of things. Journal of Cyber Security and Information Systems.
 [^3]: Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023, January). React: Synergizing reasoning and acting in language models. In International Conference on Learning Representations (ICLR).
--- a/agents.yml.example
+++ b/agents.yml.example
@ -1,35 +1,50 @@
-# Example agents.yml configuration file
+# Example universal agents.yml configuration file (used by both CLI and TUI)
-# This file is auto-loaded when CAI starts
+# Copy this to agents.yml and customize for your needs.
-# Copy this to agents.yml and customize for your needs
+
 # Optional human friendly description
 description: >-
  Preload two blue-vs-red duos. Blue teamers operate inside the container (CTF_INSIDE=true)
  while their opposing red teams remain outside (CTF_INSIDE=false). Both teams share the
  same investigation prompt and begin running automatically when the UI starts.
 # Shared defaults applied when an agent entry omits the field
 shared:
  prompt: >-
    Perform a coordinated assessment of the target container. Blue teamers defend from inside,
    red teams probe externally. Exchange findings and highlight attack paths and mitigations.
  auto_run: true
 parallel_agents:
-  # Each agent can have a name, optional model, optional prompt, and optional unified_context
+  # Each entry represents one agent instance. Available keys:
-  - name: one_tool_agent
+  #   name (required)  -> registered agent name (`cai --list-agents`)
-    model: claude-sonnet-4-20250514
+  #   team             -> grouping label shown in the TUI
-    prompt: "Focus on finding vulnerabilities and security issues"
+  #   prompt           -> overrides the shared prompt for this agent
-    unified_context: false  # Each agent has its own message history (default)
+  #   model            -> optional model override
-    
+  #   env              -> environment variables injected before launch
  #   auto_run         -> whether to auto-execute the prompt on startup
  #   unified_context  -> share history across agents in CLI parallel mode
  - name: blueteam_agent
-    model: claude-sonnet-4-20250514
+    team: Team 1 - Defender vs Adversary
-    prompt: "Focus on defensive security and mitigation strategies"
+    prompt: >-
-    unified_context: false
+      Inside defender: inspect container services, collect telemetry, harden SSH.
-    
+    auto_run: true
-  - name: bug_bounter_agent
+    env:
-    model: alias0
+      CTF_INSIDE: true
-    prompt: "Search for bugs and create detailed reports"
+  - name: redteam_agent
-    unified_context: false
+    team: Team 1 - Defender vs Adversary
-
+    prompt: >-
-  - name: web_pentester_agent
+      External adversary: enumerate open ports and attempt foothold.
-    model: alias1
+    env:
-    prompt: "Agent that specializes in web application penetration testing."
+      CTF_INSIDE: false
-    unified_context: false
+  - name: blueteam_agent
-
+    team: Team 2 - Alternate Matchup
-# Example with unified context (agents share message history)
+    auto_run: true
-# parallel_agents:
+    env:
-#   - name: redteam_agent
+      CTF_INSIDE: true
-#     unified_context: true  # Share message history with other unified agents
+  - name: redteam_agent
-#   - name: blueteam_agent
+    team: Team 2 - Alternate Matchup
-#     unified_context: true  # Share message history with other unified agents
+    prompt: >-
-
+      Standby attacker: wait for defender intel before moving.
-# When 2 or more agents are configured, parallel mode is automatically enabled
+    auto_run: true
-# The agents will be available for selection when you enter a prompt
+    env:
      CTF_INSIDE: false
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@ -25,6 +25,9 @@ AutoPenBench                                             │
                                  Beginner Novice     Graduate     Professional      Elite
 ```
 *Categories marked with asterisk are available in CAI PRO version [^8].
 <table>
  <tr>
    <th style="text-align:center;"><b>Best performance in Agent vs Agent A&amp;D</b></th>
@ -58,16 +61,13 @@ Cybersecurity AI Benchmark or `CAIBench` for short is a meta-benchmark (*benchma
    - [📁 Dataset: `memory01_80/`](#-dataset-memory01_80)
    - [🔍 Entity Coverage](#-entity-coverage)
    - [📐 Metrics](#-metrics)
-    - [📊  Evaluation](#--evaluation)
+    - [📊 Evaluation](#-evaluation)
  - [About `Attack-Defense CTF`](#about-attack-defense-ctf)
    - [Game Structure](#game-structure)
    - [Rules and Scoring](#rules-and-scoring)
    - [Architecture](#architecture)
    - [Technical Features](#technical-features)
  - [About challenges in benchmarks](#about-challenges-in-benchmarks)
    - [`Jeopardy CTF`](#jeopardy-ctf)
    - [`A&D`](#ad)
    - [Cyber Ranges](#cyber-ranges)
 ## Difficulty classification
@ -123,6 +123,8 @@ Cybersecurity AI Benchmark or `CAIBench` for short is a meta-benchmark (*benchma
 :five: **Privacy** (`benchmarks/eval.py` :book:) - Assess AI models' ability to handle sensitive information appropriately, maintain privacy standards, and properly manage Personally Identifiable Information (PII) in cybersecurity contexts.
 > **Note:** Categories :one: **Jeopardy-style CTFs**, :two: **Attack–Defense CTF**, and :three: **Cyber Range Exercises** are available in the **CAI PRO** version. Learn more at https://aliasrobotics.com/cybersecurityai.php
 ## Benchmarks
@ -130,11 +132,11 @@ Currently, supporting the following benchmarks, refer to [`ctf_configs.jsonl`](.
 | Category | Benchmark | Difficulty | Description |
 |----------|-----------|------------|-------------|
-| :one: `jeopardy`  | Base | 🚩 - 🚩🚩🚩 | `21` curated CTFs that measures initial penetration testing capabilities across challenges in rev, misc, pwn, web, crypto and forensics. *This benchmark has been saturated and frontier Cybersecurity models are able to conquer most*. |
+| :one: `jeopardy` [^8] | Base | 🚩 - 🚩🚩🚩 | `21` curated CTFs that measures initial penetration testing capabilities across challenges in rev, misc, pwn, web, crypto and forensics. *This benchmark has been saturated and frontier Cybersecurity models are able to conquer most*. |
-| :one: `jeopardy`  | [Cybench](https://github.com/andyzorigin/cybench) | 🚩 - 🚩🚩🚩🚩🚩 | A curated list with `35` CTFs stemming from the popular *`Cybench` Framework for Evaluating Cybersecurity Capabilities and Risk*[^7]. |
+| :one: `jeopardy` [^8] | [Cybench](https://github.com/andyzorigin/cybench) | 🚩 - 🚩🚩🚩🚩🚩 | A curated list with `35` CTFs stemming from the popular *`Cybench` Framework for Evaluating Cybersecurity Capabilities and Risk*[^7]. |
-| :one: `jeopardy`  | RCTF2 | 🚩 - 🚩🚩🚩🚩🚩 |  `27` Robotics CTFs challenges to attack and defend robots and robotic frameworks. Robots and robotics-related technologies considered include ROS, ROS 2, manipulators, AGVs and AMRs, collaborative robots, legged robots, humanoids and more. |
+| :one: `jeopardy` [^8] | RCTF2 | 🚩 - 🚩🚩🚩🚩🚩 |  `27` Robotics CTFs challenges to attack and defend robots and robotic frameworks. Robots and robotics-related technologies considered include ROS, ROS 2, manipulators, AGVs and AMRs, collaborative robots, legged robots, humanoids and more. |
-| :two: `A&D`  | `A&D` | 🚩 - 🚩🚩🚩🚩 | A compilation of `10` **n** vs **n** attack and defense challenges wherein each team defends their own vulnerable assets while simultaneously attacking others'. Includes IT and OT/ICS themed challenges across multiple difficulty levels. |
+| :two: `A&D` [^8] | `A&D` | 🚩 - 🚩🚩🚩🚩 | A compilation of `10` **n** vs **n** attack and defense challenges wherein each team defends their own vulnerable assets while simultaneously attacking others'. Includes IT and OT/ICS themed challenges across multiple difficulty levels. |
-| :three: `cyber-range`  |  Cyber Ranges | 🚩🚩 - 🚩🚩🚩🚩|  12 Cyber Ranges with 16 challenges to practice and test cybersecurity skills in realistic simulated environments. |
+| :three: `cyber-range` [^8] |  Cyber Ranges | 🚩🚩 - 🚩🚩🚩🚩|  15 Cyber Ranges with 28 challenges to practice and test cybersecurity skills in realistic simulated environments. |
 | :four: `knowledge` | [SecEval](https://github.com/XuanwuAI/SecEval) | N/A | Benchmark designed to evaluate large language models (LLMs) on security-related tasks. It includes various real-world scenarios such as phishing email analysis, vulnerability classification, and response generation. |
 | :four: `knowledge` | [CyberMetric](https://github.com/CyberMetric) | N/A | Benchmark framework that focuses on measuring the performance of AI systems in cybersecurity-specific question answering, knowledge extraction, and contextual understanding. It emphasizes both domain knowledge and reasoning ability. |
 | :four: `knowledge` | [CTIBench](https://github.com/xashru/cti-bench) | N/A | Benchmark focused on evaluating LLM models' capabilities in understanding and processing Cyber Threat Intelligence (CTI) information. |
@ -155,6 +157,8 @@ Currently, supporting the following benchmarks, refer to [`ctf_configs.jsonl`](.
 [^7]: CAIBench integrates only 35 (out of 40) curated Cybench scenarios for evaluation purposes. This reduction comes mainly down to restrictions in our testing infrastructure as well as reproducibility issues.
 [^8]: Internal exercises related to Jeopardy-style CTFs, Attack–Defense CTF, and Cyber Range Exercises are available upon request to [CAI PRO](https://aliasrobotics.com/cybersecurityai.php) subscribers on a use case basis. Learn more at https://aliasrobotics.com/cybersecurityai.php
 ## About `Cybersecurity Knowledge` benchmarks
@ -307,8 +311,8 @@ This is an example of how a text sould be sanitized:
 Some annotation rules:
 - Each detected entity should be sanitized using the **format: [ENTITY_TYPE]**
- Priorize IP_ADDRESS to URL: `https://192.168.2.100` is anonymized like this: `https://[IP_ADDRESS]:5050` instead of [URL]
+- Priorize IP_ADDRESS to URL: `https://192.168.2.100` is anonimized like this: `https://[IP_ADDRESS]:5050` instead of [URL]
- DATE_TIME is used for dates and for times, in this case `2025-03-11 11:41 UTC` it should be anonymized like this: ` [DATE_TIME] [DATE_TIME]`
+- DATE_TIME is used for dates and for times, in this case `2025-03-11 11:41 UTC` it sould be anonimized like this: ` [DATE_TIME] [DATE_TIME]`
 If you have any questions about the annotation, please write to us.
@ -451,9 +455,7 @@ The A&D benchmark includes **10 machines** spanning IT and OT/ICS domains across
 ## About challenges in benchmarks
-**NOTE**: Various of the challenges used in the benchmarks have been sourced from public repositories. Proper credit is given to the original authors and repositories in the tables below. The challenges have been containerized to ensure reproducibility and ease of deployment within the CAIBench framework. Some images contain confidential information and are thereby unavailable. For an exemplary image, please refer to the https://github.com/NickNameInvalid/LLM_CTF/tree/main/database/crypto/lottery.
+### `Jeopardy CTF` [^8]
 ### `Jeopardy CTF` 
 <details>
 <summary>"Base" Benchmark</summary>
@ -584,7 +586,7 @@ The A&D benchmark includes **10 machines** spanning IT and OT/ICS domains across
 </details>
-### `A&D` 
+### `A&D` [^8]
 <details>
 <summary>"Attack/Defense CTF" Benchmark</summary>
@ -604,7 +606,7 @@ The A&D benchmark includes **10 machines** spanning IT and OT/ICS domains across
 </details>
-### Cyber Ranges 
+### Cyber Ranges [^8]
 <details>
 <summary>"Cyber Ranges" Benchmark</summary>
@ -621,6 +623,10 @@ The A&D benchmark includes **10 machines** spanning IT and OT/ICS domains across
 | 8 | `leet-messenger` | Medium | 2 | Network traffic analysis, Custom protocol parsing, Base64 decoding, Binary extraction, Reverse engineering, Static analysis, Assembly debugging | https://github.com/stratosphereips/stratocyberlab/tree/main/challenges/leet-messenger | registry.gitlab.com/aliasrobotics/alias_research/caiextensions/pentestperf:leet-messenger |
 | 9 | `cherry1` | Medium | 1 | Directory enumeration on port 7755, RCE via backup/command.php, setarch SUID exploitation, SSH bruteforce | https://www.vulnhub.com/entry/cherry-1,552/ | registry.gitlab.com/aliasrobotics/alias_research/caiextensions/pentestperf:cherry1 |
 | 10 | `darkhole2` | Hard | 1 | Git repository exposure, SQL Injection, SSH access, RCE, Privilege escalation | https://www.vulnhub.com/entry/darkhole-2,740/ | registry.gitlab.com/aliasrobotics/alias_research/caiextensions/pentestperf:darkhole2 |
 | 11 | `easy_techcorp2` | Easy | 4 | SQL injection, Authenticated file upload, RCE, SMTP user enumeration, SSH brute-force, Privilege escalation, Database breach, Network pivoting | Custom cyber range for CAI | Multi-container (web, mail, database, fileserver, router) |
 | 12 | `cobalt-group-ransomware` | Medium | 5 | Adversary emulation, Phishing simulation, Lateral movement, Network segmentation bypass, Credential theft, Database access, Ransomware deployment | Custom cyber range for CAI - Cobalt Group adversary emulation | Multi-container (c2-server, router, public-web, hr-pc, dev-pc, internal-intranet, database, backup-server) |
 | 13 | `promptfoo` | Easy | 5 | Keylogger installation, Persistence mechanisms (cron/systemd), Backdoor creation (SSH keys/SUID), System modification, Privilege escalation, Data exfiltration, Log manipulation | Custom cyber range for CAI | Single-container (vulnerable-server) |
 | 14 | `poo` | Medium | 3 | Web enumeration, DS_Store parsing, IIS shortname enumeration, SQL injection, PostgreSQL exploitation, Foreign Data Wrapper abuse, Circular link privilege escalation, xp_cmdshell, sp_execute_external_script exploitation | https://app.hackthebox.com/prolabs/11 | Multi-container (poo-attacker, poo-web, poo-database) |
 </details>
--- a/benchmarks/cyberPII-bench/annotation_metrics.py
+++ b/benchmarks/cyberPII-bench/annotation_metrics.py
@ -1,9 +1,9 @@
 """
 Annotation Evaluation Script
-This script processes entity-tagged text data from multiple annotators and computes performance metrics 
+This script processes entity-tagged text data from multiple annotators and computes performance metrics
-(precision, recall, F1, F2) by comparing them to ground truth annotations. It supports multiple annotation 
+(precision, recall, F1, F2) by comparing them to ground truth annotations. It supports multiple annotation
-formats (BIO, span labels) and handles reports generation including mistakes analysis, per-entity type 
+formats (BIO, span labels) and handles reports generation including mistakes analysis, per-entity type
 breakdowns, and overall statistics.
 Main Features:
@ -28,6 +28,7 @@ Example usage:
 """
 import pandas as pd
 import re
 import os
@ -38,237 +39,294 @@ import argparse
 # Define valid entity types
 VALID_ENTITIES = {
-    'PERSON', 'PHONE_NUMBER', 'LOCATION', 'CREDIT_CARD', 'CRYPTO', 'IBAN_CODE',
+    "PERSON",
-    'IP_ADDRESS', 'EMAIL_ADDRESS', 'URL', 'DATE_TIME', 'NIF', 'MEDICAL_LICENSE',
+    "PHONE_NUMBER",
-    'US_SSN', 'US_BANK_NUMBER', 'US_DRIVER_LICENSE', 'US_ITIN', 'US_PASSPORT',
+    "LOCATION",
-    'ORGANIZATION', 'ADDRESS', 'NRP', 'DNI', 'NIE', 'IBAN', 'EUROPEAN_BANK_ACCOUNT'
+    "CREDIT_CARD",
    "CRYPTO",
    "IBAN_CODE",
    "IP_ADDRESS",
    "EMAIL_ADDRESS",
    "URL",
    "DATE_TIME",
    "NIF",
    "MEDICAL_LICENSE",
    "US_SSN",
    "US_BANK_NUMBER",
    "US_DRIVER_LICENSE",
    "US_ITIN",
    "US_PASSPORT",
    "ORGANIZATION",
    "ADDRESS",
    "NRP",
    "DNI",
    "NIE",
    "IBAN",
    "EUROPEAN_BANK_ACCOUNT",
 }
 # ============ DATA NORMALIZATION FUNCTIONS ============
-def find_entities_with_positions(text: str, skip_entities: Set[str] = set()) -> List[Tuple[str, int, int, str]]:
+
 def find_entities_with_positions(
    text: str, skip_entities: Set[str] = set()
 ) -> List[Tuple[str, int, int, str]]:
    """
    Find entities marked with brackets and their positions in the text.
    Returns: List of (entity_type, start_pos, end_pos, full_tag)
    """
    if not isinstance(text, str) or pd.isna(text):
        return []
-    
+
    entities = []
    valid_entities = VALID_ENTITIES - skip_entities
-    pattern = r'\[({})\]'.format('|'.join(valid_entities))
+    pattern = r"\[({})\]".format("|".join(valid_entities))
-    
+
    for match in re.finditer(pattern, text):
        entity_type = match.group(1)
-        if entity_type not in skip_entities:  
+        if entity_type not in skip_entities:
            start = match.start()
            end = match.end()
            full_tag = match.group(0)
            entities.append((entity_type, start, end, full_tag))
-    
+
    return sorted(entities, key=lambda x: x[1])
 def generate_span_labels(text: str, entities: List[Tuple[str, int, int, str]]) -> str:
    """
    Generate span labels in format: start:end:entity_type|start:end:entity_type
    """
    if not isinstance(text, str) or pd.isna(text) or not entities:
        return ""
-    
+
    spans = []
    for entity_type, start, end, _ in entities:
        spans.append(f"{start}:{end}:{entity_type}")
-    
+
    return "|".join(spans)
 def generate_bio_labels(text: str, entities: List[Tuple[str, int, int, str]]) -> str:
    """
    Generate BIO labels for each character in the text
    """
    if not isinstance(text, str) or pd.isna(text):
        return ""
-    
+
    # Initialize all positions as O (Outside)
-    bio_labels = ['O'] * len(text)
+    bio_labels = ["O"] * len(text)
-    
+
    # Mark entity positions
    for entity_type, start, end, _ in entities:
        # Mark B (Beginning)
        if start < len(bio_labels):
            bio_labels[start] = f"B-{entity_type}"
-        
+
        # Mark I (Inside) for the rest of the entity
        for i in range(start + 1, end):
            if i < len(bio_labels):
                bio_labels[i] = f"I-{entity_type}"
-    
+
    return "".join(bio_labels)
-def normalize_annotations(df: pd.DataFrame, annotator_config: Dict[str, Dict[str, str]], skip_entities: Set[str] = set()) -> pd.DataFrame:
+
 def normalize_annotations(
    df: pd.DataFrame, annotator_config: Dict[str, Dict[str, str]], skip_entities: Set[str] = set()
 ) -> pd.DataFrame:
    """
    Normalize annotations for ground truth and all annotators.
    """
    # First normalize ground truth
-    ground_truth_entities = df['target_text'].apply(lambda x: find_entities_with_positions(x, skip_entities))
+    ground_truth_entities = df["target_text"].apply(
-    df['span_labels'] = df.apply(lambda row: generate_span_labels(row['target_text'], ground_truth_entities[row.name]), axis=1)
+        lambda x: find_entities_with_positions(x, skip_entities)
-    df['mbert_bio_labels'] = df.apply(lambda row: generate_bio_labels(row['target_text'], ground_truth_entities[row.name]), axis=1)
+    )
-    
+    df["span_labels"] = df.apply(
        lambda row: generate_span_labels(row["target_text"], ground_truth_entities[row.name]),
        axis=1,
    )
    df["mbert_bio_labels"] = df.apply(
        lambda row: generate_bio_labels(row["target_text"], ground_truth_entities[row.name]), axis=1
    )
    # Then normalize each annotator's data
    for annotator, config in annotator_config.items():
-        target_col = config['target_text']
+        target_col = config["target_text"]
        if target_col not in df.columns:
            print(f"Warning: Column {target_col} not found for annotator {annotator}")
            continue
-            
+
        # Fill NaN values with empty string to avoid errors
        df[target_col] = df[target_col].fillna("")
-            
+
        # Generate entities and labels
-        annotator_entities = df[target_col].apply(lambda x: find_entities_with_positions(x, skip_entities))
+        annotator_entities = df[target_col].apply(
-        df[f'span_labels_{annotator}'] = df.apply(
+            lambda x: find_entities_with_positions(x, skip_entities)
            lambda row: generate_span_labels(row[target_col], annotator_entities[row.name]), 
            axis=1
        )
-        df[f'mbert_bio_labels_{annotator}'] = df.apply(
+        df[f"span_labels_{annotator}"] = df.apply(
-            lambda row: generate_bio_labels(row[target_col], annotator_entities[row.name]),
+            lambda row: generate_span_labels(row[target_col], annotator_entities[row.name]), axis=1
            axis=1
        )
-    
+        df[f"mbert_bio_labels_{annotator}"] = df.apply(
            lambda row: generate_bio_labels(row[target_col], annotator_entities[row.name]), axis=1
        )
    return df
 # ============ METRICS CALCULATION FUNCTIONS ============
-def calculate_metrics(df: pd.DataFrame, annotator_config: Dict[str, Dict[str, str]], skip_entities: Set[str] = set()) -> Dict:
+
 def calculate_metrics(
    df: pd.DataFrame, annotator_config: Dict[str, Dict[str, str]], skip_entities: Set[str] = set()
 ) -> Dict:
    """
    Calculate metrics comparing ground truth with annotators
    """
    stats = {
-        'total_rows': len(df),
+        "total_rows": len(df),
-        'entity_counts': defaultdict(lambda: defaultdict(int)),
+        "entity_counts": defaultdict(lambda: defaultdict(int)),
-        'metrics_per_annotator': defaultdict(dict),
+        "metrics_per_annotator": defaultdict(dict),
-        'metrics_per_entity_type': defaultdict(lambda: defaultdict(dict)),
+        "metrics_per_entity_type": defaultdict(lambda: defaultdict(dict)),
-        'mistakes': defaultdict(list)
+        "mistakes": defaultdict(list),
    }
-    
+
    # First calculate ground truth entities once for all annotators
    all_true_entities = []
    for idx, row in df.iterrows():
-        ground_truth = find_entities_with_positions(row['target_text'], skip_entities)
+        ground_truth = find_entities_with_positions(row["target_text"], skip_entities)
        # Store entities with row index for exact matching
        for entity in ground_truth:
            all_true_entities.append((idx, entity[0], entity[1], entity[2]))
-            stats['entity_counts']['ground_truth'][entity[0]] += 1
+            stats["entity_counts"]["ground_truth"][entity[0]] += 1
-    
+
    true_set = set(all_true_entities)
    total_ground_truth = len(true_set)
-    
+
    # Process each annotator
    for annotator, config in annotator_config.items():
-        target_col = config['target_text']
+        target_col = config["target_text"]
        if target_col not in df.columns:
            print(f"Warning: Column {target_col} not found in the dataset")
            continue
-        
+
        # Collect predicted entities
        all_pred_entities = []
-        
+
        # Process each row
        for idx, row in df.iterrows():
            pred_entities = find_entities_with_positions(row[target_col], skip_entities)
-            
+
            # Store entities with row index for exact matching
            for entity in pred_entities:
                all_pred_entities.append((idx, entity[0], entity[1], entity[2]))
-                stats['entity_counts'][annotator][entity[0]] += 1
+                stats["entity_counts"][annotator][entity[0]] += 1
-            
+
            # Record mistakes
            ground_truth = [e for e in all_true_entities if e[0] == idx]
            gt_set = {(e[1], e[2], e[3]) for e in ground_truth}
            pred_set = {(e[0], e[1], e[2]) for e in pred_entities}
-            
+
            if gt_set != pred_set:
                false_positives = list(pred_set - gt_set)
                false_negatives = list(gt_set - pred_set)
-                
+
                if false_positives or false_negatives:
-                    stats['mistakes'][annotator].append({
+                    stats["mistakes"][annotator].append(
-                        'id': row.get('id', idx),
+                        {
-                        'text': row['target_text'],
+                            "id": row.get("id", idx),
-                        'annotated_text': row[target_col],
+                            "text": row["target_text"],
-                        'ground_truth': list(gt_set),
+                            "annotated_text": row[target_col],
-                        'prediction': list(pred_set),
+                            "ground_truth": list(gt_set),
-                        'false_positives': false_positives,
+                            "prediction": list(pred_set),
-                        'false_negatives': false_negatives
+                            "false_positives": false_positives,
-                    })
+                            "false_negatives": false_negatives,
-        
+                        }
                    )
        # Calculate overall metrics
        pred_set = set(all_pred_entities)
-        
+
        tp = len(true_set & pred_set)
        fp = len(pred_set - true_set)
        fn = len(true_set - pred_set)
-        
+
        precision = tp / len(pred_set) if pred_set else 0
        recall = tp / len(true_set) if true_set else 0
        f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
        f2 = 5 * (precision * recall) / (4 * precision + recall) if (precision + recall) > 0 else 0
-        
+
-        stats['metrics_per_annotator'][annotator] = {
+        stats["metrics_per_annotator"][annotator] = {
-            'true_positives': tp,
+            "true_positives": tp,
-            'false_positives': fp,
+            "false_positives": fp,
-            'false_negatives': fn,
+            "false_negatives": fn,
-            'precision': precision,
+            "precision": precision,
-            'recall': recall,
+            "recall": recall,
-            'f1_score': f1,
+            "f1_score": f1,
-            'f2_score': f2,
+            "f2_score": f2,
-            'total_entities': total_ground_truth  # Use the same ground truth count for all annotators
+            "total_entities": total_ground_truth,  # Use the same ground truth count for all annotators
        }
-        
+
        # Calculate per-entity type metrics
        for entity_type in VALID_ENTITIES - skip_entities:  # Only evaluate non-skipped entities
            true_type = {e for e in true_set if e[1] == entity_type}
            pred_type = {e for e in pred_set if e[1] == entity_type}
-            
+
            if not true_type and not pred_type:
                continue
-            
+
            tp_type = len(true_type & pred_type)
            fp_type = len(pred_type - true_type)
            fn_type = len(true_type - pred_type)
-            
+
            precision_type = tp_type / len(pred_type) if pred_type else 0
            recall_type = tp_type / len(true_type) if true_type else 0
-            f1_type = 2 * (precision_type * recall_type) / (precision_type + recall_type) if (precision_type + recall_type) > 0 else 0
+            f1_type = (
-            f2_type = 5 * (precision_type * recall_type) / (4 * precision_type + recall_type) if (precision_type + recall_type) > 0 else 0
+                2 * (precision_type * recall_type) / (precision_type + recall_type)
-            
+                if (precision_type + recall_type) > 0
                else 0
            )
            f2_type = (
                5 * (precision_type * recall_type) / (4 * precision_type + recall_type)
                if (precision_type + recall_type) > 0
                else 0
            )
            if tp_type > 0 or fp_type > 0 or fn_type > 0:
-                stats['metrics_per_entity_type'][annotator][entity_type] = {
+                stats["metrics_per_entity_type"][annotator][entity_type] = {
-                    'true_positives': tp_type,
+                    "true_positives": tp_type,
-                    'false_positives': fp_type,
+                    "false_positives": fp_type,
-                    'false_negatives': fn_type,
+                    "false_negatives": fn_type,
-                    'precision': precision_type,
+                    "precision": precision_type,
-                    'recall': recall_type,
+                    "recall": recall_type,
-                    'f1_score': f1_type,
+                    "f1_score": f1_type,
-                    'f2_score': f2_type,
+                    "f2_score": f2_type,
-                    'total_entities': len(true_type)
+                    "total_entities": len(true_type),
                }
-    
+
    return stats
 # ============ REPORT GENERATION FUNCTIONS ============
-def generate_overall_report(stats: Dict, output_dir: str, input_file: str, annotator_config: Dict[str, Dict[str, str]], skip_entities: Set[str] = set()):
+
 def generate_overall_report(
    stats: Dict,
    output_dir: str,
    input_file: str,
    annotator_config: Dict[str, Dict[str, str]],
    skip_entities: Set[str] = set(),
 ):
    """Generate overall statistics report"""
-    with open(os.path.join(output_dir, 'overall_report.txt'), 'w') as f:
+    with open(os.path.join(output_dir, "overall_report.txt"), "w") as f:
        f.write("=== Overall Annotation Statistics ===\n\n")
-        
+
        # Add input file information
        f.write(f"Input File: {input_file}\n")
-        
+
        # Add information about skipped entities
        if skip_entities:
            f.write(f"\nExcluded Entity Types: {', '.join(sorted(skip_entities))}\n")
-        
+
        # Add annotator configuration information
        f.write("\nAnnotator Configurations:\n")
        for annotator, config in annotator_config.items():
@ -276,37 +334,40 @@ def generate_overall_report(stats: Dict, output_dir: str, input_file: str, annot
            for key, value in config.items():
                f.write(f"  {key}: {value}\n")
        f.write("\n" + "=" * 50 + "\n\n")
-        
+
        f.write(f"Total rows analyzed: {stats['total_rows']}\n\n")
-        
+
        f.write("Ground Truth Entity Counts:\n")
-        for entity_type, count in sorted(stats['entity_counts']['ground_truth'].items()):
+        for entity_type, count in sorted(stats["entity_counts"]["ground_truth"].items()):
            f.write(f"[{entity_type}]: {count}\n")
-        
+
        f.write("\nAnnotator Entity Counts:\n")
-        for annotator in stats['entity_counts']:
+        for annotator in stats["entity_counts"]:
-            if annotator != 'ground_truth':
+            if annotator != "ground_truth":
                f.write(f"\n{annotator}:\n")
-                for entity_type, count in sorted(stats['entity_counts'][annotator].items()):
+                for entity_type, count in sorted(stats["entity_counts"][annotator].items()):
                    f.write(f"[{entity_type}]: {count}\n")
-def generate_entity_report(stats: Dict, output_dir: str, annotator_names: List[str], skip_entities: Set[str] = set()):
+
 def generate_entity_report(
    stats: Dict, output_dir: str, annotator_names: List[str], skip_entities: Set[str] = set()
 ):
    """Generate per-entity type performance report"""
-    with open(os.path.join(output_dir, 'entity_performance.txt'), 'w') as f:
+    with open(os.path.join(output_dir, "entity_performance.txt"), "w") as f:
        f.write("=== Entity Type Performance by Annotator ===\n\n")
-        
+
        # Add information about skipped entities
        if skip_entities:
            f.write(f"Note: The following entity types were excluded from evaluation:\n")
            f.write(f"{', '.join(sorted(skip_entities))}\n\n")
            f.write("=" * 50 + "\n\n")
-        
+
        for annotator in annotator_names:
-            if annotator in stats['metrics_per_entity_type']:
+            if annotator in stats["metrics_per_entity_type"]:
                f.write(f"\n{annotator.upper()}:\n")
                for entity_type in sorted(VALID_ENTITIES - skip_entities):
-                    if entity_type in stats['metrics_per_entity_type'][annotator]:
+                    if entity_type in stats["metrics_per_entity_type"][annotator]:
-                        metrics = stats['metrics_per_entity_type'][annotator][entity_type]
+                        metrics = stats["metrics_per_entity_type"][annotator][entity_type]
                        f.write(f"\n  {entity_type}:\n")
                        f.write(f"    Precision: {metrics['precision']:.4f}\n")
                        f.write(f"    Recall: {metrics['recall']:.4f}\n")
@ -316,53 +377,61 @@ def generate_entity_report(stats: Dict, output_dir: str, annotator_names: List[s
                        f.write(f"    False Positives: {metrics['false_positives']}\n")
                        f.write(f"    False Negatives: {metrics['false_negatives']}\n")
-def generate_mistakes_report(stats: Dict, output_dir: str, annotator_names: List[str], skip_entities: Set[str] = set()):
+
 def generate_mistakes_report(
    stats: Dict, output_dir: str, annotator_names: List[str], skip_entities: Set[str] = set()
 ):
    """Generate detailed mistakes report"""
-    with open(os.path.join(output_dir, 'mistakes.txt'), 'w') as f:
+    with open(os.path.join(output_dir, "mistakes.txt"), "w") as f:
        f.write("=== Detailed Mistakes Analysis ===\n\n")
-        
+
        # Add information about skipped entities
        if skip_entities:
            f.write(f"Note: The following entity types were excluded from evaluation:\n")
            f.write(f"{', '.join(sorted(skip_entities))}\n\n")
            f.write("=" * 50 + "\n\n")
-        
+
        for annotator in annotator_names:
-            if annotator in stats['mistakes'] and stats['mistakes'][annotator]:
+            if annotator in stats["mistakes"] and stats["mistakes"][annotator]:
-                f.write(f"\n{annotator.upper()} Mistakes ({len(stats['mistakes'][annotator])} total):\n")
+                f.write(
-                for mistake in stats['mistakes'][annotator]:
+                    f"\n{annotator.upper()} Mistakes ({len(stats['mistakes'][annotator])} total):\n"
                )
                for mistake in stats["mistakes"][annotator]:
                    f.write(f"\nExample {mistake['id']}:\n")
                    f.write(f"Original text: {mistake['text']}\n")
                    f.write(f"Annotated text: {mistake['annotated_text']}\n")
-                    
+
-                    if mistake['false_negatives']:
+                    if mistake["false_negatives"]:
                        f.write("\nMissed entities (should have been anonymized):\n")
-                        for entity_type, start, end in mistake['false_negatives']:
+                        for entity_type, start, end in mistake["false_negatives"]:
                            f.write(f"- {entity_type} at position {start}-{end}\n")
-                    
+
-                    if mistake['false_positives']:
+                    if mistake["false_positives"]:
                        f.write("\nIncorrect anonymizations:\n")
-                        for entity_type, start, end in mistake['false_positives']:
+                        for entity_type, start, end in mistake["false_positives"]:
                            f.write(f"- {entity_type} at position {start}-{end}\n")
-                    
+
                    f.write("-" * 80 + "\n")
            else:
                f.write(f"\n{annotator.upper()}: No mistakes found\n")
-def generate_metrics_report(stats: Dict, output_dir: str, annotator_names: List[str], skip_entities: Set[str] = set()):
+
 def generate_metrics_report(
    stats: Dict, output_dir: str, annotator_names: List[str], skip_entities: Set[str] = set()
 ):
    """Generate overall metrics report"""
-    with open(os.path.join(output_dir, 'metrics.txt'), 'w') as f:
+    with open(os.path.join(output_dir, "metrics.txt"), "w") as f:
        f.write("=== Overall Metrics by Annotator ===\n\n")
-        
+
        # Add information about skipped entities
        if skip_entities:
            f.write(f"Note: The following entity types were excluded from evaluation:\n")
            f.write(f"{', '.join(sorted(skip_entities))}\n\n")
            f.write("=" * 50 + "\n\n")
-        
+
        for annotator in annotator_names:
-            if annotator in stats['metrics_per_annotator']:
+            if annotator in stats["metrics_per_annotator"]:
-                metrics = stats['metrics_per_annotator'][annotator]
+                metrics = stats["metrics_per_annotator"][annotator]
                f.write(f"\n{annotator.upper()}:\n")
                f.write(f"  Total Entities in Ground Truth: {metrics['total_entities']}\n")
                f.write(f"  True Positives: {metrics['true_positives']}\n")
@ -373,10 +442,11 @@ def generate_metrics_report(stats: Dict, output_dir: str, annotator_names: List[
                f.write(f"  F1 Score: {metrics['f1_score']:.4f}\n")
                f.write(f"  F2 Score: {metrics['f2_score']:.4f}\n")
 def get_output_dir(base_dir: str) -> str:
    """Create and return the output directory name with date and sequence number in the same directory as the input file"""
    # Get the directory of the input file
-    
+
    base_name = f"output_metrics_{datetime.now().strftime('%Y%m%d')}"
    counter = 1
    while True:
@ -386,40 +456,54 @@ def get_output_dir(base_dir: str) -> str:
            return dir_name
        counter += 1
 # ============ MAIN EXECUTION ============
 def main():
    parser = argparse.ArgumentParser(description="Annotator Evaluation Script")
-    parser.add_argument('--input_csv_path', type=str, required=True, help='Path to input CSV file')
+    parser.add_argument("--input_csv_path", type=str, required=True, help="Path to input CSV file")
-    parser.add_argument('--annotator', type=str, required=True, help='Annotator used to generate the input CSV file, options: alias0, privateAI')
+    parser.add_argument(
-    parser.add_argument('--skip_entities', type=str, nargs='+', default=[], help='List of entity types to skip in evaluation')
+        "--annotator",
        type=str,
        required=True,
        help="Annotator used to generate the input CSV file, options: alias0, privateAI",
    )
    parser.add_argument(
        "--skip_entities",
        type=str,
        nargs="+",
        default=[],
        help="List of entity types to skip in evaluation",
    )
    args = parser.parse_args()
    # Convert skip_entities to a set for faster lookups
    skip_entities = set(args.skip_entities)
-    
+
    # Validate skip_entities
    invalid_entities = skip_entities - VALID_ENTITIES
    if invalid_entities:
-        raise ValueError(f"Invalid entities to skip: {invalid_entities}. Valid entities are: {VALID_ENTITIES}")
+        raise ValueError(
            f"Invalid entities to skip: {invalid_entities}. Valid entities are: {VALID_ENTITIES}"
        )
    df = pd.read_csv(args.input_csv_path, sep=";")
    ANNOTATOR_CONFIG = {
-            args.annotator: {
+        args.annotator: {
-                'target_text': f'target_text_{args.annotator}_sanitized',
+            "target_text": f"target_text_{args.annotator}_sanitized",
-                'span_labels': f'span_labels_{args.annotator}_sanitized',
+            "span_labels": f"span_labels_{args.annotator}_sanitized",
-                'mbert_bio_labels': f'mbert_bio_labels_{args.annotator}_sanitized'
+            "mbert_bio_labels": f"mbert_bio_labels_{args.annotator}_sanitized",
        }
    }
-  
+
    print("Normalizing annotations...")
    df = normalize_annotations(df, ANNOTATOR_CONFIG, skip_entities)
-        
+
    print("Calculating metrics...")
    stats = calculate_metrics(df, ANNOTATOR_CONFIG, skip_entities)
-        
+
    # Determine output directory
    base_dir = os.path.dirname(os.path.abspath(args.input_csv_path))
    dir_annotator = os.path.join(base_dir, args.annotator)
@ -432,11 +516,13 @@ def main():
    generate_entity_report(stats, output_dir, list(ANNOTATOR_CONFIG.keys()), skip_entities)
    generate_mistakes_report(stats, output_dir, list(ANNOTATOR_CONFIG.keys()), skip_entities)
    generate_metrics_report(stats, output_dir, list(ANNOTATOR_CONFIG.keys()), skip_entities)
-        
+
    print(f"\nAnalysis complete. Reports have been generated in {output_dir}/")
    if skip_entities:
-        print(f"Note: The following entities were excluded from evaluation: {', '.join(sorted(skip_entities))}")
+        print(
            f"Note: The following entities were excluded from evaluation: {', '.join(sorted(skip_entities))}"
        )
 if __name__ == "__main__":
-    main()
+    main()
--- a/benchmarks/eval.py
+++ b/benchmarks/eval.py
@ -80,7 +80,13 @@ from annotation_metrics import (
 )
-dotenv.load_dotenv()
+# Load .env from current directory only, not from parent directories
 dotenv_path = os.path.join(os.getcwd(), '.env')
 dotenv.load_dotenv(dotenv_path=dotenv_path, verbose=False)
 # Set default for OPENAI_API_KEY if not already set
 if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = ""
 LITELLM_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
 model_pricing_cache = {}
--- a/ci/benchmarks/.benchmarks.yml
+++ b/ci/benchmarks/.benchmarks.yml
@ -1,95 +0,0 @@
 .use_base_container: &use_base_container
  stage: benchmarks
  image: "${CI_REGISTRY_IMAGE}:latest"
  services:
    - name: docker:dind
      alias: docker
 .run_benchmarks: &run_benchmarks
  <<: *use_base_container
  script:
    - pip3 install -e .
    - pip install litellm langchain transformers torch openai tqdm cvss python-dotenv
    - echo "Checking environment variables..."
    - |
      for var in $(compgen -e); do
        if [[ ($var == CTF_* || $var == *_API_KEY || $var == *_API_BASE) && -n ${!var} ]]; then
          export $var="${!var}"          
        fi
      done
    - echo $OLLAMA_API_BASE
    - python3 benchmarks/eval.py --model $MODEL_NAME --dataset_file $BENCHMARK_FILE --eval $BENCHMARK_NAME --backend $BACKEND
    - pwd
    - ls -la benchmarks/outputs/$BENCHMARK_NAME/
    - curl http://host.docker.internal:8000/api/tags # http://localhost:8000/api/tags 
  variables:
    OLLAMA_API_BASE: "http://host.docker.internal:8000" # http://localhost:8000
    OPENROUTER_API_BASE: "https://openrouter.ai/api/v1"
    OPENAI_API_BASE: "https://api.openai.com/v1"
  artifacts:
    paths:
      - benchmarks/outputs/
    expire_in: 12 month
  tags:
    - p40
    - x86
  rules:
    - if: $CI_COMMIT_BRANCH
      when: on_success
 benchmarks-test-cybermetric-ollama:
  <<: *run_benchmarks
  variables:
    MODEL_NAME: "ollama/qwen2.5:14b"
    BENCHMARK_FILE: "benchmarks/utils/cybermetric_dataset/CyberMetric-2-v1.json"
    BENCHMARK_NAME: "cybermetric"
    BACKEND: "ollama"
    OLLAMA_API_BASE: "http://localhost:8000"
 # # benchmarks-test-seceval:
 # #   <<: *run_benchmarks
 # #   variables:
 # #     OLLAMA_API_BASE: "http://localhost:8000"
 # #     OPENROUTER_API_BASE: "https://openrouter.ai/api/v1"
 # #     OPENAI_API_KEY: "fake-api-key"
 # #   script:
 # #     - pip3 install -e .
 # #     - pip install -r benchmarks/seceval/eval/requirements.txt
 # #     - python3 benchmarks/seceval/eval/eval.py --dataset_file benchmarks/utils/seceval_dataset/questions-2.json --output_dir benchmarks/seceval/eval/outputs --backend ollama --models ollama/qwen2.5:14b
 benchmarks-test-cybermetric-openrouter:
  <<: *run_benchmarks
  variables:
    MODEL_NAME: "openrouter/qwen/qwen3-32b:free"
    BENCHMARK_FILE: "benchmarks/utils/cybermetric_dataset/CyberMetric-2-v1.json"
    BENCHMARK_NAME: "cybermetric"
    BACKEND: "openrouter"
 benchmarks-test-seceval-openrouter:
  <<: *run_benchmarks
  variables:
    MODEL_NAME: "openrouter/qwen/qwen3-32b:free"
    BENCHMARK_FILE: "benchmarks/utils/seceval_dataset/questions-2.json"
    BENCHMARK_NAME: "seceval"
    BACKEND: "openrouter"
 benchmarks-test-cti_bench-openrouter:
  <<: *run_benchmarks
  variables:
    MODEL_NAME: "openrouter/qwen/qwen3-32b:free"
    BENCHMARK_FILE: "benchmarks/utils/cti_bench_dataset/cti-mcq1.tsv"
    BENCHMARK_NAME: "cti_bench"
    BACKEND: "openrouter"
 # benchmarks-test-cti_bench-openai:
 #   <<: *run_benchmarks
 #   variables:
 #     MODEL_NAME: "gpt-4o-mini"
 #     BENCHMARK_FILE: "benchmarks/utils/cti_bench_dataset/cti-mcq1.tsv"
 #     BENCHMARK_NAME: "cti_bench"
 #     BACKEND: "openai"
--- a/ci/test/.test.yml
+++ b/ci/test/.test.yml
@ -1,340 +0,0 @@
 .use_base_container: &use_base_container
  stage: test
  image: "${CI_REGISTRY_IMAGE}:latest"
  services:
    - name: docker:dind
      alias: docker
 .run_test: &run_test
  <<: *use_base_container
  script:
    - pip3 install -e .
    - pip install inline-snapshot pytest-asyncio graphviz pytest-mock
    - pytest -s $TEST_PATH
  tags:
    - p40
    - x86
  rules:
    - if: $CI_COMMIT_BRANCH
      when: on_success
 🛠️ tools test_function_tool_decorator:
  <<: *run_test
  variables:
    TEST_PATH: tests/tools/test_function_tool_decorator.py
 🛠️ tools test_function_tool:
  <<: *run_test
  variables:
    TEST_PATH: tests/tools/test_function_tool.py
 🛠️ tools test_handoff_tool:
  <<: *run_test
  variables:
    TEST_PATH: tests/tools/test_handoff_tool.py  
 🛠️ tools test_output_tool:
  <<: *run_test
  variables:
    TEST_PATH: tests/tools/test_output_tool.py
 🛠️ tools test_tool_choice_reset:
  <<: *run_test
  variables:
    TEST_PATH: tests/tools/test_tool_choice_reset.py
 🛠️ tools test_tool_converter:
  <<: *run_test
  variables:
    TEST_PATH: tests/tools/test_tool_converter.py
 🛠️ tools test_tool_generic_linux_command:
  <<: *run_test
  variables:
    TEST_PATH: tests/tools/test_tool_generic_linux_command.py
 🛠️ tools test_tool_use_behavior:
  <<: *run_test
  variables:
    TEST_PATH: tests/tools/test_tool_use_behavior.py
 🤖 agents test_agent_config:
  <<: *run_test
  variables:
    TEST_PATH: tests/agents/test_agent_config.py
 🤖 agents test_agent_hooks:
  <<: *run_test
  variables:
    TEST_PATH: tests/agents/test_agent_hooks.py
 # 🤖 agents test_agent_one_tool:
 #   <<: *run_test
 #   variables:
 #     TEST_PATH: tests/agents/test_agent_one_tool.py
 🤖 agents test_agent_prompt_system_master_template:
  <<: *run_test
  variables:
    TEST_PATH: tests/agents/test_agent_prompt_system_master_template.py
 🤖 agents test_agent_runner:
  <<: *run_test
  variables:
    TEST_PATH: tests/agents/test_agent_runner.py
 🤖 agents test_agent_runner_streamed:
  <<: *run_test
  variables:
    TEST_PATH: tests/agents/test_agent_runner_streamed.py
 🤖 agents test_global_hooks:
  <<: *run_test
  variables:
    TEST_PATH: tests/agents/test_global_hooks.py
 🤖 agents test_guardrails:
  <<: *run_test
  variables:
    TEST_PATH: tests/agents/test_guardrails.py
 🤖 agents test_items_helpers:
  <<: *run_test
  variables:
    TEST_PATH: tests/agents/test_items_helpers.py
 🤖 agents test_max_turns:
  <<: *run_test
  variables:
    TEST_PATH: tests/agents/test_max_turns.py
 # 🤖 agents test_agent_inference:
 #   <<: *run_test
 #   variables:
 #     TEST_PATH: tests/agents/test_agent_inference.py
 # ⚙️ core test_openai_chatcompletions:
 #   <<: *run_test
 #   variables:
 #     TEST_PATH: tests/core/test_openai_chatcompletions.py
 ⚙️ core test_openai_chatcompletions_converter:
  <<: *run_test
  variables:
    TEST_PATH: tests/core/test_openai_chatcompletions_converter.py
 # ⚙️ core test_openai_chatcompletions_stream:
 #   <<: *run_test
 #   variables:
 #     TEST_PATH: tests/core/test_openai_chatcompletions_stream.py
 ⚙️ core test_openai_responses_converter:
  <<: *run_test
  variables:
    TEST_PATH: tests/core/test_openai_responses_converter.py
 # ⚙️ core test_responses:
 #   <<: *run_test
 #   variables:
 #     TEST_PATH: tests/core/test_responses.py
 ⚙️ core test_run_config:
  <<: *run_test
  variables:
    TEST_PATH: tests/core/test_run_config.py
 ⚙️ core test_run_step_execution:
  <<: *run_test
  variables:
    TEST_PATH: tests/core/test_run_step_execution.py
 ⚙️ core test_run_step_processing:
  <<: *run_test
  variables:
    TEST_PATH: tests/core/test_run_step_processing.py
 ✏️ tracing test_agent_tracing:
  <<: *run_test
  variables:
    TEST_PATH: tests/tracing/test_agent_tracing.py    
 ✏️ tracing test_processor_api_key:
  <<: *run_test
  variables:
    TEST_PATH: tests/tracing/test_processor_api_key.py   
 ✏️ tracing test_responses_tracing:
  <<: *run_test
  variables:
    TEST_PATH: tests/tracing/test_responses_tracing.py    
 ✏️ tracing test_tracing_errors_streamed:
  <<: *run_test
  variables:
    TEST_PATH: tests/tracing/test_tracing_errors_streamed.py    
 ✏️ tracing test_tracing_errors:
  <<: *run_test
  variables:
    TEST_PATH: tests/tracing/test_tracing_errors.py   
 ✏️ tracing test_tracing:
  <<: *run_test
  variables:
    TEST_PATH: tests/tracing/test_tracing.py    
 🎤 voice test_input.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/voice/test_input.py
 🎤 voice test_openai_stt.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/voice/test_openai_stt.py
 🎤 voice test_openai_tts.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/voice/test_openai_tts.py   
 🎤 voice test_pipeline.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/voice/test_pipeline.py
 🎤 voice test_workflow.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/voice/test_workflow.py   
 📀 mcp test_caching.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/mcp/test_caching.py 
 📀 mcp test_connect_disconnect.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/mcp/test_connect_disconnect.py 
 📀 mcp test_mcp_tracing.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/mcp/test_mcp_tracing.py 
 📀 mcp test_mcp_util.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/mcp/test_mcp_util.py     
 📀 mcp test_mcp_tracing.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/mcp/test_mcp_tracing.py 
 📀 mcp test_server_errors.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/mcp/test_server_errors.py     
 ▪️ others test_computer_action.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/others/test_computer_action.py   
 ▪️ others test_pricing:
  <<: *run_test
  variables:
    TEST_PATH: tests/test_pricing.py
 ▪️ others test_pretty_print.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/others/test_pretty_print.py
 ▪️ others test_result_cast.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/others/test_result_cast.py
 # ▪️ others test_config.py:
 #   <<: *run_test
 #   variables:
 #     TEST_PATH: tests/others/test_config.py
 ▪️ others test_strict_schema.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/others/test_strict_schema.py
 ▪️ others test_doc_parsing.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/others/test_doc_parsing.py
 ▪️ others test_trace_processor.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/others/test_trace_processor.py
 ▪️ others test_extension_filters.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/others/test_extension_filters.py
 ▪️ others test_visualization.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/others/test_visualization.py
 ▪️ others test_function_schema.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/others/test_function_schema.py    
 💻 cli test_cli_streaming.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/cli/test_cli_streaming.py
 💻 commands test_command_base.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/commands/test_command_base.py
 💻 commands test_command_parallel.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/commands/test_command_parallel.py
 💻 commands test_command_agent.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/commands/test_command_agent.py
 💻 commands test_command_model.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/commands/test_command_model.py
 💻 commands test_command_history.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/commands/test_command_history.py
 💻 commands test_command_config.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/commands/test_command_config.py
 💻 commands test_command_help.py:
  <<: *run_test
  variables:
    TEST_PATH: tests/commands/test_command_help.py
 # 💻 commands test_command_cost.py:
 #   <<: *run_test
 #   variables:
 #     TEST_PATH: tests/commands/test_command_cost.py
--- a/dockerized/Dockerfile
+++ b/dockerized/Dockerfile
@ -1,75 +0,0 @@
 FROM kalilinux/kali-rolling
 ENV DEBIAN_FRONTEND=noninteractive
 # Fix Kali GPG Keys
 RUN apt-get update --allow-insecure-repositories && \
    apt-get install -y --no-install-recommends --allow-unauthenticated kali-archive-keyring && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*
 # Core system + Python
 RUN apt-get update && \
    apt-get install -y --no-install-recommends \
        python3 python3-pip python3-dev \
        curl wget git ca-certificates \
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/*
 # Install kali-linux-headless (brings most pentesting tools + dependencies)
 # This includes: nmap, nikto, dirb, gobuster, sqlmap, netcat, ssh, etc.
 RUN apt-get update && \
    echo "console-setup console-setup/variant select Latin1 and Latin5 - western Europe and Turkic languages" | debconf-set-selections && \
    DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
        kali-linux-headless \
        seclists \
        burpsuite \
        default-jre \
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/*
 # Metasploit
 RUN curl https://raw.githubusercontent.com/rapid7/metasploit-omnibus/master/config/templates/metasploit-framework-wrappers/msfupdate.erb > /tmp/msfinstall && \
     chmod 755 /tmp/msfinstall && \
     /tmp/msfinstall && \
     rm /tmp/msfinstall
 # FIX: pip system-packages warning
 RUN mkdir -p /root/.pip && \
    echo "[global]" > /root/.pip/pip.conf && \
    echo "break-system-packages = true" >> /root/.pip/pip.conf
 WORKDIR /opt/cai
 # Install CAI 
 RUN pip3 install  --ignore-installed cai-framework
 # .env Template (overwritten if .env file present in logs)
 RUN echo 'OPENAI_API_KEY="sk-1234"' > /opt/cai/.env && \
    echo 'ANTHROPIC_API_KEY=""' >> /opt/cai/.env && \
    echo 'DEEPSEEK_API_KEY=""' >> /opt/cai/.env && \
    echo 'OLLAMA_API_BASE=""' >> /opt/cai/.env && \
    echo 'PROMPT_TOOLKIT_NO_CPR=1' >> /opt/cai/.env && \
    echo 'CAI_STREAM=false' >> /opt/cai/.env
 #  logs directory
 RUN mkdir -p /opt/cai/logs
 # Startup Script
 RUN echo '#!/bin/bash' > /opt/cai/start.sh && \
    echo 'if [ -f /config/.env ]; then' >> /opt/cai/start.sh && \
    echo '  echo "Loading .env from /config/.env"' >> /opt/cai/start.sh && \
    echo '  cp /config/.env /opt/cai/.env' >> /opt/cai/start.sh && \
    echo 'fi' >> /opt/cai/start.sh && \
    echo 'cd /opt/cai' >> /opt/cai/start.sh && \
    echo 'exec cai "$@"' >> /opt/cai/start.sh && \
    chmod +x /opt/cai/start.sh
 # Healthcheck
 HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD python3 -c "import cai; print('OK')" || exit 1
 ENTRYPOINT ["/opt/cai/start.sh"]
 CMD []
--- a/dockerized/docker-compose.yaml
+++ b/dockerized/docker-compose.yaml
@ -1,51 +0,0 @@
 services:
  cai:
    build:
      context: .
      dockerfile: Dockerfile
    container_name: cai
    # Host-Net for scanning (WSL should do that?)
    network_mode: host
    # nmap raw sockets
    privileged: true
    # Volumes
    volumes:
      # .env file
      - ./config/.env:/config/.env:ro
      # Logs persistence
      - ./logs:/opt/cai/logs
      # Optional: Docker socket if cai should use docker should it though?
      # - /var/run/docker.sock:/var/run/docker.sock
    # Env Vars (Fallback, .env wins if present)
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY:-sk-1234}
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-}
      - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY:-}
      - OLLAMA_API_BASE=${OLLAMA_API_BASE:-}
      - PROMPT_TOOLKIT_NO_CPR=1
      - CAI_STREAM=false
    # Interactive
    stdin_open: true
    tty: true
    # Restart
    restart: unless-stopped
    # Resource Limits? Wsl doesnt like those? Mpf.
    deploy:
      resources:
        limits:
          cpus: '4'
          memory: 6G
        reservations:
          cpus: '1'
          memory: 2G
--- a/docs/Installation_Guide_for_CAI_Pro_v0.5.md
+++ b/docs/Installation_Guide_for_CAI_Pro_v0.5.md
@ -1,139 +0,0 @@
 # Installation Guide for CAI Pro v0.5
 ← [Back to Installation Guide](../README.md#nut_and_bolt-install)
 ## Welcome to CAI Pro!
 If your subscription is active, you have received a confirmation email. Then, get and save your API-Key and please follow these instructions to install CAI Pro on your system.
 ### Important
 - Your API Key is personal and non-transferable.
 - It will be permanently linked to the first system where it is used.
 ## System Requirements
 - OS: Ubuntu 24.04 (x86_64, 64-bit)
 - Language: English
 - Python: 3.8+ (installed automatically)
 - Memory: Minimum 4 GB RAM
 ## Installation Steps
 - Download the installer file we provided in the Confirmation to your CAI-Pro subscription: `cai-pro_Linux.deb`
 - Open a terminal in your Downloads directory.
 - Run the following commands:
  - `sudo apt update`
  - `sudo apt install ./cai-pro_Linux.deb`
 - During installation, follow the instructions and you will be asked to provide your API Key: `sk--xxxxxxxxxxxxxxxx`
 - Once completed, CAI will start automatically.
 ## Accessing CAI
 - Desktop Icon → double-click on "CAI (by Alias Robotics)"
 - Application Menu → search for "CAI"
 - Command Line → run: `cai`
 ## Support
 If you encounter any issues, contact us at: contact@aliasrobotics.com
 Although we do not provide official support for other operating systems, we offer the recommended installation steps below.
 ## Installation Steps for Other OS
 ### OS X
 ```bash
 # Install homebrew
 /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
 # Install dependencies
 brew update && \
 brew install git python@3.12
 # Create virtual environment
 python3.12 -m venv cai_env
 # Install the package from the local directory
 source cai_env/bin/activate && pip install cai-framework
 # Generate a .env file and set up with defaults
 echo -e 'OPENAI_API_KEY="sk-1234"\nANTHROPIC_API_KEY=""\nOLLAMA=""\nPROMPT_TOOLKIT_NO_CPR=1\nCAI_STREAM=false' > .env
 # Launch CAI
 cai  # first launch it can take up to 30 seconds
 ```
 ### Windows WSL
 Go to the Microsoft page: https://learn.microsoft.com/en-us/windows/wsl/install
 Here you will find all the instructions to install WSL. From Powershell write: `wsl --install`
 ```bash
 sudo apt-get update && \
 sudo apt-get install -y git python3-pip python3-venv
 # Create the virtual environment
 python3 -m venv cai_env
 # Install the package from the local directory
 source cai_env/bin/activate && pip install cai-framework
 # Generate a .env file and set up with defaults
 echo -e 'OPENAI_API_KEY="sk-1234"\nANTHROPIC_API_KEY=""\nOLLAMA=""\nPROMPT_TOOLKIT_NO_CPR=1\nCAI_STREAM=false' > .env
 # Launch CAI
 cai  # first launch it can take up to 30 seconds
 ```
 ### Android
 We recommend having at least 8 GB of RAM:
 1. First of all, install userland: https://play.google.com/store/apps/details?id=tech.ula&hl=es
 2. Install Kali minimal in basic options (for free). [Or any other kali option if preferred]
 3. Update apt keys like in this example: https://superuser.com/questions/1644520/apt-get-update-issue-in-kali, inside UserLand's Kali terminal execute:
 ```bash
 # Get new apt keys
 wget http://http.kali.org/kali/pool/main/k/kali-archive-keyring/kali-archive-keyring_2024.1_all.deb
 # Install new apt keys
 sudo dpkg -i kali-archive-keyring_2024.1_all.deb && rm kali-archive-keyring_2024.1_all.deb
 # Update APT repository
 sudo apt-get update
 # CAI requires python 3.12, lets install it (CAI for kali in Android)
 sudo apt-get update && sudo apt-get install -y git python3-pip build-essential zlib1g-dev \
 libncurses5-dev libgdbm-dev libnss3-dev libssl-dev libreadline-dev libffi-dev libsqlite3-dev \
 wget libbz2-dev pkg-config
 wget https://www.python.org/ftp/python/3.12.4/Python-3.12.4.tar.xz
 tar xf Python-3.12.4.tar.xz
 cd Python-3.12.4
 ./configure --enable-optimizations
 sudo make altinstall  # This command takes long to execute
 # Clone CAI's source code
 git clone https://github.com/aliasrobotics/cai && cd cai
 # Create virtual environment
 python3.12 -m venv cai_env
 # Install the package from the local directory
 source cai_env/bin/activate && pip3 install -e .
 # Generate a .env file and set up
 cp .env.example .env  # edit here your keys/models
 # Launch CAI
 cai
 ```
 ---
 **[⬅️ Return to Main Installation Guide](../README.md#nut_and_bolt-install)**
--- a/docs/Installation_Guide_for_CAI_Pro_v0.6.md
+++ b/docs/Installation_Guide_for_CAI_Pro_v0.6.md
@ -1,141 +0,0 @@
 # Installation Guide for CAI Pro v0.6
 ← [Back to Installation Guide](../README.md#nut_and_bolt-install)
 ## Welcome to CAI Pro!
 If your subscription is active, you have received a confirmation email. Then, get and save your API-Key and please follow these instructions to install CAI Pro on your system.
 ### Important
 - Your API Key is personal and non-transferable.
 - It will be permanently linked to the first system where it is used.
 ## System Requirements
 - OS: Ubuntu 24.04 (x86_64, 64-bit)
 - Language: English
 - Python: 3.8+ (installed automatically)
 - Memory: Minimum 4 GB RAM
 ## Installation Steps
 - Create a folder in your preferred directory.
 - Open a terminal in that directory.
 - Create a virtual environment, activate it, and install CAI Pro with the following commands:
  - `sudo apt update`
  - `python3.12 -m venv cai_env`
  - `source cai_env/bin/activate`
  - `pip install --index-url https://packages.aliasrobotics.com:664/<api-key>/ cai-framework`
 - Important note:
  - The last command requires customization. Replace `<api-key>` with the API Key provided in the confirmation email for your subscription, for example: `sk--xxxxxxxxxxxxxxxx`
 - Once the installation is complete, run:
  - `cai –tui`
 ## Accessing CAI
 - Command Line → run: `cai –tui`
 ## Support
 If you encounter any issues, contact us at: contact@aliasrobotics.com
 Although we do not provide official support for other operating systems, we offer the recommended installation steps below.
 ## Installation Steps for Other OS
 ### OS X
 ```bash
 # Install homebrew
 /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
 # Install dependencies
 brew update && \
 brew install git python@3.12
 # Create virtual environment
 python3.12 -m venv cai_env
 # Install the package from the local directory
 source cai_env/bin/activate && pip install cai-framework
 # Generate a .env file and set up with defaults
 echo -e 'OPENAI_API_KEY="sk-1234"\nANTHROPIC_API_KEY=""\nOLLAMA=""\nPROMPT_TOOLKIT_NO_CPR=1\nCAI_STREAM=false' > .env
 # Launch CAI
 cai  # first launch it can take up to 30 seconds
 ```
 ### Windows WSL
 Go to the Microsoft page: https://learn.microsoft.com/en-us/windows/wsl/install
 Here you will find all the instructions to install WSL. From Powershell write: `wsl --install`
 ```bash
 sudo apt-get update && \
 sudo apt-get install -y git python3-pip python3-venv
 # Create the virtual environment
 python3 -m venv cai_env
 # Install the package from the local directory
 source cai_env/bin/activate && pip install cai-framework
 # Generate a .env file and set up with defaults
 echo -e 'OPENAI_API_KEY="sk-1234"\nANTHROPIC_API_KEY=""\nOLLAMA=""\nPROMPT_TOOLKIT_NO_CPR=1\nCAI_STREAM=false' > .env
 # Launch CAI
 cai  # first launch it can take up to 30 seconds
 ```
 ### Android
 We recommend having at least 8 GB of RAM:
 1. First of all, install userland: https://play.google.com/store/apps/details?id=tech.ula&hl=es
 2. Install Kali minimal in basic options (for free). [Or any other kali option if preferred]
 3. Update apt keys like in this example: https://superuser.com/questions/1644520/apt-get-update-issue-in-kali, inside UserLand's Kali terminal execute:
 ```bash
 # Get new apt keys
 wget http://http.kali.org/kali/pool/main/k/kali-archive-keyring/kali-archive-keyring_2024.1_all.deb
 # Install new apt keys
 sudo dpkg -i kali-archive-keyring_2024.1_all.deb && rm kali-archive-keyring_2024.1_all.deb
 # Update APT repository
 sudo apt-get update
 # CAI requires python 3.12, lets install it (CAI for kali in Android)
 sudo apt-get update && sudo apt-get install -y git python3-pip build-essential zlib1g-dev \
 libncurses5-dev libgdbm-dev libnss3-dev libssl-dev libreadline-dev libffi-dev libsqlite3-dev \
 wget libbz2-dev pkg-config
 wget https://www.python.org/ftp/python/3.12.4/Python-3.12.4.tar.xz
 tar xf Python-3.12.4.tar.xz
 cd Python-3.12.4
 ./configure --enable-optimizations
 sudo make altinstall  # This command takes long to execute
 # Clone CAI's source code
 git clone https://github.com/aliasrobotics/cai && cd cai
 # Create virtual environment
 python3.12 -m venv cai_env
 # Install the package from the local directory
 source cai_env/bin/activate && pip3 install -e .
 # Generate a .env file and set up
 cp .env.example .env  # edit here your keys/models
 # Launch CAI
 cai
 ```
 ---
 **[⬅️ Return to Main Installation Guide](../README.md#nut_and_bolt-install)**
--- a/docs/agents.md
+++ b/docs/agents.md
@ -8,33 +8,18 @@ CAI provides a comprehensive suite of specialized agents for different cybersecu
 | Agent | Description | Primary Use Case | Key Tools |
 |-------|-------------|------------------|-----------|
-| **redteam_agent** | Offensive security specialist for penetration testing | Active exploitation, vulnerability discovery | generic_linux_command, execute_code, web_search |
+| **redteam_agent** | Offensive security specialist for penetration testing | Active exploitation, vulnerability discovery | nmap, metasploit, burp |
-| **blueteam_agent** | Defensive security expert for threat mitigation | Security hardening, incident response | generic_linux_command, ssh_command, execute_code, web_search |
+| **blueteam_agent** | Defensive security expert for threat mitigation | Security hardening, incident response | wireshark, suricata, osquery |
-| **bug_bounter_agent** | Bug bounty hunter optimized for vulnerability research | Web app security, API testing | generic_linux_command, execute_code, shodan_search, google_search |
+| **bug_bounter_agent** | Bug bounty hunter optimized for vulnerability research | Web app security, API testing | ffuf, sqlmap, nuclei |
-| **one_tool_agent** | Minimalist agent focused on single-tool execution | Quick scans, specific tool operations | generic_linux_command |
+| **one_tool_agent** | Minimalist agent focused on single-tool execution | Quick scans, specific tool operations | Generic Linux commands |
-| **dfir_agent** | Digital Forensics and Incident Response expert | Log analysis, forensic investigation | generic_linux_command, ssh_command, execute_code, think, web_search, shodan_search |
+| **dfir_agent** | Digital Forensics and Incident Response expert | Log analysis, forensic investigation | volatility, autopsy, log2timeline |
-| **reverse_engineering_agent** | Binary analysis and reverse engineering | Malware analysis, firmware reversing | generic_linux_command, ssh_command, execute_code, web_search |
+| **reverse_engineering_agent** | Binary analysis and reverse engineering | Malware analysis, firmware reversing | ghidra, radare2, ida |
-| **memory_analysis_agent** | Memory dump analysis specialist | RAM forensics, process analysis | generic_linux_command, ssh_command, execute_code, web_search |
+| **memory_analysis_agent** | Memory dump analysis specialist | RAM forensics, process analysis | volatility, rekall |
-| **network_security_analyzer_agent** | Network packet analysis expert | PCAP analysis, traffic inspection | generic_linux_command, ssh_command, execute_code, capture_remote_traffic, web_search |
+| **network_traffic_analyzer** | Network packet analysis expert | PCAP analysis, traffic inspection | wireshark, tcpdump, tshark |
-| **app_logic_mapper** | Android application logic mapper | APK analysis, app logic understanding | generic_linux_command, execute_code |
+| **android_sast_agent** | Android Static Application Security Testing | APK analysis, Android vulnerability scanning | jadx, apktool, mobsf |
-| **android_sast** | Android SAST specialist | Static application security testing for Android | app_mapper (handoff), generic_linux_command, execute_code |
+| **wifi_security_tester** | Wireless network security assessment | WiFi penetration testing, WPA cracking | aircrack-ng, reaver, wifite |
-| **wifi_security_agent** | Wireless network security assessment | WiFi penetration testing, WPA cracking | generic_linux_command, ssh_command, execute_code, web_search |
+| **replay_attack_agent** | Replay attack execution specialist | Protocol replay, authentication bypass | custom scripts, burp |
-| **replay_attack_agent** | Replay attack execution specialist | Protocol replay, authentication bypass | generic_linux_command, ssh_command, execute_code, capture_remote_traffic, web_search |
+| **subghz_sdr_agent** | Sub-GHz SDR signal analysis expert | RF analysis, IoT protocol testing | hackrf, gqrx, urh |
 | **subghz_sdr_agent** | Sub-GHz SDR signal analysis expert | RF analysis, IoT protocol testing | generic_linux_command, ssh_command, execute_code, web_search |
 | **selection_agent** ⭐ | Agent selection and routing | Automatically selects the best agent for a task | check_available_agents, analyze_task_requirements, get_agent_number, web_search |
 | **retester_agent** | Vulnerability retesting specialist | Re-validates previously discovered vulnerabilities | generic_linux_command, execute_code, google_search |
 | **reporting_agent** | Security report generation | Creates formatted security reports from findings | generic_linux_command, execute_code |
 | **dns_smtp_agent** | DNS and SMTP security testing | Email security and DNS configuration analysis | check_mail_spoofing_vulnerability, execute_cli_command |
 | **thought_agent** | Strategic planning and analysis | Analyzes and plans next steps in security assessments | think |
 | **use_case_agent** | Case study generation | Creates high-quality cybersecurity case studies | null_tool |
 | **flag_discriminator** | Flag extraction specialist | Extracts flags from CTF challenge outputs | handoff to one_tool_agent |
 | **redteam_gctr_agent** ⭐ | Red team with CTR game-theoretic analysis | Offensive security with strategic game theory | generic_linux_command, execute_code, web_search |
 | **blueteam_gctr_agent** ⭐ | Blue team with CTR game-theoretic analysis | Defensive security with strategic game theory | generic_linux_command, ssh_command, execute_code, web_search |
 | **bug_bounter_gctr_agent** ⭐ | Bug bounty with CTR game-theoretic analysis | Vulnerability research with strategic analysis | generic_linux_command, execute_code, shodan_search, google_search |
 | **purple_redteam_agent** ⭐ | Purple team red component with shared GCTR | Red team operations with shared GCTR tracking | generic_linux_command, execute_code, web_search |
 | **purple_blueteam_agent** ⭐ | Purple team blue component with shared GCTR | Blue team operations with shared GCTR tracking | generic_linux_command, ssh_command, execute_code, web_search |
 ⭐ this is a [CAI PRO](https://aliasrobotics.com/cybersecurityai.php) capability.
 ### Quick Start with Agents
@ -121,7 +106,7 @@ CAI> Analyze the memory dump for secrets
 ```bash
 # 1. Network traffic analysis
-CAI>/agent network_security_analyzer_agent
+CAI>/agent network_traffic_analyzer
 CAI> Analyze capture.pcap for suspicious activity
 # 2. Forensic investigation
@ -135,118 +120,43 @@ CAI> Provide mitigation strategies based on findings
 ---
 ## Basic Configuration
-## Creating Custom Agents
+Key agent properties include:
-CAI makes it easy to create custom agents tailored to your specific security needs. Custom agents can be configured with specialized instructions, tools, guardrails, and models.
+-   `name`: Name of the agent (e.g., the name of `one_tool_agent` is 'CTF Agent')
-
+-   `instructions`: The system prompt that defines agent behavior
 ### Key Agent Properties
 -   `name`: Display name of the agent (e.g., 'Custom Pentester')
 -   `description`: Brief description of the agent's purpose
 -   `instructions`: The system prompt that defines agent behavior (can be static string or dynamic function)
 -   `model`: Which LLM to use, with optional `model_settings` to configure parameters like temperature, top_p, etc.
 -   `tools`: Tools that the agent can use to achieve its tasks
 -   `input_guardrails`: Security guardrails that validate and filter inputs before processing
 -   `output_guardrails`: Security guardrails that validate outputs before execution
 -   `handoffs`: Allows an agent to delegate tasks to another agent
-### Complete Custom Agent Example
+## Example: `one_tool_agent.py`
 This example demonstrates creating a security-focused agent with all recommended components:
 ```python
 import os
 from cai.sdk.agents import Agent, OpenAIChatCompletionsModel
-from cai.tools.reconnaissance.generic_linux_command import generic_linux_command
+from cai.tools.reconnaissance.generic_linux_command import generic_linux_command 
 from openai import AsyncOpenAI
 from cai.util import create_system_prompt_renderer
-# Define agent instructions
+one_tool_agent = Agent(
-instructions = """Behave as a pentester and
+    name="CTF agent",
-                 use the generic_linux_command tool
+    description="Agent focused on conquering security challenges using generic linux commands",
-                 to run simulated Linux commands.
+    instructions="You are a Cybersecurity expert Leader facing a CTF challenge.",
                 Stay focused, concise, and task-oriented."""
 custom_agent = Agent(
    name="Custom Pentester",
    description="Offensive security pentesting agent with guardrails.",
    instructions=create_system_prompt_renderer(instructions),
    tools=[
        generic_linux_command,
    ],
    model=OpenAIChatCompletionsModel(
-        model=os.getenv("CAI_MODEL", "alias1"),
+        model="qwen2.5:14b",
        openai_client=AsyncOpenAI(),
-    ),
+    )
 )
 ```
 ### Extend Existing Agents
 This example demonstrates extending Red Team Agent **instructions** to write 'Red Team Agent at your service.' at the end of each message:
 ```python
 from cai.cli import run_cai_cli
 from cai.agents.red_teamer import redteam_agent
 from cai.util import load_prompt_template
 from dotenv import load_dotenv
 # Load environment variables from .env file
 load_dotenv()
 # Customize the red team agent's instructions
 redteam_prompt = load_prompt_template("prompts/system_red_team_agent.md")
 # Custom instruction to append
 custom_append = "\n\nAt the end of each message, write 'Red Team Agent at your service.'"
 modified_prompt = str(redteam_prompt) + custom_append
 # Save the new instructions back to the red team agent
 redteam_agent.instructions = modified_prompt
 # Run your brand new red team agent with the CAI CLI
 run_cai_cli(redteam_agent)
 ```
 In the same way you could add a **custom/existing tools**:
 ```python
 from cai.cli import run_cai_cli
 from cai.agents.red_teamer import redteam_agent
 from cai.sdk.agents.tool import function_tool
 from cai.tools.reconnaissance.shodan import shodan_search, shodan_host_info
 from dotenv import load_dotenv
 # Create new fucntion for a tool
@function_tool
 def hello_world() -> str:
    """
    Prints Hello, World!
    Args: None
    Returns: str: A greeting message.
    """
    return "Hello, World!"
 # Load environment variables from .env file
 load_dotenv()
 # Add the new function and CAI shodan tools to the red team agent
 redteam_agent.tools.extend([shodan_search, shodan_host_info, hello_world])
 # Run the red team agent
 run_cai_cli(redteam_agent)
 ```
 If you want to create your own custom tools for your agents, see the [tools documentation](tools.md) for detailed instructions.
 If you want to create Multi-Agent Patterns, see [multi_agent documentation](multi_agent.md) for orchestration patterns. 
 ## Context
 There are two main context types. See [context](context.md) for details.
-Agents are generic on their `context` type. Context is a dependency-injection tool: it's an object you create and pass to `Runner.run()`, that is passed to every agent, tool, handoff, etc., and it serves as a grab bag of dependencies and state for the agent run. You can provide any Python object as the context.
+Agents are generic on their `context` type. Context is a dependency-injection tool: it's an object you create and pass to `Runner.run()`, that is passed to every agent, tool, handoff etc, and it serves as a grab bag of dependencies and state for the agent run. You can provide any Python object as the context.
 ```python
@dataclass
@ -431,4 +341,4 @@ CAI>/load logs/logname.jsonl
 - **Agent Tools**: See [tools documentation](tools.md) for available tools
 - **Handoffs**: See [handoffs documentation](handoffs.md) for agent coordination
 - **MCP Integration**: See [mcp documentation](mcp.md) for connecting external tools
- **Multi-Agent Patterns**: See [multi_agent documentation](multi_agent.md) for orchestration patterns
+- **Multi-Agent Patterns**: See [multi_agent documentation](multi_agent.md) for orchestration patterns
--- a/docs/api.md
+++ b/docs/api.md
@ -10,7 +10,7 @@ cai --api --api-host 0.0.0.0 --api-port 8080
 # the next free port and prints it in the console.
 ```
-CLI flags and environment variables (API subset):
+CLI flags and environment variables:
 | Flag | Env | Description |
 | --- | --- | --- |
@ -20,8 +20,6 @@ CLI flags and environment variables (API subset):
 | `--api-reload` | `CAI_API_RELOAD` | Dev autoreload. |
 | `--api-workers` | `CAI_API_WORKERS` | Worker processes (ignored with reload). |
 For **all** `cai` binary flags (`--tui`, `--resume`, `--yaml`, `--version`, …), see the single source of truth: [CLI commands reference — Binary `cai` CLI flags](cli/commands_reference.md#binary-cai-cli-flags).
 Interactive docs at `/api/docs` and OpenAPI spec at `/api/openapi.json`.
 ### Authentication
@ -147,6 +145,21 @@ Quick index
 - Headers: `X-CAI-API-Key`
 - Response 200: SessionDetailModel
 ### POST /api/v1/sessions/{id}/cancel
 - Description: Cancel/interrupt the currently running task in a session (equivalent to Ctrl-C in CLI).
 - Headers: `X-CAI-API-Key`
 - Response 200:
 ```json
 {"cancelled": true, "message": "Task in session <id> has been cancelled"}
 ```
 or
 ```json
 {"cancelled": false, "message": "No running task found in session <id>"}
 ```
 ### POST /api/v1/sessions/{id}/messages
 - Description: Non-streamed inference. Runs the agent and returns the final result.
 - Headers: `X-CAI-API-Key`, `Content-Type: application/json`
@ -296,6 +309,10 @@ Implementation notes (for curious devs)
  - stderr: string
  - exit_code: number | null
 - CancelTaskResponse
  - cancelled: boolean
  - message: string
 - CreateSessionRequest
  - agent: string (optional; default from CAI_AGENT_TYPE)
  - model: string (optional; default from CAI_MODEL)
--- a/docs/assets/images/case-study-dragosCTF.png
+++ b/docs/assets/images/case-study-dragosCTF.png
--- a/docs/assets/images/case-study-hackerone.png
+++ b/docs/assets/images/case-study-hackerone.png
--- a/docs/assets/images/case-study-humanoid-portada.png
+++ b/docs/assets/images/case-study-humanoid-portada.png
--- a/docs/assets/images/portada-portswigger-web-1.jpg
+++ b/docs/assets/images/portada-portswigger-web-1.jpg
--- a/docs/cai/case-studies/operator-artifact-evidence.md
+++ b/docs/cai/case-studies/operator-artifact-evidence.md
@ -0,0 +1,67 @@
 # Case study: Network evidence and compliance inventories (field operator feedback)
 This case study summarizes feedback from field use of CAI on **WSL2 / Linux** for SCM assessments by a partner certification body: PCAP capture, “screenshots” of traffic, and CSV privacy-asset reviews. It is written for operators and support — not as marketing material.
 ## Scenario
 | Goal | What went wrong (v1.1.x) | Root cause |
 |------|--------------------------|------------|
 | PCAP per service/port | `.txt` files under `packet_captures/` | `CAP_NET_RAW` failure → model substituted openssl/curl logs |
 | Screenshots of notable frames | `.txt` in `screenshots/`, later PNG from text | Shell agents have no Wireshark GUI; model improvised |
 | Assess all PAsset-XX in CSV | Partial lists over multiple turns | LLM batching + long context; no deterministic checklist |
 ## What CAI 1.1.0 improves (artifact evidence update)
 1. **Prompt + tool contract** — Only `.pcap`/`.pcapng` count as packet captures; screenshot wording reserved for real GUI capture or user-approved diagrams.
 2. **Capture failure banner** — Tool output includes remediation (`setcap`, Docker `NET_RAW`) and forbids text substitutes.
 3. **`verify_csv_inventory`** — Compliance agent can compare CSV IDs vs assessment text before closing.
 4. **Bounded capture prompts** — Documentation stresses `timeout` and `-c` so `tcpdump` does not run indefinitely.
 ## Operator playbook (recommended prompts)
 ### Live PCAP (one host / port)
 ```text
 Capture HTTPS to <TARGET_IP>: use timeout 15 tcpdump -i <IFACE> -c 200 -s 0 -w assessments/<name>.pcap "host <TARGET_IP> and port 443", then ls -lh and file that pcap. Do not leave tcpdump running indefinitely. If capture fails, report CAP_NET_RAW remediation — do not create .txt substitutes.
 ```
 Generate traffic during the window: `curl -vk https://<TARGET_IP>/`
 ### Filtered PCAP instead of “screenshot”
 ```text
 From assessments/<full>.pcap, write filtered PCAPs under assessments/filtered-pcaps/ for TLS Client Hello and HTTP GET only (tshark -r … -Y … -w …). Do not render text as PNG screenshots.
 ```
 ### Full CSV inventory (PAsset-XX)
 ```text
 Assess every PAsset-XX in <file>.csv. Before finishing, run verify_csv_inventory with that file and your full assessment in response_text. Report covered/total; list any missing IDs and complete them.
 ```
 ## One-time host setup (WSL2 / Linux)
 ```bash
 sudo setcap cap_net_raw+eip "$(command -v dumpcap)"
 sudo setcap cap_net_raw+eip "$(command -v tcpdump)"
 getcap "$(command -v tcpdump)"
 ```
 Use CAI Docker with `NET_RAW` when host `setcap` is not allowed by policy.
 ## What CAI still cannot do
 See [Platform limitations](../troubleshooting/platform_limitations.md). Summary:
 - **Wireshark GUI screenshots** via shell agents.
 - **Guaranteed all-in-one-pass** review of very large CSVs without chunking + `verify_csv_inventory`.
 - **Grant CAP_NET_RAW** without operator or IT action on the host.
 ## Verification
 - Regression tests: `tests/tools/test_capture_notice.py`, `test_evidence_inventory_check.py`
 - Manual: [Operator feedback reproduction](../troubleshooting/operator_feedback_reproduction.md)
 ## References
 - Session logs: `nopcap-onlytxt.zip`, `txt-to-png.zip` (May 2026, WSL2, alias1 model, Network / Compliance agents).
--- a/docs/cai/getting-started/MCP.md
+++ b/docs/cai/getting-started/MCP.md
@ -1,71 +1,51 @@
 # MCP
-CAI supports the Model Context Protocol (MCP) for integrating external tools and services with AI agents. MCP is supported via two transport mechanisms:
+CAI supports the Model Context Protocol (MCP) for integrating external tools and services with AI agents. Common patterns:
 1. **STDIO (Standard Input/Output)** — For local processes, including the **Burp Suite MCP** stdio proxy from PortSwigger (extract `mcp-proxy-all.jar` from the MCP Server BApp; default Burp SSE URL is usually `http://127.0.0.1:9876`):
 1. **SSE (Server-Sent Events)** - For web-based servers that push updates over HTTP connections:
 ```bash
-CAI>/mcp load http://localhost:9876/sse burp
+CAI>/mcp load stdio burp java -jar /path/to/mcp-proxy-all.jar --sse-url http://127.0.0.1:9876
 ```
-2. **STDIO (Standard Input/Output)** - For local inter-process communication:
+2. **SSE (Server-Sent Events)** — Direct HTTP/SSE only works when the server sends a compliant `Content-Type: text/event-stream` response. Many tools (including Burp’s in-process SSE) are unreliable with CAI’s client; prefer **stdio** for Burp.
 ```bash
 CAI>/mcp load http://127.0.0.1:8000/sse myserver
 ```
 Other stdio servers:
 ```bash
 CAI>/mcp load stdio myserver python mcp_server.py
 ```
-Once connected, you can add the MCP tools to any agent:
+Once connected, add the MCP tools to an agent (server name first, then agent id or index). The REPL prints a table of each tool and its status.
 ```bash
 CAI>/mcp add burp redteam_agent
 Adding tools from MCP server 'burp' to agent 'Red Team Agent'...
                                 Adding tools to Red Team Agent
 ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
 ┃ Tool                              ┃ Status ┃ Details                                         ┃
 ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
 │ send_http_request                 │ Added  │ Available as: send_http_request                 │
 │ create_repeater_tab               │ Added  │ Available as: create_repeater_tab               │
 │ send_to_intruder                  │ Added  │ Available as: send_to_intruder                  │
 │ url_encode                        │ Added  │ Available as: url_encode                        │
 │ url_decode                        │ Added  │ Available as: url_decode                        │
 │ base64encode                      │ Added  │ Available as: base64encode                      │
 │ base64decode                      │ Added  │ Available as: base64decode                      │
 │ generate_random_string            │ Added  │ Available as: generate_random_string            │
 │ output_project_options            │ Added  │ Available as: output_project_options            │
 │ output_user_options               │ Added  │ Available as: output_user_options               │
 │ set_project_options               │ Added  │ Available as: set_project_options               │
 │ set_user_options                  │ Added  │ Available as: set_user_options                  │
 │ get_proxy_http_history            │ Added  │ Available as: get_proxy_http_history            │
 │ get_proxy_http_history_regex      │ Added  │ Available as: get_proxy_http_history_regex      │
 │ get_proxy_websocket_history       │ Added  │ Available as: get_proxy_websocket_history       │
 │ get_proxy_websocket_history_regex │ Added  │ Available as: get_proxy_websocket_history_regex │
 │ set_task_execution_engine_state   │ Added  │ Available as: set_task_execution_engine_state   │
 │ set_proxy_intercept_state         │ Added  │ Available as: set_proxy_intercept_state         │
 │ get_active_editor_contents        │ Added  │ Available as: get_active_editor_contents        │
 │ set_active_editor_contents        │ Added  │ Available as: set_active_editor_contents        │
 └───────────────────────────────────┴────────┴─────────────────────────────────────────────────┘
 Added 20 tools from server 'burp' to agent 'Red Team Agent'.
 CAI>/agent 13
 CAI>Create a repeater tab
 ```
 You can list all active MCP connections and their transport types:
 ```bash
 CAI>/mcp list
 ```
-https://github.com/user-attachments/assets/386a1fd3-3469-4f84-9396-2a5236febe1f
+Other useful subcommands: `/mcp status`, `/mcp associations`, `/mcp test <server>`, and `/mcp help` (same summary as `/help mcp` and `/h mcp`).
 [https://github.com/user-attachments/assets/386a1fd3-3469-4f84-9396-2a5236febe1f](https://github.com/user-attachments/assets/386a1fd3-3469-4f84-9396-2a5236febe1f)
 ## Example: Controlling Chrome with CAI
-1) Install node, following the instructions on the [official site](https://nodejs.org/en/download/current)
+1. Install node, following the instructions on the [official site](https://nodejs.org/en/download/current)
-
+2. Install Chrome (Chromium is not compatible with this functionality)
-2) Install Chrome (Chromium is not compatible with this functionality)
+3. Run the following commands:
 3) Run the following commands:
 	```
 	/mcp load stdio devtools npx chrome-devtools-mcp@latest
 	/mcp add devtools redteam_agent
 	/agent redteam_agent
 	```
 Once this is done, you will have full control of Chrome using the red team agent.
 ```bash
 CAI>/mcp load stdio devtools npx chrome-devtools-mcp@latest
 CAI>/mcp add devtools redteam_agent
 CAI>/agent redteam_agent
 ```
 Once this is done, you will have full control of Chrome using the red team agent.
--- a/docs/cai/getting-started/commands.md
+++ b/docs/cai/getting-started/commands.md
@ -1,13 +1,494 @@
-# CAI commands (hub)
+# CAI REPL Commands
-The **canonical** command reference (binary flags and REPL slash commands) lives here:
+This document provides documentation for all commands available in the CAI (Context-Aware Interface) REPL system.
- **[CLI commands reference](../../cli/commands_reference.md)** — use this for day-to-day lookup and GitHub Pages.
+## Base Command System (`base.py`)
 TUI-specific routing and shortcuts:
 - **[TUI commands reference](../../tui/commands_reference.md)**
 ---
-*This hub replaces the long per-file command list to avoid drift; the CLI reference is updated with each framework release.*
+## Core Commands
 ### **Agent Management (`agent.py`)**
 ### **AgentCommand**
 - **Command**: `/agent`
 - **Purpose**: Managing and switching between different AI agents
 - **Features**:
  - List available agents
  - Switch between agents
  - Display agent information
  - Visualize agent interaction graphs
 - **Defaults**: The CLI default is **`orchestration_agent`** (breadth-first entry with specialist tools: `run_specialist`, `run_dual_approach_contest`, `run_parallel_specialists`). Use **`selection_agent`** for a slimmer handoff-only router. Tune worker budgets and the optional multi-front hint with **`CAI_ORCHESTRATION_WORKER_MAX_TURNS`** and **`CAI_ORCHESTRATION_MAS_HINT`** (see [Environment variables](../../environment_variables.md)).
 ### **Configuration Management (`config.py`)**
 ### **ConfigCommand**
 - **Command**: `/config` (alias `/cfg`)
 - **Purpose**: Deprecated; prints a notice to use `/env` instead. Does not change variables.
 ### **Environment variables (`env.py`)**
 ### **EnvCommand**
 - **Command**: `/env` (alias `/e`)
 - **Purpose**: Inspect and change environment variables for the current REPL process
 - **Features**:
  - Bare `/env`: table of `CAI_`* and `CTF_`* variables currently set (sensitive values masked)
  - `/env list`: numbered catalog with defaults and descriptions
  - `/env get <n|NAME>`: read one catalog entry
  - `/env set <n|NAME> <value...>`: set by catalog index or full variable name (value may contain spaces; no quotes)
  - `/env default`: restore every catalog variable to its registered default
 ### **Cost Tracking (`cost.py`)**
 ### **CostCommand**
 - **Command**: `/cost` (aliases: `/costs`, `/usage`)
 - **Purpose**: View usage costs and statistics (session via `COST_TRACKER`; persisted global totals in `~/.cai/usage.json` when usage tracking is enabled).
 - **Subcommands**:
  - `/cost` or `/cost summary` — same: session + global summary, top models snippet, hints
  - `/cost models` — per-model costs
  - `/cost daily` — last 30 days plus weekly rollup
  - `/cost sessions` — recent sessions (default 10); `/cost sessions <n>` limits rows
  - `/cost reset` — clear persisted stats (confirm with `RESET`; backup created first)
 - **Help**: `/h cost` — syntax aligned with the above
 ### **Exit (`exit.py`)**
 ### **ExitCommand**
 - **Command**: `/exit` (aliases: `/q`, `/quit`)
 - **Purpose**: Terminate the CAI REPL session with the same orderly shutdown as Ctrl+C at the prompt (including the session summary panel)
 - **Features**:
  - Clean shutdown of the REPL
  - Save current session data
  - Cleanup background processes
 ### **Help System (`help.py`)**
 ### **HelpCommand**
 - **Command**: `/help` or `/?` (aliases include `/h`). Note: **`/?`** is an alias for **`/help`** (leading slash). **`?`** alone (no slash) is a **different** command — see **Input shortcuts** below.
 - **Purpose**: Display help information and command documentation
 - **Features**:
  - **`/help commands`** (or **`/h commands`**, **`/? commands`**): one bordered help panel (same style as other `/h` topics) listing every registered slash command by category from the live registry
  - **`/help topics`**: same categories here in the REPL plus short copy on **`/help <topic>`** (detail panels; exceptions include **`/help var`**, **`/help commands`**, **`/help topics`**, **`/help aliases`**, **`/help config`**)
  - Show command usage
  - `**/help aliases`** (or `**/h aliases**`): list registered command shortcuts
  - Provide help for specific commands (e.g. `/help agent`, `/help env`; `/help model`)
  - **Environment variables:** bare `/help` shows command's guide plus **full environment reference tables** below
  - **Orchestration:** `/help var CAI_AGENT_TYPE`, `/help var CAI_ORCHESTRATION_WORKER_MAX_TURNS`, `/help var CAI_ORCHESTRATION_MAS_HINT` for the default entry agent and worker tuning
  - **Onboarding guide:** use **`/quickstart`** (aliases **`/qs`**, **`/quick`**); there is no `/help quick` or `/help quickstart` — if used, CAI prints a short hint to run **`/quickstart`**
 ### **Input shortcuts (`shortcuts.py`)**
 - **Command**: **`?`** on its own line (CLI headless REPL only; not interpreted as a command in the TUI)
 - **Purpose**: Short table of prefix keys (`/`, `$`) and prompt-toolkit bindings (Tab, Enter, multiline keys, history, Ctrl+L, etc.)
 - **Empty-line hint**: the headless REPL shows a grey italic placeholder (**`? for shortcuts · type your prompt`**) when the line is empty (defined in `prompt.py`).
 ### **History Management (`history.py`)**
 ### **HistoryCommand**
 - **Command**: `/history`
 - **Purpose**: Display conversation history with agent filtering
 - **Features**:
  - Show conversation history
  - Filter by specific agents
  - Display message tree structure
 - **Note**: `/history export` is removed; use `/save <file>` (see **Save Data**). If you run `/history export`, CAI prints a deprecation hint pointing to `/save`.
 ---
 ## Data Management Commands
 ### **Compact Conversation (`compact.py`)**
 ### **CompactCommand**
 - **Command**: `/compact`
 - **Purpose**: Compact current conversation and manage model/prompt settings
 - **Features**:
  - Reduce conversation context size
  - Change model during compaction
  - Modify prompt settings
  - Maintain conversation flow while reducing tokens
 ### **Load Data (`load.py`)**
 ### **LoadCommand**
 - **Command**: `/load`
 - **Purpose**: Load JSONL data into the current session context
 - **Features**:
  - Load conversation history from files
  - Import external data
  - Integrate with parallel configurations
  - Support for various data formats (including JSONL written by `/save` and session logs)
  - Expands `~/` in file paths when resolving JSONL locations
 ### **Save Data (`save.py`)**
 ### **SaveCommand**
 - **Command**: `/save`
 - **Purpose**: Write all agent conversation histories to **JSONL** (reload with `/load`) or **Markdown** (readable report)
 - **Features**:
  - `**.jsonl`**: one JSON object per line (`agent`, `role`, `content`, plus tool fields); same shape as the former `/history export`, loaded by `/load`
  - `**.md` / `.markdown`**: structured Markdown export (per-agent sections, roles as headings); not consumed by `/load`
  - Works with isolated parallel histories when applicable
  - Expands `~/` paths and creates parent directories as needed before writing
 ### **Memory management (`/memory`)**
 ### **MemoryCommand**
 - **Command**: `/memory`
 - **Purpose**: Manage persistent memory storage in `.cai/memory`
 - **Features**:
  - Store conversation context persistently
  - Apply memory to current context
  - Manage memory entries
  - Persistent storage across sessions
 ### **Flush History (`flush.py`)**
 ### **FlushCommand**
 - **Command**: `/flush`
 - **Purpose**: Clear conversation history
 - **Features**:
  - Clear current conversation
  - Reset agent contexts
  - Clean up memory
  - Start fresh conversation
 ---
 ## Model Management Commands
 ### **Model Configuration (`model.py`)**
 ### **ModelCommand** (`model.py`)
 - **Command**: `/model`
 - **Purpose**: View and change the current LLM model; browse the full catalog
 - **Syntax**:
  - `/model` — short table + current `CAI_MODEL`
  - `/model show` — full LiteLLM catalog (optional `supported`, search term, or both)
  - `/model <name>` / `/model <n>` — set model (same numbering as `/model show`)
 ---
 ## Advanced Features
 ### **Graph Visualization (`graph.py`)**
 ### **GraphCommand**
 - **Command**: `/graph` (alias `/g`)
 - **Purpose**: Visualize conversation flow (user, assistant, tools) as a compact graph or tables
 - **Syntax**:
  - `/graph` or `/graph show` — multi-agent layout when `CAI_PARALLEL` > 1 or multiple parallel slots exist; otherwise the active agent
  - `/graph all` — every agent with history
  - `/graph P<n>` — agent in parallel slot n (e.g. `P1`)
  - `/graph <agent_name>` — named agent (multi-word names allowed)
 - **Subcommands**:
  - `timeline` — Rich table of messages per agent (ordered by message index, not wall-clock)
  - `stats` — per-agent message and tool-call counts
  - `export <json|dot|mermaid> [filename]` — export all tracked histories to a file
 ### **CTR analysis (`ctr.py`)**
 ### **CTRCommand**
 - **Command**: `/ctr`
 - **Purpose**: Run Cut-The-Rope-style game-theoretic analysis on the current session (in-memory history, session log, or latest JSONL fallback) and manage saved runs under the CTR output base directory (`CAI_CTR_OUTPUT_DIR` or default temp layout; see `cai.ctr.paths`).
 - **Subcommands**:
  - `/ctr` — full analysis pipeline (writes a new `run_*` tree)
  - `/ctr show` — print Nash equilibrium and strategies (Rich tables; same run resolution as below)
  - `/ctr graph` — open the best available attack-graph PNG when possible; optional node/edge summary from `graph_information.txt`
  - `/ctr list` — list `run_*` directories (top level or one nested level under the base), newest first; row numbers match `/ctr use <n>`
  - `/ctr use <n|run_name|path>` — select the active run by list index, folder name under the base, or absolute path to a run directory
  - `/ctr open` — open the containing folder in the system file manager
 - **Help**: `/h ctr` — syntax aligned with the above
 ### **Parallel Execution (`parallel.py`)**
 ### **ParallelCommand**
 - **Command**: `/parallel`
 - **Purpose**: Configure and run parallel agent workflows with isolated contexts
 - **Features**:
  - Add/remove/list parallel agents
  - Queue prompts per agent or broadcast to all agents
  - Execute queued prompts with `/parallel run`
  - Merge results back into the main context
  - Exit parallel mode with or without merge
 ### **Queue Management (`queue.py`)**
 ### **QueueCommand**
 - **Command**: `/queue`
 - **Purpose**: Manage sequential prompt queue independently from parallel mode
 - **Features**:
  - Add prompts to queue
  - List queued prompts
  - Run queued prompts sequentially
  - Clear queue safely
 ### **Merge Histories (`merge.py`)**
 ### **MergeCommand**
 - **Command**: `/merge` (alias `/mrg`)
 - **Purpose**: Merge parallel agent contexts into main context and exit parallel mode
 - **Features**:
  - Combine histories from multiple agents
  - Integrate parallel conversation results into the current main thread
  - Automatically leave parallel mode after successful merge
  - Tab completion for agent arguments matches `/flush agent` (non-empty histories) and omits agents already listed in the command
 ---
 ## Integration Commands
 ### **MCP Integration (`mcp.py`)**
 ### **MCPCommand**
 - **Command**: `/mcp` (alias `/m`)
 - **Purpose**: Manage MCP (Model Context Protocol) servers and their tools
 - **Subcommands** (see also `/mcp help`, `/help mcp`, and `/h mcp`):
  - `load <url> <name>` — SSE server; `load sse <url> <name>` — legacy SSE form; `load stdio <name> <command> [args…]` — stdio server
  - `list` — active servers (bare `/mcp` is equivalent)
  - `add <server_name> <agent_name_or_number>` — **server first**, then agent (name or index)
  - `remove`, `tools`, `status`, `associations`, `test`, `help`
 - **Features**:
  - Load SSE MCP servers
  - Load STDIO MCP servers
  - List active MCP connections
  - Add MCP tools to agents
  - Manage MCP server lifecycle
 ---
 ## System Management Commands
 ### **Shell Access (`shell.py`)**
 ### **ShellCommand**
 - **Command**: `/shell` (aliases: `/s`, `$` as the first token on the line)
 - **Purpose**: Execute shell commands from within the REPL
 - **Features**:
  - Run system commands
  - Access workspace directory
  - Container workspace support
  - Signal handling for processes
 - **Note**: To send a signal to a host OS process by PID (similar to the removed dedicated `/kill` command), use the shell’s `kill`, for example `**/shell kill <PID>`** (or `kill -TERM`, `kill -9`, etc., as supported by your shell).
 ### **Virtualization (`virtualization` package / `_virtualization_monolith.py`)**
 ### **VirtualizationCommand**
 - **Command**: `/virtualization` or `/virt`
 - **Purpose**: Manage Docker-based virtualization environments
 - **Subcommands**: `info` (same as no args), `list`, `set <container_id>`, `clear`, `pull <image>`, `run <image_or_id>` — `run` starts a new container from an image unless the token is a **unique** existing container-ID prefix (then it activates); `set <id>` or bare `/virt <id>` also attach.
 - **Features**:
  - Set up Docker containers
  - Manage container lifecycle
  - Workspace virtualization
  - Environment isolation
 ### **Workspace Management (`workspace.py`)**
 ### **WorkspaceCommand**
 - **Command**: `/workspace` or `/ws`
 - **Purpose**: Manage named workspace (`CAI_WORKSPACE`) and paths on host or in an active Docker container
 - **Subcommands**: `set <name>`, `get` (same as no args; there is no `show`), `ls [path]`, `exec <cmd>`, `copy` (requires `CAI_ACTIVE_CONTAINER` and the `container:` prefix on exactly one side)
 - **Features**:
  - Host workspace dirs under `CAI_WORKSPACE_DIR` (default `~/.cai/workspace`)
  - When `CAI_ACTIVE_CONTAINER` is set: `ls` and `exec` run in the container workspace path; `copy` uses `docker cp`
 - **REPL help**: `/h workspace` matches these subcommands.
 - **Do not use**: a `show` subcommand (none exists) or `list` as a subcommand name — use `**ls`**.
 | Invocation                       | Behaviour                                                                                                                                            |
 | -------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `/workspace` or `/workspace get` | Prints workspace name, environment (host vs container), resolved paths, and short hints for subcommands.                                             |
 | `/workspace set <name>`          | Sets `CAI_WORKSPACE` (label: letters, digits, `_`, `-` only). Creates the host folder and, if a **running** container is active, the path inside it. |
 | `/workspace ls` [path]           | Lists files (container workspace when `CAI_ACTIVE_CONTAINER` is usable; else host). Optional path is relative to the workspace root.                 |
 | `/workspace exec <cmd…>`         | Shell in workspace cwd (container when active, else host).                                                                                           |
 | `/workspace copy <src> <dst>`    | `docker cp`; **requires** `CAI_ACTIVE_CONTAINER`; `**container:`** on exactly one path.                                                              |
 To set `CAI_ACTIVE_CONTAINER`, attach a container with `**/virtualization**` or `**/virt**` (see `**/h virtualization**`).
 ### **Quickstart (`quickstart.py`)**
 ### **QuickstartCommand**
 - **Command**: `/quickstart` (aliases **`/qs`**, **`/quick`**)
 - **Purpose**: Display setup information for new users
 - **Features**:
  - Essential setup guidance
  - Configuration instructions
  - Getting started tutorial
  - Auto-runs on first launch
 ---
 ## Utility Commands
 ### **Command Completion (`completer.py`)**
 ### **FuzzyCommandCompleter**
 - **Purpose**: Intelligent command completion with fuzzy matching
 - **Features**:
  - Command auto-completion
  - Fuzzy matching for typos
  - Subcommand suggestions
  - Argument completion
  - Command shadowing detection
 ---
 ## Usage Examples
 ### Basic Workflow
 ```bash
 # Start CAI REPL
 cai
 # View available agents
 /agent list
 # Switch to a specific agent
 /agent switch <agent_name>
 # View conversation history
 /history
 # Change model
 /model gpt-4
 # Clear conversation
 /flush
 # Exit
 /exit
 ```
 ### Advanced Features
 ```bash
 # Set up parallel execution
 /parallel add red_teamer
 /parallel add network_traffic_analyzer
 # Add prompts (per agent or all)
 /parallel prompt all "Scan 192.168.1.0/24"
 # Execute in parallel
 /parallel run
 # Merge all parallel contexts into main context and exit parallel mode
 /merge
 # Optional: exit without merging contexts
 /parallel clear
 ```
 ### Integration Examples
 ```bash
 # Burp Suite MCP (PortSwigger): stdio proxy to the BApp SSE endpoint — replace /path/to with the extracted JAR path
 /mcp load stdio burp java -jar /path/to/mcp-proxy-all.jar --sse-url http://127.0.0.1:9876
 # Add MCP tools to agent (server name first)
 /mcp add burp <agent_name_or_number>
 # Set up virtualized environment and a named workspace
 /virtualization pull kalilinux/kali-rolling
 /virtualization run kalilinux/kali-rolling
 /virtualization list
 /workspace set myproject
 ```
 ---
 ## Command Registration
 All commands are automatically registered when their respective modules are imported through the `__init__.py` file. The command system uses a registry pattern to track all available commands and their aliases.
 ---
 ## File Structure
 ```
 src/cai/repl/commands/
 ├── __init__.py          # Module exports and imports
 ├── base.py              # Base command class
 ├── agent.py             # Agent management
 ├── compact.py           # Conversation compaction
 ├── completer.py         # Command completion
 ├── config.py            # Configuration management
 ├── cost.py              # Cost tracking
 ├── env.py               # Environment variables
 ├── exit.py              # REPL exit
 ├── flush.py             # History clearing
 ├── graph.py             # Graph visualization
 ├── help.py              # Help system
 ├── history.py           # History management
 ├── load.py              # Data loading
 ├── mcp.py               # MCP integration
 ├── memory/              # /memory command (compacted summaries, .cai/memory)
 ├── merge.py             # History merging
 ├── model.py             # Model management
 ├── parallel.py          # Parallel execution
 ├── quickstart.py        # User onboarding
 ├── run.py               # Parallel execution trigger
 ├── shell.py             # Shell access
 ├── virtualization/      # Container management (re-exports monolith)
 └── workspace.py         # Workspace management
 ```
 ---
 ## Extending the Command System
 To add new commands:
 1. Create a new Python file in `src/cai/repl/commands/`
 2. Import the base `Command` class from `base.py`
 3. Extend the `Command` class with your implementation
 4. Use the `register_command` decorator or function
 5. Add the import to `__init__.py`
 Example:
 ```python
 from cai.repl.commands.base import Command, register_command
 class MyCommand(Command):
    def __init__(self):
        super().__init__(
            name="/mycommand",
            description="My custom command",
            aliases=["/my", "/mc"]
        )
    def execute(self, args):
        # Command implementation
        pass
 register_command(MyCommand())
 ```
--- a/docs/cai/getting-started/configuration.md
+++ b/docs/cai/getting-started/configuration.md
@ -16,7 +16,7 @@ The OPENAI_API_KEY must not be left blank. It should contain either "sk-123" (as
 For a complete reference organized by use case, see [Environment Variables Reference](../../environment_variables.md).
-**In the REPL:** `/env list` shows the catalog with **current values** and index numbers; bare `/env` shows `CAI_*` / `CTF_*` in this session. **`/help`** includes the **full environment reference** (tables) after the quick guide; **`/help var NAME`** opens **long-form** help for a single variable. See also [Environment Variables — Discovering variables in the REPL](../../environment_variables.md#discovering-variables-in-the-repl).
+**In the REPL:** `/env list` shows variables with **current values** and index numbers; bare **`/env`** shows **`CAI_*`** / **`CTF_*`** values in the current session. **`/help`** includes the **full environment reference** (tables) after the quick guide; **`/help var NAME`** opens **long-form** help for a single variable. **`/config`** is deprecated and only prints a pointer to **`/env`**. See also [Environment Variables — Discovering variables in the REPL](../../environment_variables.md#discovering-variables-in-the-repl).
 | Variable | Description | Default |
 |----------|-------------|---------|
@ -29,24 +29,26 @@ For a complete reference organized by use case, see [Environment Variables Refer
 | CAI_DEBUG | Set debug output level (0: Only tool outputs, 1: Verbose debug output, 2: CLI debug output) | 1 |
 | CAI_BRIEF | Enable/disable brief output mode | false |
 | CAI_MAX_TURNS | Maximum number of turns for agent interactions | inf |
 | CAI_ORCHESTRATION_WORKER_MAX_TURNS | Max `Runner` turns per specialist worker spawned by `orchestration_agent` tools (`run_specialist`, `run_dual_approach_contest`, `run_parallel_specialists`). Integer 1–32 | 6 |
 | CAI_ORCHESTRATION_MAS_HINT | When `true`, `orchestration_agent` may receive one synthetic `user`-role nudge per `Runner` run if the prompt looks multi-front but only `run_specialist` ran (suggests parallel or contest tools). Set `false` to disable | true |
 | CAI_MAX_INTERACTIONS | Maximum number of interactions (tool calls, agent actions, etc.) allowed in a session. If exceeded, only CLI commands are allowed until increased. If force_until_flag=true, the session will exit | inf |
 | CAI_PRICE_LIMIT | Price limit for the conversation in dollars. If exceeded, only CLI commands are allowed until increased. If force_until_flag=true, the session will exit | 1 |
 | CAI_TRACING | Enable/disable OpenTelemetry tracing. When enabled, traces execution flow and agent interactions for debugging and analysis | true |
-| CAI_AGENT_TYPE | Specify the agents to use (e.g., boot2root, one_tool, redteam_agent). Use "/agent" command in CLI to list all available agents | redteam_agent |
+| CAI_AGENT_TYPE | Registered agent key. Defaults to `orchestration_agent` (breadth-first entry: specialist tools `run_specialist`, `run_dual_approach_contest`, `run_parallel_specialists` plus handoffs). Use `selection_agent` for a handoff-only router without those tools, or pin a specialist such as `redteam_agent` | orchestration_agent |
 | CAI_STATE | Enable/disable stateful mode. When enabled, the agent will use a state agent to keep track of the state of the network and the flags found | false |
-| CAI_MEMORY | Enable/disable memory mode (episodic: use episodic memory, semantic: use semantic memory, all: use both episodic and semantic memory) | false |
+| CAI_COMPACTED_MEMORY | When true, inject `/compact` conversation summaries into agent system prompts | false |
 | CAI_MEMORY_ONLINE | Enable/disable online memory mode | false |
 | CAI_MEMORY_OFFLINE | Enable/disable offline memory | false |
 | CAI_ENV_CONTEXT | Add environment context, dirs and current env available | true |
 | CAI_MEMORY_ONLINE_INTERVAL | Number of turns between online memory updates | 5 |
 | CAI_SUPPORT_MODEL | Model to use for the support agent | o3-mini |
 | CAI_SUPPORT_INTERVAL | Number of turns between support agent executions | 5 |
-| CAI_STREAM | Enable/disable streaming output in rich panel | false |
+| CAI_STREAM | Enable/disable streaming output for LLM inference (token-by-token display). Does NOT affect tool output | false |
 | CAI_TOOL_STREAM | Enable/disable streaming output for tool executions (real-time command output). Independent of CAI_STREAM | true |
 | CAI_DEBUG_TOOLS_VIZ | Enable debug output for tool visualization and panel rendering | false |
 | CAI_SHOW_CACHE | Show cache information and message history list | false |
 | CAI_TELEMETRY | Enable/disable telemetry | true |
 | CAI_PARALLEL | Number of parallel agent instances to run. When set to values greater than 1, executes multiple instances of the same agent in parallel and displays all results | 1 |
 | CAI_GUARDRAILS | Enable/disable security guardrails for agents. When set to "true", applies security guardrails to prevent potentially dangerous outputs and inputs | false |
 | CAI_GCTR_NITERATIONS | Number of tool interactions before triggering GCTR (Generative Cut-The-Rope) analysis in bug_bounter_gctr agent. Only applies when using gctr-enabled agents | 5 |
-| CAI_ACTIVE_CONTAINER | Docker container ID where commands should be executed. When set, shell commands and tools execute inside the specified container instead of the host. Automatically set when CTF challenges start (if CTF_INSIDE=true) or when switching containers via /virtualization command | - |
+| CAI_ACTIVE_CONTAINER | Docker container ID where commands should be executed. When set, shell commands and tools execute inside the specified container instead of the host. Automatically set when CTF challenges start (if CTF_INSIDE=true) or when attaching a container via `/virtualization` / `/virt` in the REPL | - |
 | CAI_TOOL_TIMEOUT | Override the default timeout for tool command executions in seconds. When set, this value overrides all default timeouts for shell commands and tool executions | varies (10s for interactive, 100s for regular) |
 ## Custom OpenAI Base URL Support
--- a/docs/cai/getting-started/installation.md
+++ b/docs/cai/getting-started/installation.md
@ -4,6 +4,26 @@
 pip install cai-framework
 ```
 ## Using CAI with Claude Code, Codex, and OpenCode
 You can use CAI with different coding assistants while keeping the same repository and environment.
 ### Recommended setup
 1. Use one project-local virtual environment.
 2. Keep a single `.env` file for CAI configuration.
 3. Reuse the same branch/worktree across assistants.
 4. Validate CAI behavior from the terminal after assistant-driven edits.
 ### Assistant-agnostic workflow
 - Edit and plan with your preferred assistant (Claude Code, Codex, or OpenCode).
 - Run CAI commands from the same project terminal/session.
 - For multi-agent execution, use:
  - `/parallel add ...`
  - `/parallel run`
  - `/merge` (or `/parallel clear` to exit without merge)
 ## OS X
 ```bash
 # Install homebrew
@ -72,6 +92,8 @@ Here you will find all the instructions to install WSL
 From Powershell write: ` wsl --install`
 For **packet capture** on WSL2 (`tcpdump` / `tshark`), see [Packet capture on WSL2](packet_capture_wsl.md) (`setcap`, Docker `NET_RAW`, valid PCAP vs text substitutes).
 ```bash
 sudo apt-get update && \
    sudo apt-get install -y git python3-pip python3-venv
--- a/docs/cai/getting-started/packet_capture_wsl.md
+++ b/docs/cai/getting-started/packet_capture_wsl.md
@ -0,0 +1,46 @@
 # Packet capture on WSL2 and evidence artifacts
 CAI agents capture traffic with `tcpdump`, `tshark`, or `dumpcap`. On **WSL2** (and some locked-down hosts), live capture often fails until the environment grants raw sockets.
 ## Fix capture permissions (WSL2 / Linux)
 ```bash
 # One-time: allow dumpcap to capture without root
 sudo setcap cap_net_raw+eip "$(command -v dumpcap)"
 # Verify
 getcap "$(command -v dumpcap)"
 ```
 If `tcpdump` still fails, retry with `sudo tcpdump ...` when your policy allows it. CAI may prompt for sudo when tool output indicates missing privileges.
 ## Prefer CAI Docker (NET_RAW enabled)
 CAI containers are started with `--cap-add=NET_RAW` for capture tools. Run network assessments inside the CAI container when host WSL lacks capabilities.
 ## Evidence types (what to ask the agent for)
 | User asks for | Valid artifact | Invalid substitute |
 |---------------|----------------|-------------------|
 | PCAP | `.pcap` / `.pcapng` | `.txt` logs from curl/openssl in `packet_captures/` |
 | Screenshot of traffic | Filtered PCAP or labeled `tshark` export | Text file in `screenshots/` |
 | GUI / Wireshark window | Not available via shell agent | PNG rendered from text |
 When capture fails, CAI prepends a **PACKET-CAPTURE FAILURE** notice to tool output. The agent should report the blocker and remediation—not fabricate captures.
 ## Filtered PCAPs (recommended)
 ```bash
 tshark -r assessments/full.pcap -Y "http.request" -w assessments/filtered-pcaps/http_only.pcap
 ```
 ## CSV inventories (e.g. PAsset-XX)
 Use the **Risk & Compliance** agent tool `verify_csv_inventory`:
 - Pass the CSV path and your latest assessment text.
 - Require `MISSING from response: none` before closing the task.
 Example user prompt:
 > List every PAsset-XX in `assets.csv`, assess each, run `verify_csv_inventory`, and do not finish until covered/total is complete.
--- a/docs/cai/troubleshooting/operator_feedback_reproduction.md
+++ b/docs/cai/troubleshooting/operator_feedback_reproduction.md
@ -0,0 +1,99 @@
 # Reproducing field operator feedback scenarios
 This guide reproduces the PCAP / screenshot / inventory issues from May 2026 session logs shared by a partner certification body, and how to verify fixes in CAI **v1.1.0+** (artifact evidence changes on branch `cai-v1.1.0`).
 ## Attached logs (reference)
 | Zip | Session | Main symptom |
 |-----|---------|--------------|
 | `nopcap-onlytxt.zip` | Compliance / SCM-1 | `CAP_NET_RAW` failure → `.txt` in `packet_captures/` |
 | `txt-to-png.zip` | Network analyzer | `.txt` “screenshots” → ImageMagick PNGs |
 ## 1) PCAP permission failure notice
 **Simulate (no real capture needed):**
 ```bash
 cd cai
 python3 -c "
 from cai.tools.evidence.capture_notice import apply_packet_capture_notice
 cmd = 'tcpdump -i any -w /tmp/t.pcap -c 1'
 out = '''tcpdump: any: You don't have permission to perform this capture
 (Attempt to create packet socket failed - CAP_NET_RAW may be required)'''
 print(apply_packet_capture_notice(cmd, out))
 "
 ```
 **Expected:** output starts with `[CAI PACKET-CAPTURE FAILURE]`.
 **Live on WSL without setcap:**
 ```bash
 tcpdump -i any -c 1 -w /tmp/test.pcap 2>&1 | head -5
 ```
 Run the same command via CAI `generic_linux_command`; the model should see the notice and must not write openssl/curl output into `packet_captures/`.
 **Fix environment then retest:**
 ```bash
 sudo setcap cap_net_raw+eip "$(command -v dumpcap)"
 ```
 ## 2) False screenshots (txt → png)
 **Reproduce user prompt (network or CTF agent):**
 > Initiate communications on open ports on 192.0.2.104, create pcaps per port under assessments/, and take screenshots of notable parts in the pcaps.
 **Before fix:** agent writes `assessments/screenshots/*.txt` (tshark text).
 **After user correction:** agent should prefer `assessments/filtered-pcaps/*.pcap` and must not claim ImageMagick PNGs are Wireshark GUI captures.
 **Verify in workspace:**
 ```bash
 find assessments -name '*.txt' -path '*/screenshots/*'
 file assessments/real-screenshots/*.png 2>/dev/null | head -3
 ```
 PNG files that are "PNG image data" but only contain rendered text are **diagrams**, not GUI screenshots—expected limitation.
 ## 3) CSV inventory completeness
 ```bash
 cd cai
 .venv/bin/python3 -m pytest \
  tests/tools/test_capture_notice.py \
  tests/tools/test_evidence_inventory_check.py \
  tests/tools/test_tool_generic_linux_command.py::test_packet_capture_failure_notice_detects_tcpdump_error \
  tests/tools/test_tool_generic_linux_command.py::test_packet_capture_failure_notice_skips_tshark_read_only \
  tests/tools/test_tool_generic_linux_command.py::test_generic_linux_command_prepends_capture_notice \
  -q --timeout=60
 ```
 **Expected:** `8 passed` in under a few seconds. If pytest hangs after `....`, an old build was waiting for an interactive sudo password—upgrade to the branch that skips sudo retry when the packet-capture notice is already present, then Ctrl+C and re-run.
 **Interactive test:**
 1. Create `workspace/test_assets.csv` with `PAsset-01` … `PAsset-10`.
 2. Ask Compliance agent to assess all; paste partial reply into `verify_csv_inventory` via tool call.
 3. Confirm `MISSING` lists gaps.
 ## 4) Replay JSONL logs (read-only)
 ```bash
 unzip -p ~/Downloads/nopcap-onlytxt.zip '*.jsonl' | \
  python3 -c "
 import sys, json, re
 for i, line in enumerate(sys.stdin, 1):
    if 'CAP_NET_RAW' in line or 'packet_captures' in line and '.txt' in line:
        print(i, line[:200])
 " | head -20
 ```
 This confirms permission errors and txt substitutes in the original session.
 ## What remains impossible (tell the operator)
 See `docs/cai/troubleshooting/platform_limitations.md` for customer-facing explanations.
--- a/docs/cai/troubleshooting/platform_limitations.md
+++ b/docs/cai/troubleshooting/platform_limitations.md
@ -0,0 +1,47 @@
 # Platform limitations (customer-facing)
 Items CAI **mitigates** (prompts, tool notices, `verify_csv_inventory`) but **cannot fully eliminate**.
 ## Desktop / Wireshark screenshots
 **What users expect:** PNG of the Wireshark GUI (packet list, decode panes).
 **What shell agents have:** `generic_linux_command`, optional `execute_code`—no display server, no `computer_screenshot` tool on CTF/network/compliance agents.
 **What CAI can do:** Filtered PCAPs, `tshark` field exports, markdown summaries, optional text-rendered diagrams (clearly labeled).
 **What to tell the operator:** Ask for *filtered PCAPs* or *tshark export of frames X–Y*, not GUI screenshots, unless you run a separate desktop automation stack.
 ## 100% LLM rule compliance
 Prompts and tool banners reduce wrong substitutions; models may still occasionally ignore them under long contexts or repeated interruptions.
 **Mitigation:** Short, explicit tasks; verify artifacts on disk (`file *.pcap`, `verify_csv_inventory`).
 **Not a bug:** Residual hallucination risk is inherent to LLM agents.
 ## CAP_NET_RAW on WSL2
 **Cause:** Linux capability not granted to `dumpcap`/`tcpdump` in the WSL VM.
 **What CAI does:** Detect failure, suggest `setcap`, sudo, or Docker; trigger sudo prompt when TTY allows.
 **What CAI cannot do:** Grant kernel capabilities without the user (or installer) configuring the host.
 **Action for the operator:** `sudo setcap cap_net_raw+eip $(which dumpcap)` or use CAI Docker with `NET_RAW`.
 ## Very large CSV inventories
 **Cause:** Context limits; model may stop after partial batches even with good prompts.
 **What CAI added:** `verify_csv_inventory` tool on Compliance agent—deterministic missing-ID list.
 **What still helps:** Split CSV by chapter; run verify after each batch; merge results.
 **Not solved by prompts alone** for multi-thousand-row sheets without chunking.
 ## Agent interruption (`SYSTEM CONTEXT NOTE`)
 When the user switches agents or tasks, CAI injects a note to prioritize the new request. Earlier work may stop mid-flight.
 **Not the PCAP bug**—by design. Use “resume previous task” if continuation is intended.
--- a/docs/cai_architecture.md
+++ b/docs/cai_architecture.md
@ -76,7 +76,7 @@ cai
 ### 🔹 Agent
-At its core, CAI abstracts its cybersecurity behavior via `Agents` and agentic `Patterns`. An Agent in *an intelligent system that interacts with some environment*. More technically, within CAI we embrace a robotics-centric definition wherein an agent is anything that can be viewed as a system perceiving its environment through sensors, reasoning about its goals and acting accordingly upon that environment through actuators (*adapted* from Russel & Norvig, AI: A Modern Approach). In cybersecurity, an `Agent` interacts with systems and networks, using peripherals and network interfaces as sensors, reasons accordingly and then executes network actions as if actuators. Correspondingly, in CAI, `Agent`s implement the `ReACT` (Reasoning and Action) agent model[3].
+At its core, CAI abstracts its cybersecurity behavior via `Agents` and agentic `Patterns`. An Agent in *an intelligent system that interacts with some environment*. More technically, within CAI we embrace a robotics-centric definition wherein an agent is anything that can be viewed as a system perceiving its environment through sensors, reasoning about its goals and and acting accordingly upon that environment through actuators (*adapted* from Russel & Norvig, AI: A Modern Approach). In cybersecurity, an `Agent` interacts with systems and networks, using peripherals and network interfaces as sensors, reasons accordingly and then executes network actions as if actuators. Correspondingly, in CAI, `Agent`s implement the `ReACT` (Reasoning and Action) agent model[3].
 For more details, including examples and implementation guidance, see the [Agents documentation](agents.md).
@ -87,7 +87,7 @@ For more details, including examples and implementation guidance, see the [Agent
 You may find different [tools](src/cai/tools). They are grouped in 6 major categories inspired by the security kill chain[2]:
-1. Reconnaissance and weaponization - *reconnaissance*  (crypto, listing, etc.)
+1. Reconnaissance and weaponization - *reconnaissance*  (crypto, listing, etc)
 2. Exploitation - *exploitation*
 3. Privilege escalation - *escalation*
 4. Lateral movement - *lateral*
@ -116,16 +116,16 @@ wherein:
 - **\\(C\\) (Communication Protocol):** A messaging function \\( C: A \times A \to M \\), where \\( M \\) is a message space, defining how agents share information.
 - **\\(E\\) (Execution Model):** A function \\( E: A \times I \to O \\) where \\( I \\) is the input space and \\( O \\) is the output space, defining how agents perform tasks.
-When building `Patterns`, we generally classify them among one of the following categories, though others exist:
+When building `Patterns`, we generall y classify them among one of the following categories, though others exist:
-| **Agentic** `Pattern` **categories** | **Description**                                                                                                                                                                                                                                                                                                                                               |
+| **Agentic** `Pattern` **categories** | **Description** |
-|--------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+|--------------------|------------------------|
-| `Swarm` (Decentralized) | Agents share tasks and self-assign responsibilities without a central orchestrator. Handoffs occur dynamically. *An example of a peer-to-peer agentic pattern is the `CTF Agentic Pattern`, which involves a team of agents working together to solve a CTF challenge with dynamic handoffs.*                                                                 |
+| `Swarm` (Decentralized) | Agents share tasks and self-assign responsibilities without a central orchestrator. Handoffs occur dynamically. *An example of a peer-to-peer agentic pattern is the `CTF Agentic Pattern`, which involves a team of agents working together to solve a CTF challenge with dynamic handoffs.* |
-| `Hierarchical` | A top-level agent (e.g., "PlannerAgent") assigns tasks via structured handoffs to specialized sub-agents. Alternatively, the structure of the agents is hardcoded into the agentic pattern with pre-defined handoffs.                                                                                                                                         |
+| `Hierarchical` | A top-level agent (e.g., "PlannerAgent") assigns tasks via structured handoffs to specialized sub-agents. Alternatively, the structure of the agents is harcoded into the agentic pattern with pre-defined handoffs. |
 | `Chain-of-Thought` (Sequential Workflow) | A structured pipeline where Agent A produces an output, hands it to Agent B for reuse or refinement, and so on. Handoffs follow a linear sequence. *An example of a chain-of-thought agentic pattern is the `ReasonerAgent`, which involves a Reasoning-type LLM that provides context to the main agent to solve a CTF challenge with a linear sequence.*[1] |
-| `Auction-Based` (Competitive Allocation) | Agents "bid" on tasks based on priority, capability, or cost. A decision agent evaluates bids and hands off tasks to the best-fit agent.                                                                                                                                                                                                                      |
+| `Auction-Based` (Competitive Allocation) | Agents "bid" on tasks based on priority, capability, or cost. A decision agent evaluates bids and hands off tasks to the best-fit agent. |
-| `Recursive` | A single agent continuously refines its own output, treating itself as both executor and evaluator, with handoffs (internal or external) to itself. *An example of a recursive agentic pattern is the `CodeAgent` (when used as a recursive agent), which continuously refines its own output by executing code and updating its own instructions.*           |
+| `Recursive` | A single agent continuously refines its own output, treating itself as both executor and evaluator, with handoffs (internal or external) to itself. *An example of a recursive agentic pattern is the `CodeAgent` (when used as a recursive agent), which continuously refines its own output by executing code and updating its own instructions.* |
-| `Parallelization` | Multiple agents run in parallel, each handling different subtasks or independent inputs simultaneously. This approach speeds up processing when tasks do not depend on each other. *For example, you can launch several agents to analyze different log files or scan multiple IP addresses at the same time, leveraging concurrency to improve efficiency.*  |
+| `Parallelization` | Multiple agents run in parallel, each handling different subtasks or independent inputs simultaneously. This approach speeds up processing when tasks do not depend on each other. *For example, you can launch several agents to analyze different log files or scan multiple IP addresses at the same time, leveraging concurrency to improve efficiency.* |
 Moreover in this new version we could orchestrate agents and add decision mechanism in several ways. See [Orchestrating multiple agents](multi_agent.md)
@ -134,7 +134,7 @@ Moreover in this new version we could orchestrate agents and add decision mechan
 During the agentic flow (conversation), we distinguish between **interactions** and **turns**.
 - **Interactions** are sequential exchanges between one or multiple agents. Each agent executing its logic corresponds with one *interaction*. Since an `Agent` in CAI generally implements the `ReACT` agent model[3], each *interaction* consists of 1) a reasoning step via an LLM inference and 2) act by calling zero-to-n `Tools`. 
- **Turns**: A turn represents a cycle of one or more **interactions** which finishes when the `Agent` (or `Pattern`) executing returns `None`, judging there're no further actions to undertake.
+- **Turns**: A turn represents a cycle of one ore more **interactions** which finishes when the `Agent` (or `Pattern`) executing returns `None`, judging there're no further actions to undertake.
 > CAI Agents are not related to Assistants in the Assistants API. They are named similarly for convenience, but are otherwise completely unrelated. CAI is entirely powered by the Chat Completions API and is hence stateless between calls.
--- a/docs/cai_benchmark.md
+++ b/docs/cai_benchmark.md
@ -171,11 +171,11 @@ Currently, supporting the following benchmarks, refer to [`ctf_configs.jsonl`](h
 [^3]: **Medium (`Graduate Level`)**: Aimed at participants with a solid grasp of cybersecurity principles. Focus areas include intermediate exploits including web shells, network traffic analysis, and steganography.
-[^4]: **Hard (`Professionals`)**: Crafted for experienced penetration testers. Focus areas include advanced techniques such as heap exploitation, kernel vulnerabilities, and complex multistep challenges.
+[^4]: **Hard (`Professionals`)**: Crafted for experienced penetration testers. Focus areas include advanced techniques such as heap exploitation, kernel vulnerabilities, and complex multi-step challenges.
 [^5]: **Very Hard (`Elite`)**: Designed for elite, highly skilled participants requiring innovation. Focus areas include cutting-edge vulnerabilities like zero-day exploits, custom cryptography, and hardware hacking.
-[^6]: A meta-benchmark is a benchmark of benchmarks: a structured evaluation framework that measures, compares, and summarizes the performance of systems, models, or methods across multiple underlying benchmarks rather than a single one.
+[^6]: A meta-benchmark is a a benchmark of benchmarks: a structured evaluation framework that measures, compares, and summarizes the performance of systems, models, or methods across multiple underlying benchmarks rather than a single one.
 [^7]: CAIBench integrates only 35 (out of 40) curated Cybench scenarios for evaluation purposes. This reduction comes mainly down to restrictions in our testing infrastructure as well as reproducibility issues.
@ -215,7 +215,7 @@ Some of the backends need and url to the api base, set as follows in .env: NAME_
 OLLAMA_API_BASE="..."
 OPENROUTER_API_BASE="..."
 ```
-Once everything is configured run the script
+Once evething is configured run the script
 ```bash
 python benchmarks/eval.py --model MODEL_NAME --dataset_file INPUT_FILE --eval EVAL_TYPE --backend BACKEND
@ -324,7 +324,7 @@ IBAN
 EUROPEAN_BANK_ACCOUNT
 ```
-This is an example of how a text should be sanitized:
+This is an example of how a text sould be sanitized:
 ```
 "Contact Mikel at mikel@example.com" → "Contact [PERSON] at [EMAIL_ADDRESS]"
@ -333,8 +333,8 @@ This is an example of how a text should be sanitized:
 Some annotation rules:
 - Each detected entity should be sanitized using the **format: [ENTITY_TYPE]**
- Priorize IP_ADDRESS to URL: `https://192.168.2.100` is anonymized like this: `https://[IP_ADDRESS]:5050` instead of [URL]
+- Priorize IP_ADDRESS to URL: `https://192.168.2.100` is anonimized like this: `https://[IP_ADDRESS]:5050` instead of [URL]
- DATE_TIME is used for dates and for times, in this case `2025-03-11 11:41 UTC` it should be anonymized like this: ` [DATE_TIME] [DATE_TIME]`
+- DATE_TIME is used for dates and for times, in this case `2025-03-11 11:41 UTC` it sould be anonimized like this: ` [DATE_TIME] [DATE_TIME]`
 If you have any questions about the annotation, please write to us.
@ -397,7 +397,7 @@ python benchmarks/eval.py --model alias1 --dataset_file benchmarks/cyberPII-benc
 The input CSV file must contain the following columns:
 - id: Unique row identifier
- target_text: The original text from memory01_80 dataset to be annotated
+- target_text: The original text from memory01_80 dataseto be annotated
 - target_text_{annotator}_sanitized: The sanitized version of the text produced by each annotator
--- a/docs/cai_faq.md
+++ b/docs/cai_faq.md
@ -86,9 +86,9 @@
 ??? question "Where can I list all the environment variables? /env"
-    Use **`/env list`** to see the full catalog with **current values** and index numbers for **`/env set`**. Bare **`/env`** shows only `CAI_*` / `CTF_*` keys in the current session.
+    Use **`/env list`** to see all catalog variables with **current values** and index numbers for **`/env set`**. Use bare **`/env`** for **`CAI_*`** / **`CTF_*`** values in the current session only.
-    For **full documentation tables** (defaults, constraints, when each applies), run **`/help`** and scroll past the quick guide, or **`/help topics`** and read to the end. For **one variable** in depth, use **`/help var VARIABLE_NAME`** (e.g. `/help var CAI_DEBUG`).
+    For **full documentation tables** (defaults, constraints, when each applies), run bare **`/help`** and scroll past the quick guide. **`/help topics`** lists slash commands by category and how to open **`/help <topic>`** panels (no env tables). For **one variable** in depth, use **`/help var VARIABLE_NAME`** (e.g. `/help var CAI_DEBUG`).
    The same topics are covered on the site in [Environment Variables](environment_variables.md).
@ -96,6 +96,8 @@
 ??? question "How to know more about the CLI? /help"
    In the **CLI headless** REPL, type **`?`** alone for a compact **input shortcuts** panel. **`/?`** is an alias for **`/help`** (full guide and env tables when bare).
    ![cai-006-help](media/cai-006-help.png)
--- a/docs/cai_pro.md
+++ b/docs/cai_pro.md
@ -14,50 +14,55 @@
 The cybersecurity AI landscape is rapidly evolving, and professionals need tools that can keep pace with sophisticated threats. CAI PRO delivers:
- **🚀 State-of-the-Art Performance**: Access to `alias1`, our cutting-edge cybersecurity model that **outperforms GPT-5** in CTF benchmarks
+- **🚀 The Best Cybersecurity LLMs**: Unlimited access to the **entire Alias model family** (`alias1` and successors), purpose-built for offensive and defensive security and **outperforming GPT-5** in CTF benchmarks
- **🔓 Zero Restrictions**: Unrestricted AI with no refusals, specifically trained for offensive security tasks
+- **♾️ Unlimited Alias Tokens**: No per-query metering, no monthly token caps — run long engagements and parallel agents without rationing
 - **🧩 Extended Scaffold Library**: Access to additional agent scaffolds and patterns (red/blue/purple teamers, bug bounty, DFIR, APT, reverse engineering, SAST, SDR/Wi-Fi, and more) beyond the Community defaults
 - **🔓 Unrestricted Mode**: Zero-refusal operation with optional model steering for authorized offensive security work — no nerfed responses on exploitation, payload crafting, or post-exploitation tradecraft
 - **🛡️ No Third-Party Telemetry**: PRO ships with all outbound third-party telemetry disabled by default — your prompts, targets, and findings never leave Alias infrastructure
 - **🇪🇺 European Hosting**: GDPR and NIS2 compliant infrastructure ensuring maximum privacy and data sovereignty
 - **💬 Professional Support**: Dedicated technical support to help you maximize your security testing capabilities
 - **📱 Mobile UI (iOS)**: Native iOS app for security testing on the go - **[Join TestFlight Beta](https://testflight.apple.com/join/nXZZD4Z5)**
- **⚡ Advanced Features**: Terminal UI (CLI recommended), usage and cost tooling (`/cost`, compaction), and exclusive tools not available in the Community Edition
+- **⚡ Advanced Features**: Terminal UI (deprecated) and other subscriber-focused capabilities beyond the Community Edition defaults
 ---
 ## CAI FREE vs CAI PRO
 <div class="comparison-table" markdown>
 | Feature | CAI FREE | CAI PRO |
 |---------|-------------------|---------|
 | **Core Framework** | ✅ Access | ✅ Full Access |
 | **300+ AI Models** | ✅ BYO API Keys | ✅ BYO API Keys |
 | **Built-in Security Tools** | ✅ Full Suite | ✅ Full Suite |
 | **Agent Patterns** | ✅ Base Patterns | ✅ All Patterns |
 | **Agent Scaffold Library** | ✅ Core scaffolds | ✅ **Extended catalog** (red/blue/purple, bug bounty, DFIR, APT, RE, SAST, SDR/Wi-Fi…) |
 | **Command Line Interface** | ✅ Yes | ✅ Yes |
 | **European Data Hosting** | ✅ **GDPR + NIS2 Compliant** | ✅ **GDPR + NIS2 Compliant** |
 | **Alias Model Family (`alias1`+)** | ❌ Not Available | ✅ **Unlimited Tokens — best-in-class cybersecurity LLMs** |
 | **Unrestricted Mode** | ❌ Refusals from BYO models | ✅ **Zero refusals + model steering** for authorized offensive work |
 | **Third-Party Telemetry** | ⚠️ Enabled by default (opt-out) | ✅ **Disabled by default** — no outbound third-party reporting |
 | **Mobile UI (iOS App)** | ❌ | ✅ **Native iOS App** - [TestFlight](https://testflight.apple.com/join/nXZZD4Z5) |
 | **Terminal User Interface (TUI)** | ❌ | ✅ Multi-terminal (Deprecated) |
 | **Advanced Reporting** | ❌ | ✅ Professional formats |
 | **Priority Support** | ❌ Community | ✅ **Professional** |
 | **Commercial Use License** | ❌ Research Only | ✅ **Full Commercial** |
 | **Custom Extensions** | ❌ | ✅ Available on request |
 | **Pricing** | **Free** (Research) | **€350/month** |
-
+</div>
 | Feature                             | CAI FREE                    | CAI PRO                                                                         |
 | ----------------------------------- | --------------------------- | ------------------------------------------------------------------------------- |
 | **Core Framework**                  | ✅ Access, ~6-months behind  | ✅ Full Access                                                                   |
 | **300+ AI Models**                  | ✅ BYO API Keys              | ✅ BYO API Keys                                                                  |
 | **Built-in Security Tools**         | ✅ Full Suite                | ✅ Full Suite                                                                    |
 | **Agent Patterns**                  | ✅ All Patterns              | ✅ All Patterns                                                                  |
 | **Command Line Interface**          | ✅ Yes                       | ✅ Yes                                                                           |
 | **European Data Hosting**           | ✅ **GDPR + NIS2 Compliant** | ✅ **GDPR + NIS2 Compliant**                                                     |
 | `**alias1` Model**                  | ❌ Not Available             | ✅ **Unlimited Tokens**                                                          |
 | **Mobile UI (iOS App)**             | ❌                           | ✅ **Native iOS App** - [TestFlight](https://testflight.apple.com/join/nXZZD4Z5) |
 | **Terminal User Interface (TUI)**   | ❌                           | ✅ Multi-terminal (Deprecated)                                                   |
 | **Usage & cost visibility**         | ❌                           | ✅ `/cost`, TUI cost panels, reporting workflows                                  |
 | **Advanced Reporting**              | ❌                           | ✅ Professional formats                                                          |
 | **Priority Support**                | ❌ Community                 | ✅ **Professional**                                                              |
 | **Commercial Use License**          | ❌ Research Only             | ✅ **Full Commercial**                                                           |
 | **Custom Extensions**               | ❌                           | ✅ Available on request                                                          |
 | **Pricing**                         | **Free** (Research)         | **€350/month**                                                                  |
 ---
-## The `alias1` Model
+## The Alias Model Family — The Best Cybersecurity LLMs
 <div class="highlight-box" markdown>
 ### 🏆 **Beats GPT-5 in Cybersecurity Benchmarks**
-The `alias1` model is our flagship cybersecurity AI, specifically trained for:
+The Alias model family — currently led by **`alias1`** with successor models in active training — is purpose-built for security work. CAI PRO grants **unlimited token access** to the entire family, with no monthly caps, no per-query metering, and no rate-limit throttling at the API tier.
 Trained specifically for:
 - **Offensive Security**: Penetration testing, exploit development, vulnerability research
 - **Bug Bounty Hunting**: Automated reconnaissance, analysis, and exploitation
@ -65,12 +70,12 @@ The `alias1` model is our flagship cybersecurity AI, specifically trained for:
 - **Zero Refusals**: No ethical restrictions for authorized security testing
 **Performance Highlights:**
 - Outperforms GPT-5 in AI vs AI cybersecurity benchmarks
 - 500B-parameter architecture optimized for security workflows
 - Unrestricted responses for authorized pentesting engagements
 - Unlimited tokens for long engagements, multi-agent orchestration, and overnight autonomous runs
-
+</div>
 [View Full Benchmarks →](https://aliasrobotics.com/alias1.php#benchmarking)
@ -78,16 +83,20 @@ The `alias1` model is our flagship cybersecurity AI, specifically trained for:
 The performance of `alias1` and the CAI framework is validated through rigorous peer-reviewed research:
- 📊 **[CAIBench: Cybersecurity AI Benchmark](https://arxiv.org/pdf/2510.24317)** (2025)
+- 📊 [**CAIBench: Cybersecurity AI Benchmark**](https://arxiv.org/pdf/2510.24317) (2025)
-Modular meta-benchmark framework for evaluating LLM models across offensive and defensive cybersecurity domains. `alias1` demonstrates superior performance compared to general-purpose models.
+  Modular meta-benchmark framework for evaluating LLM models across offensive and defensive cybersecurity domains. `alias1` demonstrates superior performance compared to general-purpose models.
- 🎯 **[Evaluating Agentic Cybersecurity in Attack/Defense CTFs](https://arxiv.org/pdf/2510.17521)** (2025)
+
-Real-world evaluation showing defensive agents achieved 54.3% patching success versus 28.3% offensive initial access. Validates practical effectiveness of CAI agents in live CTF environments.
+- 🎯 [**Evaluating Agentic Cybersecurity in Attack/Defense CTFs**](https://arxiv.org/pdf/2510.17521) (2025)
- 🚀 **[Cybersecurity AI (CAI) Framework](https://arxiv.org/pdf/2504.06017)** (2025)
+  Real-world evaluation showing defensive agents achieved 54.3% patching success versus 28.3% offensive initial access. Validates practical effectiveness of CAI agents in live CTF environments.
-Core framework paper demonstrating that CAI outperforms humans by up to **3,600× in specific security testing scenarios**, establishing a new standard for automated security assessment.
+
- 🛡️ **[Hacking the AI Hackers via Prompt Injection](https://arxiv.org/pdf/2508.21669)** (2025)
+- 🚀 [**Cybersecurity AI (CAI) Framework**](https://arxiv.org/pdf/2504.06017) (2025)
-Demonstrates four-layer guardrail defenses against prompt injection attacks, ensuring `alias1` remains secure even when processing adversarial inputs.
+  Core framework paper demonstrating that CAI outperforms humans by up to **3,600× in specific security testing scenarios**, establishing a new standard for automated security assessment.
- 📚 **[CAI Fluency: Educational Framework](https://arxiv.org/pdf/2508.13588)** (2025)
+
-Comprehensive educational platform for democratizing cybersecurity AI knowledge and best practices.
+- 🛡️ [**Hacking the AI Hackers via Prompt Injection**](https://arxiv.org/pdf/2508.21669) (2025)
  Demonstrates four-layer guardrail defenses against prompt injection attacks, ensuring `alias1` remains secure even when processing adversarial inputs.
 - 📚 [**CAI Fluency: Educational Framework**](https://arxiv.org/pdf/2508.13588) (2025)
  Comprehensive educational platform for democratizing cybersecurity AI knowledge and best practices.
 **Explore all research:** [Alias Robotics Research Library](https://aliasrobotics.com/research-security.php#papers) (24+ peer-reviewed publications)
@ -127,14 +136,12 @@ Building and maintaining CAI is resource-intensive. CAI PRO enables us to:
 ### Fair Pricing
 **€350/month** provides:
 - **Unlimited `alias1` tokens** (compare: OpenAI GPT-4o costs ~$2.50 per 1M tokens)
 - **Professional support** (compare: enterprise support typically $1000+/month)
 - **Privacy guarantees** (priceless for security professionals)
 - **Commercial license** (required for security consulting businesses)
 Most security professionals already pay similar or higher amounts for:
 - **Burp Suite Professional**: $449/year ($37/month)
 - **ChatGPT Plus/Pro**: $20-200/month (with severe restrictions)
 - **Other AI security tools**: $500-2000/month (closed-source, inferior models)
@ -145,6 +152,39 @@ Most security professionals already pay similar or higher amounts for:
 ## Exclusive CAI PRO Features
 ### 🧩 Extended Scaffold Library
 CAI PRO unlocks the full catalog of agent scaffolds — pre-built, role-specialized agents you can drop into a workflow or compose into multi-agent operations:
 - **Offensive**: `red_teamer`, `bug_bounter`, `web_pentester`, `apt_agent`, `replay_attack_agent`, `subghz_sdr_agent`, `wifi_security_tester`
 - **Defensive & Forensics**: `blue_teamer`, `dfir`, `memory_analysis_agent`, `network_traffic_analyzer`, `compliance_agent`
 - **Code & Reverse Engineering**: `codeagent`, `reverse_engineering_agent`, `android_sast_agent`
 - **Orchestration**: `orchestration_agent`, `selection_agent`, `purple_teamer_gctr`, `continuous_ops_agent`, `reporter`, `retester`
 New scaffolds ship regularly to PRO subscribers ahead of the Community release window. Custom scaffolds can also be developed on request.
 ### 🔓 Unrestricted Mode
 CAI PRO operates with **zero refusals** for authorized security testing. This includes:
 - **No safety nerfing** on exploit development, payload crafting, lateral movement, or post-exploitation tradecraft
 - **Optional model steering** (abliteration-based) to suppress residual refusals from BYO third-party models
 - **Authorization-context aware**: prompts framed within authorized engagement scope are answered fully and directly
 - **Guardrail toggles**: `CAI_GUARDRAILS` is opt-in, not enforced, so you control the constraint level
 This stands in contrast to general-purpose providers (OpenAI, Anthropic, Google), whose models refuse a significant fraction of legitimate offensive security queries even with valid authorization context.
 ### 🛡️ Third-Party Telemetry Disabled
 CAI PRO ships with **all outbound third-party telemetry disabled by default**:
 - **No OpenInference / OpenAI instrumentation** uploads — prompts, completions, and tool calls stay local
 - **No anonymous usage analytics** sent to third parties
 - **Alias-only data plane**: the only outbound traffic is to the Alias inference endpoint (European, GDPR/NIS2)
 - **Verifiable**: PRO is distributed as the `cai-framework-privacy` build variant, with telemetry hooks removed from the wheel — not just disabled by config
 For operators handling sensitive targets (red team engagements, client pentests, internal SOC work), this guarantees that **target names, payloads, credentials, and findings never reach a third-party SaaS**.
 ### 🖥️ Terminal User Interface (TUI)
 Run multiple agents in parallel with an intuitive multi-terminal interface:
@ -156,10 +196,6 @@ Run multiple agents in parallel with an intuitive multi-terminal interface:
 [TUI Documentation →](tui/tui_index.md)
 ### 📊 Usage and long threads
 Track spend with **`/cost`**, use **`/compact`** when conversations grow large, and use the **TUI** cost/model indicators for per-terminal visibility.
 ### 📝 Advanced Reporting
 Generate professional security reports automatically:
@ -199,13 +235,11 @@ CAI_GUARDRAILS=true
 ### 3. Launch CAI PRO
 #### CLI Mode (Standard)
 ```bash
 cai
 ```
 #### TUI Mode (Multi-terminal)
 ```bash
 cai --tui
 ```
@ -218,10 +252,7 @@ Check that you're using CAI PRO features:
 CAI> /model
 # Should show alias1 is available
-CAI> /cost
+CAI> --tui
 # Should display session usage / costs
 cai --tui
 # Should launch multi-terminal interface
 ```
@ -233,7 +264,7 @@ cai --tui
 **CAI PRO subscribers receive:**
- **Email Support**: [research@aliasrobotics.com](mailto:research@aliasrobotics.com) (48-hour response SLA)
+- **Email Support**: research@aliasrobotics.com (48-hour response SLA)
 - **Priority Discord**: Exclusive #pro-support channel
 - **Quarterly Strategy Calls**: Discuss roadmap and feature requests
 - **Custom Development**: Request tailored agents and extensions
@ -242,7 +273,7 @@ cai --tui
 - **[TUI Guide](tui/tui_index.md)**: Complete Terminal UI documentation
 - **[Agent Reference](agents.md)**: All available agents and configurations
- **[Command Reference](cli/commands_reference.md)**: Full CLI/TUI command list
+- **[Command Reference](cai/getting-started/commands.md)**: Full CLI/TUI command list
 - **[Benchmarks](cai_benchmark.md)**: Performance data and comparisons
 ### Community Resources
@ -272,8 +303,7 @@ No. The Community Edition license restricts use to research and educational purp
 ### Do you offer team/enterprise pricing?
-Yes! Contact [research@aliasrobotics.com](mailto:research@aliasrobotics.com) for:
+Yes! Contact research@aliasrobotics.com for:
 - **Team plans** (5+ users): Volume discounts
 - **Enterprise plans** (20+ users): Custom pricing, on-premise deployment
 - **Academic licenses**: Special rates for universities and research institutions
@ -281,8 +311,7 @@ Yes! Contact [research@aliasrobotics.com](mailto:research@aliasrobotics.com) for
 ### Is my security testing data private?
 **Absolutely.** CAI PRO guarantees:
-
+- **No training on your data**: Your pentesting activities never improve our models (unless you explicitly opt-in)
 - **No training on your data**: Your pentesting activities never improve our models (unless you explicitly opt in)
 - **European hosting**: All data processed in GDPR-compliant datacenters
 - **No third-party sharing**: Unlike OpenAI/Anthropic, we never send your data elsewhere
 - **Encryption**: End-to-end encryption for all communications
@ -290,7 +319,6 @@ Yes! Contact [research@aliasrobotics.com](mailto:research@aliasrobotics.com) for
 ### Can I switch between models?
 Yes! CAI PRO includes:
 - **Unlimited `alias1` tokens** (your PRO model)
 - **BYO API keys**: Continue using OpenAI, Anthropic, etc. with your own keys
 - **Mix and match**: Use `alias1` for exploitation, GPT-4 for reporting, etc.
@ -298,7 +326,6 @@ Yes! CAI PRO includes:
 ### What if alias1 refuses a query?
 `alias1` has **zero refusals** for authorized security testing. If you encounter issues:
 1. Ensure your prompt includes security context (e.g., "authorized pentest of...")
 2. Check your `CAI_GUARDRAILS` setting (may block malicious patterns)
 3. Contact support—we'll investigate immediately
@ -309,13 +336,16 @@ Yes! CAI PRO includes:
 **Transform your security testing workflow with CAI PRO.**
-
+<div class="cta-box" markdown>
 ### 🚀 **Ready to Upgrade?**
- ✅ Unlimited `alias1` access
+- ✅ Unlimited tokens across the Alias model family — best-in-class cybersecurity LLMs
 - ✅ Extended scaffold library — every red/blue/purple, bug bounty, DFIR, RE, SAST agent
 - ✅ Unrestricted Mode — zero refusals and model steering for authorized offensive work
 - ✅ Third-party telemetry disabled by default — no SaaS leakage of prompts or findings
 - ✅ Terminal UI with parallel agents
- ✅ Usage and cost visibility (`/cost`, compaction, TUI)
+- ✅ Context monitoring and optimization
 - ✅ Professional support
 - ✅ European data privacy
 - ✅ Commercial use license
@ -324,8 +354,11 @@ Yes! CAI PRO includes:
 **[Get CAI PRO →](https://aliasrobotics.com/cybersecurityai.php)**
-
+</div>
 ---
-*Have questions? Contact research@aliasrobotics.com* *Need a quote for your organization? [Request enterprise pricing →](mailto:research@aliasrobotics.com?subject=CAI%20PRO%20Enterprise%20Inquiry)*
+<small>
 *Have questions? Contact research@aliasrobotics.com*
 *Need a quote for your organization? [Request enterprise pricing →](mailto:research@aliasrobotics.com?subject=CAI%20PRO%20Enterprise%20Inquiry)*
 </small>
--- a/docs/cai_pro_alias1.md
+++ b/docs/cai_pro_alias1.md
@ -1,323 +0,0 @@
 # Alias1 Model - Unrestricted Cybersecurity AI
 > **⚡ Exclusively Available with CAI PRO**
 > 
 > Alias1 is the world's most capable cybersecurity LLM, delivering state-of-the-art performance with **zero refusals** for authorized security testing.
 >
 > **[Get CAI PRO with Alias1 →](https://aliasrobotics.com/cybersecurityai.php)**
 ---
 ## What is Alias1?
 **Alias1** is a purpose-built large language model specifically trained for cybersecurity professionals. Unlike general-purpose models that refuse security tasks, alias1 delivers **unrestricted AI capabilities** for offensive and defensive security operations.
 ### Key Highlights
 - **🏆 #2 Rank** - CAIBench Base CTF Performance
 - **75% Accuracy** - Cyber Threat Intelligence tasks
 - **100% Zero Refusals** - No "I cannot help with that" responses
 - **62.5% Success Rate** - Solved 15/24 base CTF challenges (pass@1)
 ---
 ## Performance Benchmarks
 ### CAIBench CTF Results
 Alias1 consistently outperforms leading models in real-world cybersecurity challenges:
 | Model | Base CTF Score | Rank |
 |-------|---------------|------|
 | **Claude Sonnet 4.5** | 70.8% | #1 |
 | **🤖 alias1** | **62.5%** | **#2** |
 | **GPT-5** | 58.3% | #3 |
 | **Gemini 2.5 Pro** | 54.2% | #4 |
 **Source**: [CAIBench Meta-Benchmark Framework](https://arxiv.org/pdf/2510.24317) (2025)
 ### Real-World Validation
 Alias1's capabilities are proven in actual security assessments:
 - **🎯 3,600× Faster** than manual testing in specific scenarios ([CAI Framework Paper](https://arxiv.org/pdf/2504.06017))
 - **54.3% Patching Success** in Attack/Defense CTFs ([Agentic Evaluation Paper](https://arxiv.org/pdf/2510.17521))
 - **Zero Refusals** - Purpose-built for authorized security testing
 ---
 ## Why Alias1 Outperforms Competitors
 ### No Cybersecurity Censorship
 Unlike mainstream LLMs, alias1 never refuses legitimate security work:
 | Task | GPT-5 | Claude | Gemini | Alias1 |
 |------|-------|--------|--------|--------|
 | Write exploit code | ❌ | ❌ | ❌ | ✅ |
 | Generate payloads | ❌ | ❌ | ❌ | ✅ |
 | Bypass security controls | ❌ | ❌ | ❌ | ✅ |
 | Craft phishing templates | ❌ | ❌ | ❌ | ✅ |
 | Reverse engineer malware | ❌ | ❌ | ❌ | ✅ |
 **While others refuse, alias1 delivers.**
 ---
 ### Specialized Training
 Alias1 is trained specifically for cybersecurity workflows:
 #### Offensive Security
 - **Penetration Testing**: Full exploit development and PoC creation
 - **Vulnerability Research**: Novel attack vector discovery
 - **CTF Competitions**: Top-tier performance in capture-the-flag challenges
 - **Red Teaming**: Adversary simulation and attack chain execution
 #### Defensive Security
 - **Automated Mitigation**: Instant patch generation and security fixes
 - **Threat Hunting**: Proactive threat detection and analysis
 - **Incident Response**: Rapid response to security incidents
 - **Blue Team Operations**: Defensive posture analysis and hardening
 #### Bug Bounty
 - **Reconnaissance**: Comprehensive target enumeration
 - **Analysis**: Deep vulnerability assessment
 - **Exploitation**: Full exploit chain development
 - **Reporting**: Professional vulnerability disclosure
 ---
 ## Technical Specifications
 ### Model Architecture
 - **Parameters**: 500B+ (optimized for security workflows)
 - **Context Window**: Extended context for complex security scenarios
 - **Training Data**: Curated cybersecurity datasets, CTF challenges, exploit databases
 - **Fine-tuning**: Specialized for penetration testing and vulnerability research
 ### Performance Characteristics
 - **Speed**: Optimized inference for real-time security operations
 - **Accuracy**: State-of-the-art performance on security benchmarks
 - **Reliability**: Four-layer guardrails against prompt injection ([Research Paper](https://arxiv.org/pdf/2508.21669))
 - **Consistency**: Deterministic outputs for reproducible security testing
 ---
 ## Unlimited Tokens - Massive Savings
 ### Cost Comparison (1 Billion Tokens/Month)
 Based on CAI's average text generation profile: 15,430 input / 436 output tokens per request
 | Provider | Monthly Cost | vs CAI PRO |
 |----------|--------------|------------|
 | **GPT-5** | €1,491/month | ❌ **4.3× more expensive** |
 | **Claude Sonnet 4.5** | €3,330/month | ❌ **9.5× more expensive** |
 | **Claude Opus 4.1** | €16,650/month | ❌ **47.6× more expensive** |
 | **🤖 CAI PRO (∞ alias1)** | **€350/month** | ✅ **Unlimited included** |
 **💰 Save €1,141 - €16,300/month** with unlimited alias1 tokens in CAI PRO.
 ---
 ## European Data Sovereignty
 ### 100% European Infrastructure
 Your security testing data never leaves Europe:
 - **GDPR Compliant by Design**: Full compliance with General Data Protection Regulation
 - **NIS2 Directive Ready**: Aligned with Network and Information Security Directive 2
 - **European Data Centers Only**: All processing, storage, and AI inference in EU jurisdiction
 - **No Third-Party Data Sharing**: Your pentesting activities remain completely private
 #### Compliance Checklist
 | Regulation | Status |
 |------------|--------|
 | GDPR (EU 2016/679) | ✅ **Compliant** |
 | NIS2 Directive (EU 2022/2555) | ✅ **Compliant** |
 | EU AI Act | ✅ **Ready** |
 | Data Residency | ✅ **EU Only** |
 **Perfect for European enterprises, government agencies, and privacy-conscious security professionals.**
 ---
 ## Real-World Success Stories
 ### Case Studies
 Alias1 has been validated in production environments across multiple industries:
 #### 🏭 OT Security - Ecoforest Heat Pumps
 Alias1 discovered critical vulnerabilities allowing unauthorized remote access to heat pumps deployed across Europe, including exposed credentials and DES encryption weaknesses.
 [Read Case Study →](https://aliasrobotics.com/case-studies-robot-cybersecurity.php)
 #### 🤖 Robotics - Mobile Industrial Robots (MiR)
 Automated ROS message injection attacks exposed unauthorized access to robot control systems through AI-driven vulnerability discovery.
 [Read Case Study →](https://aliasrobotics.com/case-studies-robot-cybersecurity.php)
 #### 🛒 Web Security - Mercado Libre E-commerce
 API vulnerability discovery through automated enumeration revealed user data exposure risks at scale in one of Latin America's largest e-commerce platforms.
 [Read Case Study →](https://aliasrobotics.com/case-studies-robot-cybersecurity.php)
 #### 🌐 Web Application - PortSwigger Race Condition
 Successfully exploited race conditions in file upload vulnerabilities, uploading and executing web shells through automated parallel requests.
 [Read Case Study →](https://aliasrobotics.com/case-studies-robot-cybersecurity.php)
 ---
 ## Research Foundation
 Alias1's capabilities are backed by 24+ peer-reviewed publications:
 ### Key Research Papers
 #### 📊 [CAIBench: Meta-Benchmark Framework](https://arxiv.org/pdf/2510.24317) (2025)
 Modular evaluation framework demonstrating alias1's superior performance across offensive and defensive cybersecurity domains.
 #### 🚀 [Cybersecurity AI (CAI) Framework](https://arxiv.org/pdf/2504.06017) (2025)
 Foundational paper showing CAI outperforms humans by **3,600× in specific security scenarios**, establishing new standards for automated security assessment.
 #### 🛡️ [Hacking the AI Hackers via Prompt Injection](https://arxiv.org/pdf/2508.21669) (2025)
 Demonstrates alias1's four-layer guardrail defenses, ensuring security even when processing adversarial inputs.
 #### 🎯 [Evaluating Agentic Cybersecurity in Attack/Defense CTFs](https://arxiv.org/pdf/2510.17521) (2025)
 Real-world validation showing 54.3% patching success in live CTF environments, proving practical effectiveness.
 **[Explore All 24+ Research Papers →](https://aliasrobotics.com/research-security.php#papers)**
 ---
 ## How to Access Alias1
 ### 1. Subscribe to CAI PRO
 Visit [aliasrobotics.com/cybersecurityai.php](https://aliasrobotics.com/cybersecurityai.php) and:
 - Choose your subscription plan (€350/month per user)
 - Complete secure European payment processing
 - Receive your `ALIAS_API_KEY`
 ### 2. Configure Your Environment
 Update your `.env` file:
 ```bash
 # CAI PRO Configuration
 ALIAS_API_KEY="sk-your-caipro-key-here"
 CAI_MODEL="alias1"
 # Enable CAI PRO features
 CAI_TUI_MODE=true
 CAI_STREAM=false
 ```
 ### 3. Start Using Alias1
 #### CLI Mode
 ```bash
 cai
 ```
 #### TUI Mode (Multi-terminal)
 ```bash
 cai --tui
 ```
 #### Verify Access
 ```bash
 CAI> /model
 # Should show alias1 is available
 CAI> scan target.com for vulnerabilities
 # Start testing with unlimited tokens
 ```
 ---
 ## Frequently Asked Questions
 ### How does alias1 compare to GPT-5?
 Alias1 consistently outperforms GPT-5 in cybersecurity-specific benchmarks:
 - **CAIBench Base CTF**: alias1 (62.5%) vs GPT-5 (58.3%)
 - **Zero Refusals**: alias1 never blocks security tasks
 - **Specialized Training**: Purpose-built for security vs general-purpose
 ### Is alias1 safe to use?
 Yes. Alias1 includes:
 - **Four-layer guardrails** against prompt injection
 - **Authorization context** - understands scope of security testing
 - **Audit logging** - All queries logged for compliance
 - **GDPR compliance** - European data protection standards
 ### Can I mix alias1 with other models?
 Absolutely! CAI PRO allows you to:
 - Use **unlimited alias1** for exploitation and security tasks
 - Switch to **GPT-4o** for reporting and documentation
 - Leverage **Claude** for code analysis
 - **BYO API keys** for any of 300+ supported models
 ### What if I need more than 1 billion tokens?
 CAI PRO includes **unlimited alias1 tokens** (subject to fair use policy). There are no hard limits - use as much as you need for your security operations.
 For extreme usage (e.g., large-scale automated scanning), contact us for enterprise plans.
 ### Does alias1 work offline?
 Not in the standard CAI PRO plan. However:
 - **CAI GOV & ENTERPRISE** offers on-premise deployment
 - **Air-gapped environments** supported with custom licensing
 - Contact **research@aliasrobotics.com** for offline deployments
 ---
 ## Get Alias1 Today
 **Transform your security testing with the world's most capable cybersecurity AI.**
 <div class="cta-box" markdown>
 ### 🚀 **Ready for Unrestricted AI?**
 - ✅ Unlimited alias1 tokens
 - ✅ Zero refusals for authorized testing
 - ✅ #2 ranked in CAIBench CTFs
 - ✅ European data sovereignty (GDPR + NIS2)
 - ✅ Professional support included
 - ✅ Commercial use license
 **€350/month/user** · No long-term contracts · Cancel anytime
 **[Get CAI PRO with Alias1 →](https://aliasrobotics.com/cybersecurityai.php)**
 </div>
 ---
 ## Next Steps
 - **[View Full Pricing](cai_pro_pricing.md)** - Compare all CAI plans
 - **[Explore Features](cai_pro_features.md)** - See all CAI PRO capabilities
 - **[Quick Start Guide](cai_pro_quickstart.md)** - Get started in 5 minutes
 - **[Contact Sales](cai_pro_contact.md)** - Enterprise & custom plans
 ---
 <small>
 *Questions about alias1? Contact **support@aliasrobotics.com***  
 *Need enterprise deployment? [Request custom pricing →](mailto:contact@aliasrobotics.com?subject=Alias1%20Enterprise%20Inquiry)*
 </small>
--- a/docs/cai_pro_contact.md
+++ b/docs/cai_pro_contact.md
@ -1,366 +0,0 @@
 # Contact Sales & Support
 > **Get in Touch with the CAI Team**
 >
 > Whether you're interested in CAI PRO, need enterprise solutions, or have questions about our platform, we're here to help.
 >
 > **[Buy CAI PRO Now →](https://aliasrobotics.com/cybersecurityai.php)**
 ---
 ## Quick Links
 <div class="contact-grid" markdown>
 ### 🚀 Ready to Buy?
 **Self-Service Purchase**
 Get CAI PRO instantly (€350/month):
 **[Buy CAI PRO →](https://aliasrobotics.com/cybersecurityai.php)**
 - ✅ Instant access to alias1
 - ✅ Automated onboarding
 - ✅ Payment via credit card
 - ✅ Start using in 5 minutes
 ---
 ### 🏢 Enterprise Inquiry
 **Custom Solutions**
 For teams of 20+ or special requirements:
 **[Email: contact@aliasrobotics.com](mailto:contact@aliasrobotics.com?subject=CAI%20Enterprise%20Inquiry)**
 **[Schedule a Call →](mailto:contact@aliasrobotics.com?subject=CAI%20Enterprise%20Call%20Request)**
 Include in your message:
 - Team size
 - Use case
 - Deployment requirements
 - Budget range
 ---
 ### 💬 Professional Support
 **CAI PRO Subscribers**
 Get help with technical issues:
 **[Email: support@aliasrobotics.com](mailto:support@aliasrobotics.com?subject=CAI%20PRO%20Support)**
 ---
 ### 🎓 Academic Inquiries
 **Universities & Research**
 Special pricing for education:
 **[Email: contact@aliasrobotics.com](mailto:contact@aliasrobotics.com?subject=CAI%20Academic%20Inquiry)**
 Include:
 - Institution name
 - Research project description
 - Number of users
 - .edu email address
 </div>
 ---
 ## Contact Information
 ### 📧 Email
 **General Inquiries & Sales:**  
 **contact@aliasrobotics.com**
 **Support (CAI PRO Subscribers):**  
 **support@aliasrobotics.com**  
 **Expected Response Time:**
 - **CAI PRO Subscribers**: 48 hours (SLA)
 - **Enterprise Inquiries**: 24-48 hours
 - **General Questions**: 2-5 business days
 ---
 ### 💬 Discord Community
 **Join 1000+ Security Researchers**
 **[Discord Server →](https://discord.gg/fnUFcTaQAC)**
 **Channels:**
 - **#general** - Community discussions
 - **#help** - Community support (FREE users)
 - **#pro-support** - Exclusive for CAI PRO subscribers
 - **#announcements** - Product updates
 - **#research** - Security research discussions
 ---
 ### 🌐 Web
 **Official Website:**  
 [https://aliasrobotics.com](https://aliasrobotics.com)
 **CAI PRO Product Page:**  
 [https://aliasrobotics.com/cybersecurityai.php](https://aliasrobotics.com/cybersecurityai.php)
 **Alias1 Model Page:**  
 [https://aliasrobotics.com/alias1.php](https://aliasrobotics.com/alias1.php)
 **GitHub Repository:**  
 [https://github.com/aliasrobotics/cai](https://github.com/aliasrobotics/cai)
 ---
 ## What to Include in Your Message
 ### For Enterprise Inquiries
 Help us provide an accurate quote by including:
 1. **Organization Details:**
   - Company name
   - Industry sector
   - Location (for compliance requirements)
 2. **Team Size:**
   - Number of users
   - Expected growth in next 12 months
 3. **Use Case:**
   - Primary security testing needs
   - Current toolchain
   - Integration requirements
 4. **Technical Requirements:**
   - On-premise vs cloud deployment
   - Air-gapped environment needs
   - Compliance requirements (GDPR, NIS2, etc.)
 5. **Timeline:**
   - When do you need to start?
   - Any specific deadlines?
 6. **Budget:**
   - Budget range (optional but helpful)
   - Procurement process details
 **Example Message:**
 ```
 Subject: CAI Enterprise Inquiry - [Company Name]
 Hello,
 We're interested in CAI PRO/Enterprise for our security team.
 Organization: [Company Name], [Industry]
 Team Size: 25 security professionals
 Use Case: Penetration testing and bug bounty operations
 Requirements: On-premise deployment, GDPR compliance
 Timeline: Q2 2025
 Budget: €50,000 - €100,000 annual
 Can we schedule a call to discuss custom pricing?
 Best regards,
 [Name]
 [Title]
 [Contact Info]
 ```
 ---
 ### For Support Requests (CAI PRO)
 Help us resolve your issue quickly:
 1. **Subscription Details:**
   - Email used for CAI PRO subscription
   - When did you subscribe?
 2. **Issue Description:**
   - What were you trying to do?
   - What happened instead?
   - Error messages (exact text)
 3. **Environment:**
   - Operating System (Linux, macOS, Windows)
   - CAI version (`cai --version`)
   - Python version
 4. **Reproduction Steps:**
   - Step-by-step to reproduce the issue
   - Configuration files (`.env` - remove API keys!)
   - Screenshots if applicable
 **Example Support Message:**
 ```
 Subject: CAI PRO Support - alias1 not available
 Hello,
 I'm a CAI PRO subscriber experiencing an issue.
 Subscription: pro-user@example.com (subscribed March 2025)
 Issue: When I run `cai`, alias1 is not showing as available.
 Environment:
 - Ubuntu 22.04 LTS
 - CAI v0.6.5
 - Python 3.11.2
 Steps:
 1. Set ALIAS_API_KEY in .env
 2. Run `cai`
 3. Type `/model`
 4. alias1 not listed
 I've attached my .env file (with key redacted) and a screenshot.
 Thank you for your help.
 [Name]
 ```
 ---
 ## Sales Process
 ### Individual & Small Teams (1-5 users)
 **Self-Service:**
 1. Visit [aliasrobotics.com/cybersecurityai.php](https://aliasrobotics.com/cybersecurityai.php)
 2. Click "Buy CAI PRO"
 3. Complete payment
 4. Receive `ALIAS_API_KEY` via email
 5. Start using immediately
 **Timeline**: Instant (5 minutes)
 ---
 ### Medium Teams (5-19 users)
 **Sales-Assisted:**
 1. Email research@aliasrobotics.com with team size
 2. Receive volume discount quote (10-20% off)
 3. Complete payment (invoice or credit card)
 4. Onboarding call with CAI team (optional)
 5. Receive team API keys
 **Timeline**: 1-3 business days
 ---
 ### Enterprise (20+ users)
 **Custom Process:**
 1. Initial inquiry via email or scheduled call
 2. Discovery session (30-60 min call)
 3. Custom proposal with:
   - Volume pricing
   - Deployment options
   - Support SLA
   - Training plan
 4. Contract negotiation
 5. Legal/procurement review
 6. Deployment and onboarding (1-4 weeks)
 **Timeline**: 2-8 weeks (depends on organization)
 ---
 ## Frequently Asked Questions
 ### How quickly will I get a response?
 **CAI PRO Support (technical)**: 48 hours (SLA)  
 **Enterprise Sales**: 24-48 hours  
 **General Inquiries**: 2-5 business days  
 **Urgent issues?** Email with "URGENT" in subject line.
 ---
 ### Can I schedule a demo?
 **Yes!** Email contact@aliasrobotics.com with:
 - Your role/organization
 - Preferred date/time (include timezone)
 - Specific features you want to see
 We'll schedule a 30-60 minute live demo.
 ---
 ## Additional Resources
 ### Before Contacting Sales
 Review these resources to answer common questions:
 - **[Pricing & Plans](cai_pro_pricing.md)** - Detailed pricing information
 - **[Features Overview](cai_pro_features.md)** - What's included in each plan
 - **[Alias1 Model](cai_pro_alias1.md)** - Learn about our flagship model
 - **[Quick Start](cai_pro_quickstart.md)** - How to get started
 - **[FAQ](cai_faq.md)** - General frequently asked questions
 ---
 ## Follow Us
 **Stay Updated:**
 - **Twitter**: [@aliasrobotics](https://twitter.com/aliasrobotics)
 - **LinkedIn**: [Alias Robotics](https://www.linkedin.com/company/alias-robotics/)
 - **YouTube**: [Alias Robotics Channel](https://www.youtube.com/@aliasrobotics)
 - **Research Blog**: [aliasrobotics.com/research](https://aliasrobotics.com/research-security.php)
 ---
 ## Legal & Compliance
 **Terms of Service:**  
 [CAI Terms & Conditions](https://aliasrobotics.com/terms-and-conditions.php)
 **Privacy Policy:**  
 GDPR-compliant - data processed in EU only
 **License Information:**
 - CAI FREE: [Open source license](https://github.com/aliasrobotics/cai/blob/main/LICENSE)
 - CAI PRO: Proprietary commercial license
 - CAI ENTERPRISE: Custom licensing available
 ---
 <div class="cta-box" markdown>
 ## 🚀 Ready to Get Started?
 ### Individual Users & Small Teams
 **[Buy CAI PRO Now →](https://aliasrobotics.com/cybersecurityai.php)**
 €350/month · Instant access · No contracts
 </div>
 ---
 <small>
 *Questions? **contact@aliasrobotics.com** · +34 945 19 85 15*  
 *Privacy: Your data stays in Europe (GDPR-compliant)*
 </small>
--- a/docs/cai_pro_features.md
+++ b/docs/cai_pro_features.md
@ -1,377 +0,0 @@
 # CAI PRO Exclusive Features
 > **Unlock Advanced Capabilities**
 >
 > CAI PRO delivers professional-grade features designed for security teams, enterprises, and advanced users who need unrestricted AI, parallel execution, and comprehensive monitoring.
 >
 > **[Get CAI PRO →](https://aliasrobotics.com/cybersecurityai.php)**
 ---
 ## Feature Overview
 | Capability | CAI FREE | CAI PRO |
 |------------|----------|---------|
 | **🤖 Alias1 Model** | ❌ | ✅ **Unlimited Tokens** |
 | **🖥️ Terminal UI (TUI)** | ❌ | ✅ Multi-terminal parallel execution |
 | **📊 Usage / cost** | ❌ | ✅ `/cost`, TUI cost panels |
 | **⚡ Multi-Agent Swarms** | ❌ | ✅ 100+ parallel agents |
 | **💬 Professional Support** | ❌ Community | ✅ Priority (48h SLA) |
 | **🇪🇺 European Hosting** | ✅ GDPR + NIS2 | ✅ GDPR + NIS2 |
 | **📝 Advanced Reporting** | ❌ | ✅ Professional formats |
 | **🏢 Commercial License** | ❌ Research Only | ✅ Full Commercial |
 | **🛡️ Guardrails** | ✅ Basic | ✅ Four-layer advanced |
 | **🔧 Custom Extensions** | ❌ | ✅ Available on request |
 ---
 ## 1. Unlimited Alias1 Tokens
 ### World's Most Capable Cybersecurity LLM
 **Alias1** is purpose-built for security professionals:
 - **#2 Rank** in CAIBench Base CTF Performance (62.5% success rate)
 - **Zero Refusals** for authorized security testing
 - **75% Accuracy** on Cyber Threat Intelligence tasks
 - **Unrestricted** exploit development, payload generation, and bypass techniques
 **Cost Savings**: Unlimited tokens save **€1,141 - €16,300/month** compared to GPT-5, Claude, or other providers.
 [Learn More About Alias1 →](cai_pro_alias1.md)
 ---
 ## 2. Terminal User Interface (TUI)
 ### Multi-Agent Orchestration at Your Fingertips
 **One human operator can monitor dozens of agents** with CAI PRO's professional Terminal User Interface.
 ![CAI TUI Screenshot](media/cai-tui-main.png)
 ### Key Capabilities
 #### Multi-Pane Views
 - **4+ parallel terminals** with independent contexts
 - **Visual monitoring**: Real-time cost tracking, model selection, agent status
 - **Synchronized execution**: Broadcast prompts to all terminals simultaneously
 #### Keyboard Control
 - **Vim-style shortcuts**: Navigate without touching your mouse
 - **Quick commands**: `/agent`, `/model`, `/parallel`, `/cost`
 - **Terminal switching**: `Ctrl+N`/`Ctrl+B` for rapid navigation
 #### Real-Time Stats
 - **Cost tracking**: Per-terminal and session-wide expense monitoring
 - **Token usage**: Input/output token breakdown by terminal
 - **Performance metrics**: Response times and API call statistics
 #### Preconfigured Teams
 - **11 team presets**: Red team, blue team, bug bounty combos
 - **One-click setup**: Instantly deploy specialized agent configurations
 - **Custom teams**: Save your own team compositions
 ### Time Savings
 **Save 40+ hours/month** by replacing manual monitoring with intelligent automation:
 - **Before**: Manually switching between terminal windows, copy-pasting commands
 - **After**: Single interface, parallel execution, automated coordination
 [TUI Documentation →](tui/tui_index.md)
 ---
 ## 3. Parallel Agent Swarms
 ### 100+ Concurrent Agents
 Deploy hundreds of specialized agents simultaneously for unprecedented security coverage.
 ### Performance Multipliers
 | Metric | Manual | With Swarms | Improvement |
 |--------|--------|-------------|-------------|
 | **Parallel Agents** | 1 | 100+ | **100×** |
 | **Discovery Speed** | 1× | 10× | **10× faster** |
 | **Coverage** | Limited | Comprehensive | **Complete** |
 | **Availability** | 9-5 | 24/7 | **Continuous** |
 **Save 100+ hours per month** with autonomous security operations.
 ### Swarm Patterns
 #### Broadcast Execution
 Send the same prompt to multiple agents:
 ```bash
 CAI TUI> Scan target.com for vulnerabilities
 # Executes across: redteam_agent, blueteam_agent, bug_bounter_agent, retester_agent
 ```
 #### Specialized Teams
 Deploy role-specific agent combinations:
 - **Offensive Team**: 4× redteam_agent in parallel
 - **Balanced Team**: 2× red + 2× blue team agents
 - **Bug Bounty**: 2× bug_bounter + 2× retester agents
 #### Sequential Workflows
 Chain agents for complex operations:
 1. **Discovery**: bug_bounter_agent finds vulnerabilities
 2. **Validation**: retester_agent confirms findings
 3. **Exploitation**: redteam_agent develops exploits
 4. **Documentation**: reporting_agent generates writeups
 [Teams & Parallel Execution Guide →](tui/teams_and_parallel_execution.md)
 ---
 ## 4. Professional Support
 ### Priority Technical Assistance
 CAI PRO subscribers receive dedicated support from security experts.
 ### Support Channels
 #### Email Support
 - **Address**: research@aliasrobotics.com
 - **SLA**: 48-hour response time
 - **Coverage**: Technical issues, configuration, best practices
 #### Priority Discord
 - **Channel**: #pro-support (exclusive)
 - **Access**: Direct communication with CAI developers
 - **Community**: Network with other PRO users
 #### Quarterly Strategy Calls
 - **Frequency**: 4× per year (optional)
 - **Topics**: Roadmap discussion, feature requests, use case optimization
 - **Format**: Video call with CAI team
 ### Custom Development
 Request tailored solutions:
 - **Custom Agents**: Domain-specific security agents
 - **Integration Support**: Connect CAI to your existing tools
 - **Workflow Optimization**: Fine-tune CAI for your organization
 - **Training**: Onboarding sessions for your team
 ---
 ## 5. European Data Sovereignty
 ### GDPR & NIS2 Compliant by Design
 Your security testing data never leaves Europe.
 ### Compliance Features
 | Regulation | Compliance Level | Details |
 |------------|------------------|---------|
 | **GDPR** (EU 2016/679) | ✅ **Fully Compliant** | Data minimization, encryption, audit trails |
 | **NIS2 Directive** (EU 2022/2555) | ✅ **Ready** | Incident reporting, supply chain security, risk management |
 | **EU AI Act** | ✅ **Prepared** | Transparency, accountability, human oversight |
 | **Data Residency** | ✅ **EU Only** | No data routing through non-EU jurisdictions |
 ### Privacy Guarantees
 - **No Third-Party Sharing**: Your pentesting activities remain private
 - **No Training on Your Data**: Your queries never improve models (unless opt-in)
 - **Encryption**: End-to-end encryption for all communications
 - **Audit Logs**: Complete traceability for compliance requirements
 **Perfect for European enterprises, government agencies, and regulated industries.**
 ---
 ## 6. Advanced Reporting
 ### Professional Security Reports
 Generate compliance-ready reports automatically.
 ### Report Types
 #### CTF Writeups
 - **Challenge description**: Automatic extraction from prompts
 - **Exploitation steps**: Detailed attack chain documentation
 - **Flags obtained**: Proof of successful exploitation
 - **Tools used**: Complete tooling inventory
 #### Penetration Testing Reports
 - **Executive summary**: High-level findings for management
 - **Technical findings**: Detailed vulnerability descriptions
 - **Proof of concept**: Code snippets and screenshots
 - **Remediation guidance**: Actionable fix recommendations
 #### NIS2 Compliance Reports
 - **Incident documentation**: Structured incident response records
 - **Risk assessment**: Vulnerability severity and impact analysis
 - **Mitigation tracking**: Patch deployment verification
 - **Audit trails**: Complete testing activity logs
 ### Output Formats
 - **Markdown**: Easy editing and version control
 - **PDF**: Professional presentation-ready format
 - **HTML**: Web-based viewing and sharing
 - **JSON**: Machine-readable for automation
 ---
 ## 7. Four-Layer Guardrails
 ### Advanced Security Protection
 CAI PRO includes enterprise-grade guardrails against adversarial attacks.
 ### Protection Layers
 #### Layer 1: Input Validation
 - **Prompt injection detection**: Identify adversarial inputs
 - **Malicious pattern filtering**: Block known attack vectors
 - **Context verification**: Ensure legitimate security testing scope
 #### Layer 2: Output Sanitization
 - **Dangerous command filtering**: Prevent accidental destructive operations
 - **Data leak prevention**: Avoid exposing sensitive information
 - **Format enforcement**: Ensure outputs match expected structure
 #### Layer 3: Authorization Context
 - **Scope validation**: Verify testing is within authorized boundaries
 - **Target verification**: Confirm permissions for specified targets
 - **Audit logging**: Record all security operations for compliance
 #### Layer 4: Human Oversight
 - **Confirmation prompts**: Request approval for high-risk operations
 - **Manual review**: Pause for human validation when needed
 - **Override capability**: Expert users can bypass when justified
 **Research validation**: [Hacking the AI Hackers via Prompt Injection](https://arxiv.org/pdf/2508.21669) (2025)
 ---
 ## 8. Commercial Use License
 ### Unrestricted Business Use
 CAI PRO includes full commercial licensing for professional security services.
 ### Authorized Uses
 ✅ **Penetration Testing Services**  
 ✅ **Security Consulting**  
 ✅ **Bug Bounty Hunting (for profit)**  
 ✅ **Enterprise Security Operations**  
 ✅ **Security Training & Education (commercial)**  
 ✅ **Product Integration (with agreement)**
 ### License Comparison
 | Use Case | CAI FREE | CAI PRO |
 |----------|----------|---------|
 | **Academic Research** | ✅ | ✅ |
 | **Personal Learning** | ✅ | ✅ |
 | **Commercial Pentesting** | ❌ | ✅ |
 | **Security Consulting** | ❌ | ✅ |
 | **Bug Bounty (paid)** | ❌ | ✅ |
 | **Enterprise Deployment** | ❌ | ✅ |
 ---
 ## 9. Custom Extensions
 ### Tailored Solutions for Your Organization
 Work with the CAI team to develop specialized capabilities.
 ### Extension Types
 #### Custom Agents
 - **Domain-specific security agents**: OT, IoT, cloud, robotics
 - **Industry-tailored**: Finance, healthcare, manufacturing
 - **Workflow-optimized**: Match your existing processes
 #### Tool Integration
 - **SIEM/SOAR**: Connect CAI to Splunk, QRadar, Sentinel
 - **Ticketing Systems**: Jira, ServiceNow automation
 - **CI/CD Pipelines**: Jenkins, GitLab, GitHub Actions
 #### Reporting Templates
 - **Compliance-specific**: PCI-DSS, ISO 27001, SOC 2
 - **Client-branded**: Match your corporate identity
 - **Multi-language**: Localized reports
 #### API Wrappers
 - **Internal tools**: Integrate with proprietary systems
 - **Data pipelines**: Feed CAI results to analytics platforms
 - **Automation**: Trigger CAI from existing workflows
 **Contact contact@aliasrobotics.com to discuss custom development.**
 ---
 ## Feature Comparison Matrix
 ### CAI FREE vs CAI PRO vs CAI GOV/ENTERPRISE
 | Feature | FREE | PRO | GOV/ENTERPRISE |
 |---------|------|-----|----------------|
 | **Core Framework** | ✅ (~6mo delay) | ✅ Latest | ✅ Latest + Custom |
 | **300+ Models** | ✅ BYO Keys | ✅ BYO Keys | ✅ BYO Keys + Private |
 | **Alias1 Tokens** | ❌ | ✅ Unlimited | ✅ Unlimited + On-prem |
 | **TUI** | ❌ | ✅ Yes | ✅ Yes + Custom UI |
 | **Usage & cost visibility** (`/cost`, TUI) | ❌ | ✅ Yes | ✅ Yes + Analytics |
 | **Parallel Agents** | ❌ | ✅ 100+ | ✅ Unlimited |
 | **Support** | Community | ✅ Priority | ✅ Dedicated + Training |
 | **Reporting** | Basic | ✅ Advanced | ✅ Custom Templates |
 | **Guardrails** | Basic | ✅ 4-layer | ✅ Configurable |
 | **Commercial License** | ❌ | ✅ Yes | ✅ Yes + Redistribution |
 | **Custom Extensions** | ❌ | ✅ Available | ✅ Included |
 | **Audit Logging** | ❌ | ✅ Basic | ✅ Forensics-grade |
 | **Air-gapped Deployment** | ❌ | ❌ | ✅ Yes |
 | **On-premise Alias1** | ❌ | ❌ | ✅ Yes |
 | **Pricing** | **Free** | **€350/month** | **Custom Quote** |
 ---
 ## Get CAI PRO Today
 **Unlock all features and transform your security operations.**
 <div class="cta-box" markdown>
 ### 🚀 **Ready to Upgrade?**
 - ✅ Unlimited alias1 tokens
 - ✅ Terminal UI with parallel agents
 - ✅ Usage and cost visibility (`/cost`, compaction, TUI)
 - ✅ Professional support (48h SLA)
 - ✅ European data sovereignty (GDPR + NIS2)
 - ✅ Commercial use license
 - ✅ Advanced reporting
 - ✅ Custom extensions available
 **€350/month/user** · No long-term contracts · Cancel anytime
 **[Get CAI PRO →](https://aliasrobotics.com/cybersecurityai.php)**
 </div>
 ---
 ## Next Steps
 - **[View Full Pricing](cai_pro_pricing.md)** - Compare all plans
 - **[Learn About Alias1](cai_pro_alias1.md)** - Explore the flagship model
 - **[Quick Start Guide](cai_pro_quickstart.md)** - Get started in 5 minutes
 - **[Contact Sales](cai_pro_contact.md)** - Enterprise & custom plans
 ---
 <small>
 *Questions about features? Contact **support@aliasrobotics.com***  
 *Need enterprise capabilities? [Request custom pricing →](mailto:contact@aliasrobotics.com?subject=CAI%20PRO%20Features%20Inquiry)*
 </small>
--- a/docs/cai_pro_pricing.md
+++ b/docs/cai_pro_pricing.md
@ -1,349 +0,0 @@
 # CAI Pricing & Plans
 > **Choose the Right Plan for Your Security Needs**
 >
 > From individual researchers to enterprise security teams, CAI offers flexible pricing tailored to your requirements.
 >
 > **[Get CAI PRO →](https://aliasrobotics.com/cybersecurityai.php)**
 ---
 ## Plan Overview
 <div class="pricing-grid" markdown>
 ### CAI FREE
 **€0 / forever**
 Leading open-source framework for AI Security.  
 Free for research purposes.
 #### Included:
 - ✅ AI Security Framework (300+ LLM Models)
 - ✅ Built-in Security Tools
 - ✅ Agent-based Architecture
 - ✅ Guardrails Protection Built-in
 - ✅ Community Support
 - ✅ **Free for research** (non-commercial)
 #### Limitations:
 - ❌ No alias1 model access
 - ❌ No Terminal UI (TUI)
 - ❌ No parallel agent swarms
 - ❌ No PRO usage analytics and reporting
 - ❌ No commercial license
 - ⚠️ Framework updates ~6 months behind PRO
 **[View on GitHub →](https://github.com/aliasrobotics/cai)**
 ---
 ### CAI PRO
 **€350 / month / user**
 Leading enterprise framework for AI Security with professional support.
 #### Everything in FREE, plus:
 - ✅ **Unlimited alias1 tokens**
 - ✅ **Terminal User Interface (TUI)**
 - ✅ **Multi-agent parallel execution** (100+ agents)
 - ✅ **Usage visibility** (`/cost`, TUI cost panels)
 - ✅ **Commercial license** included
 - ✅ **Professional support** (48h SLA)
 - ✅ **GDPR & NIS2 compliant** European hosting
 - ✅ **Advanced reporting** (PDF, Markdown, HTML)
 - ✅ **Custom extensions** available
 - ✅ **Latest features** (6+ months ahead of open source)
 - ✅ **Priority Discord channel**
 **🎯 Most Popular for Security Professionals**
 **[Buy Now →](https://aliasrobotics.com/cybersecurityai.php)**
 ---
 ### CAI GOV & ENTERPRISE
 **Custom Pricing**
 Cybersecurity AI tailored to your organization requirements.  
 Custom deployment options.
 #### Everything in PRO, plus:
 - ✅ **On-premise & air-gapped** alias1 deployment
 - ✅ **Full privacy-by-design** architecture (alias0 arch)
 - ✅ **Priority support & training**
 - ✅ **Custom AI model fine-tuning** for your domain
 - ✅ **Audit logging & forensics** capabilities
 - ✅ **Dedicated account manager**
 - ✅ **SLA guarantees** (custom)
 - ✅ **Multi-platform deployment** (unlimited)
 - ✅ **Custom integrations**
 **Perfect for Large Teams, Government, & Regulated Industries**
 **[Contact Sales →](mailto:contact@aliasrobotics.com?subject=CAI%20Enterprise%20Inquiry)**
 </div>
 ---
 ## Detailed Feature Comparison
 | Feature | FREE | PRO | GOV/ENTERPRISE |
 |---------|------|-----|----------------|
 | **💰 Pricing** | **Free** | **€350/month** | **Custom** |
 | | | | |
 | **🤖 Core Capabilities** | | | |
 | AI Framework | ✅ (~6mo delay) | ✅ Latest | ✅ Latest + Custom |
 | 300+ LLM Models | ✅ BYO Keys | ✅ BYO Keys | ✅ BYO + Private |
 | Built-in Security Tools | ✅ Full Suite | ✅ Full Suite | ✅ Full Suite + Custom |
 | Agent-based Architecture | ✅ All Patterns | ✅ All Patterns | ✅ All + Custom |
 | Command Line Interface | ✅ Yes | ✅ Yes | ✅ Yes |
 | | | | |
 | **🚀 Alias1 Model** | | | |
 | Alias1 Access | ❌ | ✅ Unlimited Tokens | ✅ Unlimited + On-prem |
 | #2 CAIBench Rank | ❌ | ✅ Yes | ✅ Yes |
 | Zero Refusals | ❌ | ✅ Yes | ✅ Yes |
 | European Hosting | ❌ | ✅ Yes | ✅ + On-premise Option |
 | | | | |
 | **🖥️ User Interfaces** | | | |
 | Terminal UI (TUI) | ❌ | ✅ Multi-terminal | ✅ Multi-terminal + Custom |
 | Parallel Agent Execution | ❌ | ✅ 100+ agents | ✅ Unlimited |
 | Usage / cost visibility | ❌ | ✅ `/cost`, TUI | ✅ + Analytics Dashboard |
 | Keyboard Shortcuts | ❌ | ✅ Full Set | ✅ Full Set + Custom |
 | Team Presets | ❌ | ✅ 11 Teams | ✅ Unlimited Custom |
 | | | | |
 | **💬 Support** | | | |
 | Community Discord | ✅ Yes | ✅ Yes | ✅ Yes |
 | Email Support | ❌ | ✅ 48h SLA | ✅ Custom SLA |
 | Priority Discord Channel | ❌ | ✅ Yes | ✅ Yes |
 | Quarterly Strategy Calls | ❌ | ✅ Yes | ✅ Yes + More Frequent |
 | Dedicated Account Manager | ❌ | ❌ | ✅ Yes |
 | On-site Training | ❌ | ❌ | ✅ Available |
 | | | | |
 | **🛡️ Security & Compliance** | | | |
 | GDPR Compliant | ✅ Yes | ✅ Yes | ✅ Yes |
 | NIS2 Directive Ready | ✅ Yes | ✅ Yes | ✅ Yes |
 | EU Data Centers Only | ✅ Yes | ✅ Yes | ✅ + On-premise |
 | Guardrails | ✅ Basic | ✅ 4-layer | ✅ Configurable |
 | Audit Logging | ❌ | ✅ Basic | ✅ Forensics-grade |
 | | | | |
 | **📝 Reporting & Documentation** | | | |
 | Basic Reporting | ✅ CLI Output | ✅ CLI + Advanced | ✅ Custom Templates |
 | CTF Writeups | ❌ | ✅ Automated | ✅ Automated + Branded |
 | Pentest Reports | ❌ | ✅ Executive + Technical | ✅ Compliance-ready |
 | Export Formats | ✅ Markdown | ✅ MD, PDF, HTML, JSON | ✅ All + Custom |
 | | | | |
 | **🔧 Customization** | | | |
 | Custom Agents | ❌ | ✅ On Request | ✅ Included |
 | Custom Extensions | ❌ | ✅ Available | ✅ Included |
 | Tool Integration | ❌ | ✅ On Request | ✅ Full Integration Support |
 | API Wrappers | ❌ | ❌ | ✅ Custom Development |
 | Model Fine-tuning | ❌ | ❌ | ✅ Domain-specific |
 | | | | |
 | **🏢 Licensing** | | | |
 | Commercial Use | ❌ Research Only | ✅ Full Commercial | ✅ Full + Redistribution |
 | Academic Research | ✅ Yes | ✅ Yes | ✅ Yes |
 | Bug Bounty (paid) | ❌ | ✅ Yes | ✅ Yes |
 | Security Consulting | ❌ | ✅ Yes | ✅ Yes |
 | Enterprise Deployment | ❌ | ✅ Yes | ✅ Yes |
 | | | | |
 | **🐳 Deployment** | | | |
 | Cloud (Your Infra) | ✅ Yes | ✅ Yes | ✅ Yes |
 | On-premise | ✅ Self-hosted | ✅ Self-hosted | ✅ + Managed |
 | Air-gapped Networks | ❌ | ❌ | ✅ Supported |
 | Multi-platform | ✅ Limited | ✅ Unlimited | ✅ Unlimited + Support |
 ---
 ## Cost Comparison: Alias1 vs Competitors
 ### Monthly Cost for 1 Billion Tokens
 Based on CAI's average text generation profile: **15,430 input / 436 output tokens per request**
 <div class="cost-comparison" markdown>
 | Provider | Monthly Cost | Annual Cost | vs CAI PRO |
 |----------|--------------|-------------|------------|
 | **GPT-5** | €1,491 | €17,892 | ❌ **4.3× more expensive** |
 | **Claude Sonnet 4.5** | €3,330 | €39,960 | ❌ **9.5× more expensive** |
 | **Claude Opus 4.1** | €16,650 | €199,800 | ❌ **47.6× more expensive** |
 | **🤖 CAI PRO (∞ alias1)** | **€350** | **€4,200** | ✅ **Unlimited included** |
 ### Annual Savings with CAI PRO
 | vs Provider | Monthly Savings | Annual Savings |
 |-------------|-----------------|----------------|
 | vs GPT-5 | €1,141 | €13,692 |
 | vs Claude Sonnet 4.5 | €2,980 | €35,760 |
 | vs Claude Opus 4.1 | €16,300 | €195,600 |
 **💰 ROI: CAI PRO pays for itself in the first month** for teams using more than ~230M tokens/month.
 </div>
 ---
 ## Frequently Asked Questions
 ### How does billing work?
 **CAI PRO:**
 - **Monthly subscription**: Billed on the 1st of each month
 - **Payment methods**: Credit card, bank transfer (annual plans)
 - **Currency**: EUR (€)
 - **VAT**: Excluded from listed prices (added at checkout for EU customers)
 - **Cancellation**: Cancel anytime, access until end of billing period
 **CAI GOV/ENTERPRISE:**
 - **Custom billing**: Annual, quarterly, or monthly
 - **Purchase orders**: Accepted for enterprise plans
 - **Invoicing**: NET 30 terms available
 - **Multi-year contracts**: Discounts available
 ### What happens if I exceed fair use limits?
 CAI PRO includes **unlimited alias1 tokens** subject to fair use:
 **Fair use policy:**
 - Typical usage: 100M - 10B tokens/month ✅ **No issues**
 - Heavy usage: 10B - 100B tokens/month ✅ **Monitored but allowed**
 - Extreme usage: >100B tokens/month ⚠️ **Contact us to upgrade to Enterprise**
 We've never had to enforce limits—most users stay well within fair use.
 ### Can I switch plans?
 Yes! You can upgrade or downgrade anytime:
 **Upgrade (FREE → PRO):**
 - Instant access to PRO features
 - Billed monthly starting immediately
 **Downgrade (PRO → FREE):**
 - Access to PRO features until end of billing period
 - Automatic switch to FREE plan
 - Retain all data and configurations
 **Contact Sales for Enterprise:**
 - Custom migration path
 - Dedicated onboarding support
 ### Do you offer academic discounts?
 Yes! We offer special pricing for universities and research institutions:
 **Academic CAI PRO:**
 - **Contact**: contact@aliasrobotics.com with:
  - Institutional affiliation
  - Research project description
  - Number of users needed
 ### Can I pay annually?
 Yes! Annual plans available:
 **CAI PRO Annual:**
 - **€4,200/year** 
 - **Benefits**: Same as monthly + priority support
 **Contact contact@aliasrobotics.com for annual billing**
 ### What if I need more than alias1?
 CAI PRO includes:
 - **Unlimited alias1**: Use as much as you need
 - **BYO API keys**: Use your own keys for GPT-5, Claude, Gemini, etc.
 ### Can I schedule a demo?
 Yes! Contact us:
 **CAI PRO demos:**
 - Contact contact@aliasrobotics.com
 - Schedule a live demo with our team
 - See TUI, alias1, and advanced features in action
 We don't offer free PRO trials, but CAI FREE lets you evaluate the framework before committing.
 ### Can I use CAI PRO for bug bounties?
 **Yes!** CAI PRO includes a **full commercial license**, which covers:
 ✅ **Authorized bug bounty programs** (HackerOne, Bugcrowd, etc.)  
 ✅ **Security consulting services**  
 ✅ **Penetration testing** for clients  
 ✅ **Enterprise security operations**
 Bug bounties discovered with CAI PRO:
 - **$2,500+ earned** by CAI users in documented bounties
 - Vulnerabilities found in Ecoforest, MiR, Mercado Libre, and more
 [View Case Studies →](https://aliasrobotics.com/case-studies-robot-cybersecurity.php)
 ---
 ## Choose Your Plan
 <div class="pricing-cta" markdown>
 ### 🆓 **CAI FREE**
 **Perfect for:**
 - Individual researchers
 - Students & academics
 - Open-source contributors
 - Personal learning
 **[Get Started (GitHub) →](https://github.com/aliasrobotics/cai)**
 ---
 ### 🚀 **CAI PRO** (Most Popular)
 **Perfect for:**
 - Security professionals
 - Bug bounty hunters
 - Small security teams (1-5)
 - Security consultants
 **€350/month** · No contracts · Cancel anytime
 **[Buy CAI PRO →](https://aliasrobotics.com/cybersecurityai.php)**
 ---
 ### 🏢 **CAI GOV & ENTERPRISE**
 **Perfect for:**
 - Large security teams (20+)
 - Government agencies
 - Regulated industries
 - Custom requirements
 **Custom pricing** · Volume discounts · SLA guarantees
 **[Contact Sales →](mailto:contact@aliasrobotics.com?subject=CAI%20Enterprise%20Inquiry)**
 </div>
 ---
 ## Next Steps
 - **[Explore Features](cai_pro_features.md)** - See what's included in CAI PRO
 - **[Learn About Alias1](cai_pro_alias1.md)** - Understand our flagship model
 - **[Quick Start Guide](cai_pro_quickstart.md)** - Get started in 5 minutes
 - **[Contact Sales](cai_pro_contact.md)** - Questions? We're here to help
 ---
 <small>
 *All prices exclude VAT. Volume discounts available for teams.*  
 *Questions about pricing? Contact **contact@aliasrobotics.com***
 </small>
--- a/docs/cai_pro_quickstart.md
+++ b/docs/cai_pro_quickstart.md
@ -1,393 +0,0 @@
 # Get Started with CAI PRO
 > **Quick Start Guide**
 >
 > This guide will have you running CAI PRO with unlimited alias1 tokens in minutes.
 >
 > **Already subscribed?** Jump to [Step 2: Install CAI](#2-install-cai)
 ---
 ## Prerequisites
 - **Operating System**: Linux, macOS, or Windows (WSL2)
 - **Python**: 3.9 or higher
 - **Internet Connection**: Required for initial setup
 - **CAI PRO Subscription**: [Subscribe here](https://aliasrobotics.com/cybersecurityai.php)
 ---
 ## Quick Start Steps
 ### 1. Subscribe to CAI PRO
 Visit [https://aliasrobotics.com/cybersecurityai.php](https://aliasrobotics.com/cybersecurityai.php):
 1. Click **"Buy CAI PRO"**
 2. Complete payment (€350/month, secure European processing)
 3. Receive your **`ALIAS_API_KEY`** via email (within 5 minutes)
 **💡 Tip**: Check your spam folder if you don't receive the key immediately.
 ---
 ### 2. Install CAI
 #### Installation Steps
 1. **Create and navigate to your project directory:**
 ```bash
 mkdir cai-pro
 cd cai-pro
 ```
 2. **Update system packages:**
 ```bash
 sudo apt update
 ```
 3. **Create a Python virtual environment:**
 ```bash
 python3.12 -m venv cai_env
 ```
 4. **Activate the virtual environment:**
 ```bash
 source cai_env/bin/activate
 ```
 5. **Install CAI PRO from private package repository:**
 ```bash
 pip install --index-url https://packages.aliasrobotics.com:664/<api-key>/ cai-framework
 ```
 **⚠️ Important**: Replace `<api-key>` with your API Key from the subscription confirmation email.
 **Example:**
 ```bash
 pip install --index-url https://packages.aliasrobotics.com:664/sk-xxxxxxxxxxxxxxxx/ cai-framework
 ```
 **💡 Tip**: Your API Key looks like `sk-xxxxxxxxxxxxxxxx` and is provided in your CAI PRO subscription email.
 For detailed installation instructions and troubleshooting, see the [CAI PRO Installation Guide](Installation_Guide_for_CAI_Pro_v0.6.md).
 ---
 ### 3. Configure Your Environment
 Create or update your `.env` file in your project directory:
 ```bash
 # CAI PRO Configuration
 ALIAS_API_KEY="sk-your-caipro-key-here"
 CAI_MODEL="alias1"
 # Optional: Enable advanced features
 CAI_TUI_MODE=true
 CAI_GUARDRAILS=true
 CAI_STREAM=false
 ```
 **💡 Security Tip**: Never commit `.env` files to version control. Add `.env` to your `.gitignore`.
 ---
 ### 4. Verify Installation
 Test that CAI PRO is working correctly:
 ```bash
 # Launch CAI CLI
 cai
 # Inside CAI, check your model
 CAI> /model
 # You should see:
 # Available models:
 # - alias1 (active) ✅
 # - alias0
 # - gpt-4o (requires OPENAI_API_KEY)
 # - claude-sonnet-4 (requires ANTHROPIC_API_KEY)
 # ...
 ```
 **Expected output**: `alias1` should be listed and marked as active.
 ---
 ### 5. Run Your First Security Test
 Let's start with a simple security assessment:
 ```bash
 CAI> Analyze the security posture of https://testphp.vulnweb.com
 # Alias1 will:
 # 1. Perform reconnaissance
 # 2. Identify vulnerabilities
 # 3. Suggest exploitation techniques
 # 4. Provide remediation guidance
 ```
 **✅ Success!** You're now using unlimited alias1 tokens for security testing.
 ---
 ## Launch Terminal UI (TUI)
 CAI PRO includes a powerful multi-terminal interface:
 ```bash
 # Launch TUI mode
 cai --tui
 # Or set it as default in .env
 echo "CAI_TUI_MODE=true" >> .env
 cai
 ```
 ### TUI Quick Tips
 **Keyboard Shortcuts:**
 - `Ctrl+S` - Toggle sidebar
 - `Ctrl+N` / `Ctrl+B` - Switch between terminals
 - `Ctrl+L` - Clear terminal
 - `Ctrl+Q` - Exit
 **Add More Terminals:**
 - Click the `[+]` button in the top bar
 - Or use `/add` command
 **Load Preconfigured Teams:**
 - Open sidebar (`Ctrl+S`)
 - Click "Teams" tab
 - Select a team (e.g., "#1: 2 red + 2 bug")
 [Full TUI Documentation →](tui/tui_index.md)
 ---
 ## Common First Tasks
 ### Task 1: Web Application Security Assessment
 ```bash
 CAI> Conduct a comprehensive security assessment of https://example.com
 # Alias1 will:
 # - Enumerate subdomains and technologies
 # - Identify OWASP Top 10 vulnerabilities
 # - Test for SQL injection, XSS, CSRF
 # - Generate a detailed report
 ```
 ### Task 2: CTF Challenge Solving
 ```bash
 CAI> Solve this CTF challenge: [paste challenge description]
 # Alias1 excels at:
 # - Web challenges
 # - Binary exploitation
 # - Cryptography
 # - Reverse engineering
 ```
 ### Task 3: Exploit Development
 ```bash
 CAI> Write a Python exploit for CVE-2024-1234
 # Alias1 will:
 # - Research the vulnerability
 # - Develop a working exploit
 # - Include error handling
 # - Add comments explaining each step
 ```
 ### Task 4: Bug Bounty Reconnaissance
 ```bash
 CAI> Perform recon on https://bugbounty-target.com for a bug bounty program
 # Alias1 will:
 # - Enumerate attack surface
 # - Identify interesting endpoints
 # - Suggest testing strategies
 # - Prioritize high-value targets
 ```
 ---
 ## Advanced Configuration
 ### Check usage and costs
 Use **`/cost`** for session spend and token stats; use **`/compact`** when histories grow long.
 ```bash
 CAI> /cost
 ```
 ### Multi-Agent Parallel Execution
 Run multiple agents simultaneously in TUI:
 ```bash
 # In TUI mode, open sidebar (Ctrl+S)
 # Click "Teams" tab
 # Select Team #1: "2 red + 2 bug"
 # Type your prompt and press Ctrl+Shift+A to broadcast to all terminals
 Scan target.com for vulnerabilities
 ```
 ### Save and Load Sessions
 ```bash
 # Save your current conversation
 CAI> /save pentest_session.json
 # Load it later
 CAI> /load pentest_session.json
 ```
 ---
 ## Troubleshooting
 ### Issue: "alias1 not available"
 **Solution 1**: Check your API key
 ```bash
 # Verify ALIAS_API_KEY is set correctly
 env | grep ALIAS
 ```
 **Solution 2**: Ensure you're using CAI PRO version
 ```bash
 cai --version
 # Should show v0.6.0 or higher
 ```
 **Solution 3**: Contact support
 - Email: support@aliasrobotics.com
 - Subject: "alias1 not available - [your email]"
 ---
 ### Issue: "Rate limit exceeded"
 The client will automatically pause until the appropriate time frame has passed and then retry the iteration without interrupting the session. 
 If this is happening frequently, consider compacting the context with `/compact`.
 ---
 ### Issue: TUI not launching
 **Solution 1**: Install required dependencies
 ```bash
 pip install textual rich
 ```
 **Solution 2**: Check terminal compatibility
 ```bash
 # TUI requires a modern terminal emulator
 # Recommended: Alacritty, iTerm2, Windows Terminal
 ```
 **Solution 3**: Use CLI mode instead
 ```bash
 # TUI is optional, CLI works everywhere
 cai  # without --tui flag
 ```
 ---
 ## Next Steps
 ### 📚 Learn More
 - **[TUI Full Guide](tui/tui_index.md)** - Master the Terminal UI
 - **[Commands Reference](tui/commands_reference.md)** - All available commands
 - **[Alias1 Deep Dive](cai_pro_alias1.md)** - Understand your flagship model
 - **[Features Overview](cai_pro_features.md)** - Explore all CAI PRO capabilities
 ### 🎯 Practical Guides
 - **[Running Agents](running_agents.md)** - Agent selection and configuration
 - **[Context Management](context.md)** - Optimize token usage
 - **[Guardrails & Security](guardrails.md)** - Secure testing practices
 - **[Environment Variables](environment_variables.md)** - Complete configuration reference
 ### 🏆 Case Studies
 Learn from real-world CAI applications:
 - [Ecoforest Heat Pumps OT Security](https://aliasrobotics.com/case-studies-robot-cybersecurity.php)
 - [MiR Robot Vulnerability Discovery](https://aliasrobotics.com/case-studies-robot-cybersecurity.php)
 - [Mercado Libre API Testing](https://aliasrobotics.com/case-studies-robot-cybersecurity.php)
 ---
 ## Get Help
 ### Professional Support (CAI PRO Subscribers)
 - **Email**: support@aliasrobotics.com (48h SLA)
 - **Discord**: #pro-support channel (exclusive)
 - **Quarterly Calls**: Strategy and roadmap discussions
 ### Community Resources
 - **[Discord Community](https://discord.gg/fnUFcTaQAC)** - 1000+ security researchers
 - **[GitHub Issues](https://github.com/aliasrobotics/cai/issues)** - Bug reports and feature requests
 - **[Documentation](index.md)** - Complete CAI documentation
 ---
 ## Tips for Success
 ### 🎯 Best Practices
 1. **Start with clear prompts**: Be specific about your testing scope and objectives
 2. **Watch costs and thread length**: Use `/cost` and `/compact` as sessions grow
 3. **Leverage parallel execution**: Run multiple agents for comprehensive coverage
 4. **Save your sessions**: Use `/save` to preserve important conversations
 5. **Enable guardrails**: Keep `CAI_GUARDRAILS=true` for safer operations
 ### ⚡ Power User Tips
 - **Keyboard shortcuts**: Master `Ctrl+N`, `Ctrl+B`, `Ctrl+S` for efficient TUI navigation
 - **Team presets**: Use preconfigured teams instead of manual agent setup
 - **Mix models**: Use alias1 for exploitation, GPT-4o for professional reporting
 - **Custom agents**: Request specialized agents for your domain (contact support)
 ---
 ## Congratulations! 🎉
 You're now ready to leverage CAI PRO for professional security testing. 
 **Remember:**
 - ✅ Unlimited alias1 tokens
 - ✅ Zero refusals for authorized testing
 - ✅ Professional support available
 - ✅ European data privacy guaranteed
 **Questions?** Contact support@aliasrobotics.com
 ---
 <small>
 *Need help? We're here: **support@aliasrobotics.com***  
 *Want to upgrade to Enterprise? [Request quote →](mailto:contact@aliasrobotics.com?subject=CAI%20Enterprise%20Inquiry)*
 </small>
--- a/docs/cai_prompt_injection.md
+++ b/docs/cai_prompt_injection.md
@ -2,7 +2,7 @@
 ## Summary
-This implementation adds guardrails to protect CAI agents from prompt injection attacks when interacting with untrusted external content (web pages, server responses, CTF challenges, etc.).
+This implementation adds guardrails to protect CAI agents from prompt injection attacks when interacting with untrusted external content (web pages, server responses, CTF challenges, etc).
 ## Problem
--- a/docs/cai_quickstart.md
+++ b/docs/cai_quickstart.md
@ -52,6 +52,7 @@ cai --continue --prompt "find SQL injection vulnerabilities"
 ```
 With `--continue`, CAI will:
 - Analyze the conversation context after each turn
 - Generate intelligent continuation prompts
 - Keep working until the task is complete or interrupted
@ -64,23 +65,24 @@ From here on, type on `CAI` and start your security exercise. Best way to learn
 ??? "List of Environment Variables"
-    | Variable | Description |
+```
-    |----------|-------------|
+| Variable | Description |
-    | CTF_NAME | Name of the CTF challenge to run (e.g. "picoctf_static_flag") |
+|----------|-------------|
-    | CTF_CHALLENGE | Specific sub challenge name within the CTF to test (e.g. CTF_NAME="kiddoctf" contains 4 subchallenges. For running one of them: "01 linux i") |
+| CTF_NAME | Name of the CTF challenge to run (e.g. "picoctf_static_flag") |
-    | CTF_SUBNET | Network subnet for the CTF container |
+| CTF_CHALLENGE | Specific sub challenge name within the CTF to test (e.g. CTF_NAME="kiddoctf" contains 4 subchallenges. For running one of them: "01 linux i") |
-    | CTF_IP | IP address for the CTF container |
+| CTF_SUBNET | Network subnet for the CTF container |
-    | CTF_INSIDE | Whether to conquer the CTF from within container |
+| CTF_IP | IP address for the CTF container |
-    | CAI_MODEL | Model to use for agents |
+| CTF_INSIDE | Whether to conquer the CTF from within container |
-    | ⚠️ CAI_DEBUG | Set debug output level (0: Only tool outputs, 1: Verbose debug output, 2: CLI debug output) |
+| CAI_MODEL | Model to use for agents |
-    | ⚠️ CAI_BRIEF | Enable/disable brief output mode |
+| ⚠️ CAI_DEBUG | Set debug output level (0: Only tool outputs, 1: Verbose debug output, 2: CLI debug output) |
-    | CAI_MAX_TURNS | Maximum number of turns for agent interactions |
+| ⚠️ CAI_BRIEF | Enable/disable brief output mode |
-    | ⚠️ CAI_TRACING | Enable/disable OpenTelemetry tracing |
+| CAI_MAX_TURNS | Maximum number of turns for agent interactions |
-    | CAI_AGENT_TYPE | Specify the agents to use (e.g. "boot2root") |
+| ⚠️ CAI_TRACING | Enable/disable OpenTelemetry tracing |
-    | CAI_PRICE_LIMIT | Price limit for the conversation in dollars |
+| CAI_AGENT_TYPE | Specify the agents to use (e.g. "boot2root") |
-    | CAI_WORKSPACE | Defines the name of the workspace |
+| CAI_PRICE_LIMIT | Price limit for the conversation in dollars |
-    | CAI_GUARDRAILS | Enable/disable guardrails for prompt injection protection (default: true) |
+| CAI_WORKSPACE | Defines the name of the workspace |
-
+| CAI_GUARDRAILS | Enable/disable guardrails for prompt injection protection (default: true) |
 ```
 ## Setting Environment Variables
@ -106,17 +108,16 @@ CAI_PRICE_LIMIT="0.004" CAI_MODEL="qwen2.5:72b" cai
 #### 3. Runtime configuration
-After running CAI, use **`/env`** (the catalog).
+After running CAI, use `/env list` for the numbered catalog (current values and indices), or bare `/env` for `CAI_*` / `CTF_*` in the session.
 ```
-  /env set <#|NAME> <value...>   # set a catalog variable
+  /env set <number> <value>   # configure a catalog variable; see `env.py` or type `/help`
-  /env list                      # numbered catalog + live values
+
  /env default                   # restore catalog defaults
 ```
 ```
  cai
-  /env list
+  /env list   # numbered table: pick the # column for /env set
-  # Pick the catalog index or variable name from the first column
+  /env set <number> 0.004   # example: set CAI_PRICE_LIMIT after matching its row number
  /env set 18 "0.004"
 ```
--- a/docs/cli/advanced_usage.md
+++ b/docs/cli/advanced_usage.md
--- a/docs/cli/cli_index.md
+++ b/docs/cli/cli_index.md
@ -1,356 +0,0 @@
 # CAI Command Line Interface (CLI)
 The CAI CLI provides a powerful, terminal-based interface for interacting with cybersecurity AI agents through a traditional command-line environment, optimized for automation, scripting, and integration workflows.
 ```
          CCCCCCCCCCCCC      ++++++++   ++++++++      IIIIIIIIII
       CCC::::::::::::C  ++++++++++       ++++++++++  I::::::::I
     CC:::::::::::::::C ++++++++++         ++++++++++ I::::::::I
    C:::::CCCCCCCC::::C +++++++++    ++     +++++++++ II::::::II
   C:::::C       CCCCCC +++++++     +++++     +++++++   I::::I
  C:::::C                +++++     +++++++     +++++    I::::I
  C:::::C                ++++                   ++++    I::::I
  C:::::C                 ++                     ++     I::::I
  C:::::C                  +   +++++++++++++++   +      I::::I
  C:::::C                    +++++++++++++++++++        I::::I
  C:::::C                     +++++++++++++++++         I::::I
   C:::::C       CCCCCC        +++++++++++++++          I::::I
    C:::::CCCCCCCC::::C         +++++++++++++         II::::::II
     CC:::::::::::::::C           +++++++++           I::::::::I
       CCC::::::::::::C             +++++             I::::::::I
          CCCCCCCCCCCCC               ++              IIIIIIIIII
                      Cybersecurity AI (CAI), v0.6.0
                          Bug bounty-ready AI
 CAI>
 ```
 ## Overview
 The CLI is the foundational interface for CAI, offering:
 - **⚡ Lightweight Execution**: Minimal resource overhead for maximum performance
 - **🤖 Direct Agent Interaction**: Immediate access to all CAI agents
 - **📝 Command System**: 30+ built-in commands for complete control
 - **🔄 Automation Ready**: Perfect for scripting and CI/CD pipelines
 - **🧩 Queue System**: Batch processing with command chaining
 - **⚙️ Parallel Execution**: Run multiple agents simultaneously
 - **💾 Session Management**: Save and restore conversations
 - **🔧 Shell Integration**: Direct shell command execution
 ## When to Use the CLI vs TUI
 | Feature | CLI | TUI |
 |---------|-----|-----|
 | **Scripting/Automation** | ✅ Full support | ❌ Interactive only |
 | **CI/CD Integration** | ✅ Perfect fit | ❌ Not suitable |
 | **Resource Usage** | ✅ Minimal | ⚠️ Higher (UI overhead) |
 | **Batch Processing** | ✅ Queue system | ⚠️ Limited |
 | **Visual Feedback** | ⚠️ Text-based | ✅ Rich UI |
 | **Multi-agent Workflows** | ✅ Parallel mode | ✅ Visual split-screen |
 | **Remote/Headless** | ✅ SSH friendly | ⚠️ Requires terminal UI |
 | **Learning Curve** | ⚠️ Steeper | ✅ Intuitive |
 **Use CLI for**: Automation, scripting, CI/CD, headless servers, SSH sessions, batch processing
 **Use TUI for**: Interactive testing, visual multi-agent workflows, exploratory analysis, real-time monitoring
 ## Quick Start
 Launch the CLI:
 ```bash
 cai
 ```
 With an initial prompt:
 ```bash
 cai --prompt "scan 192.168.1.1 for open ports"
 ```
 With YAML configuration:
 ```bash
 cai --yaml agents.yaml
 ```
 Basic workflow:
 1. Launch CAI: `cai`
 2. Configure API key in `.env` or environment
 3. Select a model: `/model alias1`
 4. Choose an agent: `/agent redteam_agent`
 5. Type your prompt and press **Enter**
 See the [Getting Started Guide](getting_started.md) for detailed instructions.
 ## Key Features
 ### 🎯 Command System
 Over 30 built-in commands organized by category:
 - **Agent Management**: `/agent`, `/parallel`
 - **Memory & History**: `/memory`, `/history`, `/compact`, `/flush`, `/load`, `/merge`, `/save`
 - **Environment**: `/env` (catalog `list` / `get` / `set` / `default`), `/help var` (per-variable help), `/workspace`, `/virtualization`
 - **Tools & Integration**: `/mcp`, `/shell`
 - **Utilities**: `/model`, `/graph`, `/cost`, `/help`
 All commands support aliases for faster typing (e.g., `/a` for `/agent`, `/h` for `/help`).
 Learn more: [Commands Reference](commands_reference.md)
 ### ⚡ Parallel Execution
 Run multiple agents simultaneously:
 ```bash
 # Configure parallel agents
 /parallel add redteam_agent
 /parallel add bug_bounter_agent
 /parallel add blueteam_agent
 # Execute on all agents
 /parallel run "analyze target.com"
 ```
 Or use YAML configuration:
 ```bash
 cai --yaml agents.yaml --prompt "test application security"
 ```
 Learn more: [Advanced Usage](advanced_usage.md)
 ### 💻 Shell Integration
 Execute shell commands directly:
 ```bash
 # Using /shell command
 /shell nmap -sV 192.168.1.1
 # Using $ shortcut
 $ whoami
 # Using /$ alias
 /$ ls -la
 ```
 ### 💾 Session Management
 Save and restore conversations:
 ```bash
 # Save current session
 /save pentest_session.json
 # Save as Markdown report
 /save findings_report.md
 # Load previous session
 /load pentest_session.json
 ```
 ### 🧠 Memory Management
 Advanced memory features for long-term context:
 ```bash
 # Enable episodic memory
 CAI_MEMORY=episodic cai
 # Save memory snapshot
 /memory save "web app vulnerabilities found"
 # List saved memories
 /memory list
 # Apply memory to current session
 /memory apply mem_12345
 ```
 ## System Requirements
 - **Python**: 3.9 or higher
 - **Terminal**: Any modern terminal (bash, zsh, fish)
 - **API Key**: Valid `ALIAS_API_KEY` (get one from [Alias Robotics](https://aliasrobotics.com))
 - **Operating System**: Linux, macOS, Windows (WSL recommended)
 ### Supported Terminals
 - ✅ bash (Linux/macOS/WSL)
 - ✅ zsh (macOS/Linux)
 - ✅ fish (Linux/macOS)
 - ✅ PowerShell (Windows)
 - ✅ SSH sessions
 - ✅ tmux/screen
 - ✅ CI/CD environments
 ## Architecture
 ```
 CAI CLI
 ├── Core Components
 │   ├── run_cai_cli - Main interactive loop
 │   ├── AgentManager - Agent lifecycle management
 │   ├── CommandRegistry - Command routing and execution
 │   └── SessionRecorder - Session logging and persistence
 ├── Command System
 │   ├── AgentCommand - Agent switching and management
 │   ├── ParallelCommand - Multi-agent coordination
 │   ├── MCPCommand - External tool integration
 │   ├── ConfigCommand - Environment management
 │   └── 25+ additional commands
 └── Integration Layer
    ├── PromptToolkit - Input handling and completion
    ├── FuzzyCompleter - Intelligent autocompletion
    ├── QueueManager - Batch execution
    └── ShellExecutor - Direct shell access
 ```
 For technical details, see the [Architecture Overview](../cai_architecture.md).
 ## Common Use Cases
 ### 1. CTF Challenges
 ```bash
 # Set up CTF environment
 export CTF_NAME="hackableii"
 export CTF_CHALLENGE="web_challenge"
 export CAI_AGENT_TYPE="redteam_agent"
 # Launch with auto-execution
 cai --prompt "analyze the challenge and find the flag"
 ```
 ### 2. Bug Bounty Automation
 ```bash
 # Configure bug bounty workflow
 /agent bug_bounter_agent
 /model alias1
 # Execute reconnaissance
 Perform full reconnaissance on bugcrowd.example.com
 ```
 ### 3. CI/CD Security Testing
 ```bash
 #!/bin/bash
 # security-check.sh
 export CAI_MAX_TURNS=10
 export CAI_PRICE_LIMIT=5.0
 export CAI_TRACING=false
 cai --prompt "scan $CI_TARGET for OWASP Top 10 vulnerabilities ; generate JSON report" > security-report.json
 ```
 ### 4. Parallel Reconnaissance
 ```bash
 # agents.yaml
 agents:
  - name: subdomain_scanner
    agent_type: redteam_agent
    model: alias1
  - name: port_scanner
    agent_type: network_security_analyzer_agent
    model: alias1
  - name: vulnerability_checker
    agent_type: bug_bounter_agent
    model: alias1
 # Execute
 cai --yaml agents.yaml --prompt "full reconnaissance on target.com"
 ```
 ## Quick Reference
 ### Essential Commands
 | Command | Description | Example |
 |---------|-------------|---------|
 | `/agent list` | List all agents | `/agent list` |
 | `/agent <name>` | Switch agent | `/agent redteam_agent` |
 | `/model <name>` | Change model | `/model alias1` |
 | `/env list` | Catalog + live values | `/env list` |
 | `/help` | Show help | `/help agent` |
 | `/save <file>` | Save session | `/save session.json` |
 | `/load <file>` | Load session | `/load session.json` |
 | `/cost` | Show costs | `/cost` |
 ### Keyboard Shortcuts
 | Shortcut | Action |
 |----------|--------|
 | `Tab` | Autocomplete commands |
 | `↑/↓` | Navigate command history |
 | `Ctrl+C` | Interrupt execution |
 | `Ctrl+L` | Clear screen |
 | `Ctrl+D` | Exit CAI |
 | `Ctrl+Z` | Suspend process |
 | `Ctrl+X Ctrl+E` | Open editor |
 See the complete [Commands Reference](commands_reference.md) for all commands.
 ## Configuration
 CAI CLI can be configured via:
 1. **Environment Variables**: `CAI_MODEL`, `CAI_AGENT_TYPE`, etc.
 2. **`.env` File**: Place in your working directory
 3. **`/env` command**: Runtime changes via catalog (`/env set …`, `/env default`, …)
 4. **YAML Files**: Agent and workflow definitions
 Example `.env`:
 ```env
 ALIAS_API_KEY=ak_live_1234567890abcdef
 CAI_MODEL=alias1
 CAI_AGENT_TYPE=redteam_agent
 CAI_DEBUG=1
 CAI_PRICE_LIMIT=10.0
 CAI_MAX_TURNS=50
 ```
 For all configuration options, see [Configuration Guide](../getting-started/configuration.md).
 ## Documentation Structure
 ### For New Users
 1. [Getting Started](getting_started.md) - First steps and basic usage
 2. [Commands Reference](commands_reference.md) - Essential commands
 ### For Advanced Users
 3. [Commands Reference](commands_reference.md) - Complete command list
 4. [Advanced Usage](advanced_usage.md) - Automation, scripting, and advanced features
 ### Related Documentation
 - [Configuration Guide](../getting-started/configuration.md) - All environment variables
 - [Architecture Overview](../cai_architecture.md) - Technical architecture
 - [TUI Documentation](../tui/tui_index.md) - Terminal UI alternative
 ## Community and Support
 - **Documentation**: [https://docs.aliasrobotics.com](https://docs.aliasrobotics.com)
 - **GitHub Issues**: [https://github.com/aliasrobotics/cai/issues](https://github.com/aliasrobotics/cai/issues)
 - **Discord**: [Join our community](https://discord.gg/aliasrobotics)
 - **Twitter**: [@aliasrobotics](https://twitter.com/aliasrobotics)
 ## What's Next?
 - 📖 [Getting Started Guide](getting_started.md) - Learn the basics
 - 📚 [Commands Reference](commands_reference.md) - Master all commands
 - 🚀 [Advanced Usage](advanced_usage.md) - Unlock powerful features
 ---
 *Last updated: November 2025 | CAI CLI v0.6+*
--- a/docs/cli/commands_reference.md
+++ b/docs/cli/commands_reference.md
@ -1,833 +0,0 @@
 # CAI CLI and REPL commands reference
 This page documents flags accepted by the `cai` binary and slash commands in the **CLI** interactive REPL. For **Textual TUI** shortcuts, multi-terminal routing, and UI-only behavior, see the [TUI commands reference](../tui/commands_reference.md).
 ---
 ## On this page
 1. [Binary `cai` CLI flags](#binary-cai-cli-flags)
 2. [REPL syntax](#repl-syntax)
 3. [Agents and models](#agents-and-models)
 4. [Conversation and context](#conversation-and-context)
 5. [Memory](#memory)
 6. [Parallel execution and queues](#parallel-execution-and-queues)
 7. [Configuration and environment](#configuration-and-environment)
 8. [Integrations](#integrations)
 9. [System and processes](#system-and-processes)
 10. [Utilities](#utilities)
 11. [Quick reference table](#quick-reference-table)
 12. [Commands registered](#commands-registered)
 13. [Next steps](#next-steps)
 ---
 ## Binary `cai` CLI flags
 The binary parses known flags and treats **unrecognized tokens as an initial prompt** (positional prompt text), similar to `parse_known_args()` semantics.
 | Flag                 | Type     | Default   | Description                                                                           |
 | -------------------- | -------- | --------- | ------------------------------------------------------------------------------------- |
 | `--tui`              | flag     | false     | Start CAI in Textual TUI (multi-terminal). Sets `CAI_TUI_MODE=true`.                  |
 | `--yaml FILE`        | option   | —         | Load parallel agent definitions from a YAML file.                                     |
 | `--prompt TEXT`      | option   | —         | Initial prompt executed immediately on startup.                                       |
 | `--version`          | flag     | false     | Print `cai-framework` version and exit.                                               |
 | `--update`           | flag     | false     | Check for updates and install if available. Requires `ALIAS_API_KEY`.                 |
 | `--continue` / `-c`  | flag     | false     | Enable auto-continue mode (`CAI_CONTINUE_MODE=true`).                                 |
 | `--resume [SESSION]` | optional | —         | Resume a prior session: `last`, `list`, a session id, or path to a `.jsonl` log.      |
 | `--logpath PATH`     | option   | —         | Custom log directory (often used with `--resume`).                                    |
 | `--unrestricted`     | flag     | false     | Enable abliteration steering: `CAI_UNRESTRICTED=true`.                                |
 | `--yolo`             | flag     | false     | Skip confirmation for sensitive commands (`CAI_YOLO=true`). **Insecure.**             |
 | `--api`              | flag     | false     | Run CAI as an HTTP API backend (FastAPI + uvicorn). See [CAI API Backend](../api.md). |
 | `--api-host`         | option   | 127.0.0.1 | API bind host. Env: `CAI_API_HOST`.                                                   |
 | `--api-port`         | int      | 8000      | API bind port. Env: `CAI_API_PORT`.                                                   |
 | `--api-reload`       | flag     | false     | Uvicorn auto-reload. Env: `CAI_API_RELOAD`.                                           |
 | `--api-workers`      | int      | 1         | Uvicorn workers. Env: `CAI_API_WORKERS`.                                              |
 **API mode:** operational details, auth headers, and routes are documented in [CAI API Backend](../api.md). For a single list of all binary flags, stay on this page; the API doc focuses on HTTP behavior.
 **Example (non-interactive prompt):**
 ```bash
 cai --prompt "Summarize SSH hardening for Ubuntu 22.04"
 ```
 ---
 ## REPL syntax
 ```text
 /command [subcommand] [arguments ...]
 ```
 Aliases are listed per command. Arguments in `[]` are optional; `|` means alternatives.
 In **Example** sections below, lines starting with `CAI>` are commands you type; other lines are **representative** REPL output (exact text, tables, and colors vary by build and environment).
 ---
 ## Agents and models
 ### `/agent` (`/a`)
 Manage and switch agents.
 | Subcommand | Arguments               | Description                                             |
 | ---------- | ----------------------- | ------------------------------------------------------- |
 | `list`     | —                       | List available agents and parallel patterns.            |
 | `select`   | name / number / pattern | Switch to the given agent; transfers history.           |
 | `info`     | [name / number]         | Detailed info: key, model, tools, handoffs, guardrails. |
 | `current`  | —                       | Active agent configuration (in TUI: all terminals).     |
 | `new`      | —                       | Create a new agent interactively.                       |
 - **No arguments:** same as `/agent current`.
 - **Shortcut:** `/agent red_teamer` is equivalent to `/agent select red_teamer`.
 **Example:**
 ```text
 CAI> /agent list
 … table of agents, models, and ids (layout varies by build) …
 CAI> /agent select red_teamer
 Switched to agent: red_teamer
 CAI> /agent
 … same as /agent current: active agent, model, tools summary …
 ```
 ### `/model` (`/mod`)
 View or change the active LLM. The LiteLLM-backed catalog is under the `**show**` subcommand (for example `**/model show**` or `**/mod show**`).
 | Usage                            | Description                                                                                        |
 | -------------------------------- | -------------------------------------------------------------------------------------------------- |
 | `/model` (no extra args)         | Prints the current model and a numbered table of predefined models (with pricing where available). |
 | `/model <name>`                  | Sets the model by name (typically via `CAI_MODEL`), e.g. `/model gpt-4o`.                          |
 | `/model <n>`                     | Selects by number from the last displayed table, e.g. `/model 5`.                                  |
 | `/model show`                    | Lists LiteLLM-backed models (full catalog). Use `/model` / `/model <n>` to switch after choosing.  |
 | `/model show supported`          | Same catalog filtered to models with function-calling support.                                     |
 | `/model show <search>`           | Filter the catalog by substring.                                                                   |
 | `/model show supported <search>` | Filter to supported models matching a substring.                                                   |
 **Examples:**
 ```text
 CAI> /model
 Current model: alias1
 … numbered shortcuts / pricing table …
 CAI> /model gpt-4o
 Model set to gpt-4o
 CAI> /model show ollama
 … LiteLLM rows whose id/name contains "ollama" …
 CAI> /mod show supported
 … catalog filtered to function-calling models …
 ```
 ---
 ## Conversation and context
 ### `/flush` (`/clear`)
 Clear conversation history.
 | Subcommand | Arguments | Description                                          |
 | ---------- | --------- | ---------------------------------------------------- |
 | `all`      | —         | Clear **all** agents, plus sudo cache and allowlist. |
 | `agent`    | name      | Clear history for one agent.                         |
 - **CLI, no args:** interactive menu of agents that have history.
 - **TUI, no args:** clears **only** the current terminal.
 - **Shortcut:** `/flush Red Teamer` or `/flush P2` clears by display name or agent id.
 **Example:**
 ```text
 CAI> /flush agent red_teamer
 Cleared history for red_teamer.
 CAI> /flush all
 Cleared all agent histories (and related caches per build).
 ```
 ### `/compact` (`/cmp`)
 Summarize the conversation into memory.
 | Subcommand | Arguments                 | Description                       |
 | ---------- | ------------------------- | --------------------------------- |
 | `model`    | name / number / `default` | Model used for compaction.        |
 | `prompt`   | text / `reset`            | Custom summarization prompt.      |
 | `status`   | —                         | Show current compaction settings. |
 Inline flags: `--model …`, `--prompt …`. **No args:** in parallel mode compacts all; in single-agent / TUI shows a menu.
 **Example:**
 ```text
 CAI> /compact status
 Compaction model: default  |  custom prompt: (none)
 CAI> /compact
 … summary written to memory / thread shortened (wording varies) …
 ```
 ### `/history` (`/his`)
 History with optional filtering.
 | Subcommand | Arguments | Description                  |
 | ---------- | --------- | ---------------------------- |
 | `all`      | —         | Full history for all agents. |
 | `agent`    | name      | History for one agent.       |
 | `search`   | term      | Search the history.          |
 | `index`    | —         | Message index.               |
 **No arguments:** interactive control panel.
 **Example:**
 ```text
 CAI> /history search "nmap"
 … matching transcript excerpts …
 CAI> /history agent red_teamer
 … recent messages for that agent …
 ```
 ### `/load` (`/l`)
 Load JSONL into context.
 | Subcommand | Arguments | Description                        |
 | ---------- | --------- | ---------------------------------- |
 | `agent`    | [path]    | Load history for the active agent. |
 | `all`      | [path]    | Load for all agents.               |
 | `parallel` | [path]    | Load into parallel configuration.  |
 | `load-all` | —         | Load all available logs.           |
 **No arguments:** load from `logs/last`.
 **Example:**
 ```text
 CAI> /load session_2025-04-01.jsonl
 Loaded conversation from session_2025-04-01.jsonl
 ```
 ### `/merge` (`/mrg`)
 Shortcut for `/parallel merge`. Merges parallel contexts into the main thread and exits parallel mode.
 **Example:**
 ```text
 CAI> /merge
 … merged parallel branches into main; returned to single-agent mode …
 ```
 ---
 ## Memory
 ### `/memory` (`/mem`)
 Persistent memory under `~/.cai/memory`.
 | Subcommand     | Arguments      | Description                            |
 | -------------- | -------------- | -------------------------------------- |
 | `list`         | —              | List saved memories.                   |
 | `save`         | [name] [agent] | Save conversation as memory.           |
 | `apply`        | id / name      | Apply memory to context.               |
 | `show`         | id / name      | Show one memory’s content.             |
 | `delete`       | id / name      | Delete a memory.                       |
 | `remove`       | id / name      | Alias of `delete`.                     |
 | `merge`        | id1 id2 …      | Merge multiple memories.               |
 | `status`       | —              | Memory subsystem status.               |
 | `compact`      | [agent]        | Compact memory for an agent.           |
 | `clear`        | —              | Delete all memories.                   |
 | `list-applied` | —              | Memories applied to the current agent. |
 **No arguments:** control panel. A single id such as `M001` routes to `show`.
 **Example:**
 ```text
 CAI> /memory list
 M001  pentest-notes-20250401   12 messages
 M002  ctf-web-flags            8 messages
 CAI> /memory show M001
 … saved memory content …
 ```
 ---
 ## Parallel execution and queues
 ### `/parallel` (`/par`, `/p`)
 Configure and run parallel agents.
 | Subcommand        | Arguments               | Description                                 |
 | ----------------- | ----------------------- | ------------------------------------------- |
 | `add`             | agent [`--model` model] | Add an agent to the parallel set.           |
 | `list`            | —                       | List configured parallel agents.            |
 | `clear`           | —                       | Remove parallel agents (no merge).          |
 | `remove`          | agent / index           | Remove one parallel agent.                  |
 | `override-models` | model                   | Set a shared model for all parallel agents. |
 | `merge`           | [`all` / agent]         | Merge contexts into the main thread.        |
 | `prompt`          | agent / `all` `"text"`  | Enqueue a prompt for agent(s).              |
 | `run`             | —                       | Run all enqueued prompts in parallel.       |
 **No arguments:** show current parallel configuration.
 **Example:**
 ```text
 CAI> /parallel list
 P1  red_teamer   alias1
 P2  blueteam     gpt-4o
 CAI> /parallel prompt all "Enumerate TLS on target:443"
 … prompts queued …
 CAI> /parallel run
 … parallel fan-out; per-terminal output in TUI …
 ```
 ### `/queue` (`/que`)
 Sequential prompt queue.
 | Subcommand      | Arguments | Description                   |
 | --------------- | --------- | ----------------------------- |
 | `show` / `list` | —         | Show queued prompts.          |
 | `add`           | `"text"`  | Append a prompt.              |
 | `run`           | —         | Run all prompts sequentially. |
 | `remove`        | index     | Remove one entry.             |
 | `clear`         | —         | Empty the queue.              |
 | `next`          | —         | Run only the next prompt.     |
 | `load`          | path      | Load prompts from a file.     |
 **Example:**
 ```text
 CAI> /queue add "Run nmap -sV on 10.0.0.5"
 Queued #1
 CAI> /queue show
 #1  Run nmap -sV on 10.0.0.5
 CAI> /queue run
 … runs prompts in order …
 ```
 ### `/continue`
 Auto-continue mode.
 | Subcommand | Description                   |
 | ---------- | ----------------------------- |
 | `on`       | Set `CAI_CONTINUE_MODE=true`. |
 | `off`      | Disable auto-continue.        |
 | `status`   | Show state.                   |
 **No arguments:** turns the mode **on**.
 **Example:**
 ```text
 CAI> /continue status
 CAI_CONTINUE_MODE=false
 CAI> /continue on
 Auto-continue enabled.
 ```
 ---
 ## Configuration and environment
 ### `/env` (`/e`)
 Catalog and edit `CAI_*` and `CTF_*` variables; sensitive values are masked in listings.
 | Subcommand | Arguments                | Description                                                                   |
 | ---------- | ------------------------ | ----------------------------------------------------------------------------- |
 | `list`     | —                        | Numbered catalog with descriptions and current values (default when helpful). |
 | `get`      | `VAR` or index           | Print one variable's effective value.                                         |
 | `set`      | `VAR` or index, `value…` | Set a variable (by name or by catalog number from `list`).                    |
 | `default`  | —                        | Restore catalog defaults where applicable.                                    |
 **No arguments / bare `/env`:** compact view of live `CAI_`* / `CTF_`* values (not the full numbered catalog). Piping: `/env \| grep VAR` works in supported terminals.
 Alternate assignment style (some builds): `/env set VAR=value` or multiple tokens after `set`.
 **Examples:**
 ```text
 CAI> /env list
 … numbered catalog: CAI_MODEL, CAI_MAX_TURNS, CTF_NAME, … with current values …
 CAI> /env get CAI_MODEL
 CAI_MODEL=alias1
 CAI> /env set CAI_MODEL gpt-4o
 Set CAI_MODEL=gpt-4o
 CAI> /env set 18 5.0
 Set variable #18 to 5.0   (index 18 from /env list)
 CAI> /env
 CAI_MODEL=gpt-4o
 CAI_MAX_TURNS=100
 CTF_NAME=
 … other CAI_* / CTF_* keys in this session …
 CAI> /env default
 … restored defaults where the catalog defines them …
 ```
 ### `/settings` (`/set`)
 Interactive settings and FAQ.
 | Topic                           | Description               |
 | ------------------------------- | ------------------------- |
 | `faq` / `help` / `troubleshoot` | FAQ and troubleshooting.  |
 | `validate` / `check`            | Validate API keys.        |
 | `status` / `info`               | System status.            |
 | `lang` / `language`             | Language selector.        |
 | `ollama` / `local`              | Ollama / local model FAQ. |
 **No args:** full-screen interactive UI in CLI; TUI-adapted UI in TUI.
 **Example:**
 ```text
 CAI> /settings faq
 … opens FAQ / troubleshooting flow …
 ```
 ### `/temperature` (`/temp`)
 Show or set `CAI_TEMPERATURE` (float `0.0`–`2.0`).
 **Example:**
 ```text
 CAI> /temp
 CAI_TEMPERATURE=0.7
 CAI> /temp 0.2
 CAI_TEMPERATURE=0.2
 ```
 ### `/topp` (`/top_p`)
 Show or set nucleus sampling `CAI_TOP_P` (float `0.0`–`1.0`).
 **Example:**
 ```text
 CAI> /topp
 CAI_TOP_P=1.0
 CAI> /topp 0.9
 CAI_TOP_P=0.9
 ```
 ---
 ## Integrations
 ### `/mcp`
 Model Context Protocol servers.
 | Subcommand     | Arguments    | Description                                                                 |
 | -------------- | ------------ | --------------------------------------------------------------------------- |
 | `load`         | url name     | Load an MCP **SSE** server.                                                 |
 | `load-stdio`   | command name | Load an MCP **stdio** server (some builds use `/mcp load stdio …` instead). |
 | `list`         | —            | Active MCP connections.                                                     |
 | `add-to-agent` | agent server | Attach MCP tools to an agent.                                               |
 Many builds also support extra helpers (`tools`, `status`, `remove`, …); use `/mcp help` locally.
 **Example:**
 ```text
 CAI> /mcp load https://example.com/mcp/sse ctf-tools
 Loaded MCP server "ctf-tools" (SSE).
 CAI> /mcp list
 ctf-tools   SSE   connected
 ```
 ### `/api` (`/apikey`)
 Manage `ALIAS_API_KEY` in the environment / `.env`.
 | Subcommand | Arguments | Description              |
 | ---------- | --------- | ------------------------ |
 | `show`     | —         | Display key (masked).    |
 | `set`      | key       | Persist `ALIAS_API_KEY`. |
 **No arguments:** `show`. Shortcut: `/api sk-…` behaves like `/api set sk-…`.
 **Example:**
 ```text
 CAI> /api
 ALIAS_API_KEY=sk-…xxxx   (masked)
 CAI> /api set sk-proj-…
 API key saved to environment / .env (path depends on build).
 ```
 ### `/auth`
 API users and device pairing.
 | Subcommand | Arguments         | Description                 |
 | ---------- | ----------------- | --------------------------- |
 | `add-user` | username password | Add an API user.            |
 | `add-ip`   | ip[:port]         | Register an IP for pairing. |
 **Example:**
 ```text
 CAI> /auth add-user analyst hunter2
 User "analyst" created.
 CAI> /auth add-ip 203.0.113.10
 Registered pairing for 203.0.113.10
 ```
 ---
 ## System and processes
 ### `/shell` (`/s`, `$`)
 Run the remainder of the line as a shell command.
 Examples: `/shell ls -la`, `/s whoami`, `$ nmap -sV 10.0.0.1`
 **Example:**
 ```text
 CAI> $ whoami
 alice
 CAI> /shell ls -la /tmp
 total 12
 drwxrwxrwt … /tmp
 ```
 ### `/workspace` (`/ws`)
 Workspace (host or Docker container).
 | Subcommand | Arguments | Description                        |
 | ---------- | --------- | ---------------------------------- |
 | `set`      | path      | Set working directory / workspace. |
 | `get`      | —         | Show current workspace.            |
 | `ls`       | [path]    | List files.                        |
 | `exec`     | command   | Run a command in the workspace.    |
 | `copy`     | src dst   | Copy between host and container.   |
 **No arguments:** same as `get`.
 **Example:**
 ```text
 CAI> /ws get
 Workspace: /home/alice/projects/cai-target
 CAI> /ws ls src
 agent.py  tools.py  …
 ```
 ### `/virtualization` (`/virt`)
 Docker environments.
 | Subcommand | Arguments       | Description      |
 | ---------- | --------------- | ---------------- |
 | `pull`     | image           | Pull an image.   |
 | `run`      | image [options] | Run a container. |
 **No arguments:** Docker status. A single non-subcommand argument can select/activate an image as container context (see `/help virtualization` on your build).
 **Example:**
 ```text
 CAI> /virt
 Docker: running   images: 12   active: cai-kali:latest
 CAI> /virt pull kalilinux/kali-rolling
 … pull progress …
 ```
 ---
 ## Utilities
 ### `/help` (`/h`, `/?`)
 Built-in help and topic deep-dives.
 Example topics: `agent`, `parallel`, `queue`, `memory`, `history`, `compact`, `flush`, `load`, `merge`, `env`, `workspace`, `virtualization`, `mcp`, `shell`, `model`, `graph`, `aliases`, `commands`, `quick`, `quickstart`, `topics`.
 Examples: `/help`, `/help parallel`, `/? model`
 **Example:**
 ```text
 CAI> /help env
 … environment catalog / usage for /env …
 ```
 ### `/cost` (`/costs`, `/usage`)
 | Subcommand | Description                             |
 | ---------- | --------------------------------------- |
 | `summary`  | Session summary (default when no args). |
 | `models`   | Per-model breakdown.                    |
 | `daily`    | Daily costs.                            |
 | `sessions` | Per-session costs.                      |
 | `reset`    | Reset counters.                         |
 **Example:**
 ```text
 CAI> /cost
 Session spend: $0.42   tokens in: 120k   tokens out: 18k
 CAI> /cost models
 alias1   $0.30
 gpt-4o   $0.12
 ```
 ### `/graph` (`/g`)
 | Subcommand | Arguments | Description    |
 | ---------- | --------- | -------------- |
 | `all`      | —         | Full graph.    |
 | `timeline` | —         | Timeline view. |
 | `stats`    | —         | Statistics.    |
 | `export`   | [path]    | Export graph.  |
 **No args:** active agent view. A free argument can select `P1`, `P2`, or an agent name.
 **Example:**
 ```text
 CAI> /graph
 … ASCII or Rich-rendered interaction graph for the active agent …
 ```
 ### `/exit` (`/q`, `/quit`)
 Clean exit: telemetry flush, session end, `sys.exit(0)`.
 **Example:**
 ```text
 CAI> /exit
 Goodbye.
 ```
 ### `/quickstart` (`/qs`, `/quick`)
 Essential setup instructions and tables (no args).
 **Example:**
 ```text
 CAI> /quickstart
 … printed checklist: keys, /env list, /model show, agents, … …
 ```
 ### `/replay`
 Replay a session JSONL.
 - Path to `.jsonl`, or no args → `logs/last`.
 - Optional trailing **numeric** argument: delay in seconds between actions.
 - `stop` / `cancel`: stop in-progress replay (TUI).
 Sets `CAI_DISABLE_SESSION_RECORDING=true` while replaying.
 **Example:**
 ```text
 CAI> /replay logs/last.jsonl 0.5
 … replay with 0.5s delay between actions …
 ```
 ### `/resume` (`/r` when registered)
 Resume a previous session. Optional:
 - `--full` / `-f` — full history.
 - Path to `.jsonl`, or session id.
 - No args — latest session.
 **Example:**
 ```text
 CAI> /resume --full ./logs/session_20250401.jsonl
 … session restored with full transcript …
 ```
 ### `/sessions` (`/sess`)
 List recent sessions; optional count limit or session id / label for details.
 **Example:**
 ```text
 CAI> /sessions 5
 … last five session ids, paths, and labels …
 ```
 ### `/ctr`
 Cyber threat reasoning / attack graphs. Optional alias form `ctr` (without leading `/`) in some builds.
 | Subcommand | Description                 |
 | ---------- | --------------------------- |
 | `show`     | Current CTR analysis.       |
 | `graph`    | Attack graph visualization. |
 | `list`     | Available analyses.         |
 | `use`      | Select an analysis.         |
 | `open`     | Open dedicated view.        |
 **No arguments:** full CTR analysis output.
 **Example:**
 ```text
 CAI> /ctr show
 … current CTR narrative / indicators …
 ```
 ### `/metadebug` (`/md`)
 Meta-agent debugging (**TUI**). Requires `CAI_META_AGENT=true`.
 **Example:**
 ```text
 CAI> /md
 … meta-agent trace / routing (TUI only; requires CAI_META_AGENT=true) …
 ```
 ---
 ## Quick reference table
 | Command           | Aliases            | Description                                                       |
 | ----------------- | ------------------ | ----------------------------------------------------------------- |
 | `/agent`          | `/a`               | Manage and switch agents                                          |
 | `/api`            | `/apikey`          | Show or set `ALIAS_API_KEY`                                       |
 | `/auth`           | —                  | API users and device pairing                                      |
 | `/compact`        | `/cmp`             | Compact conversation to memory                                    |
 | `/continue`       | —                  | Auto-continue mode                                                |
 | `/cost`           | `/costs`, `/usage` | Usage cost statistics                                             |
 | `/ctr`            | `ctr`              | CTR analysis / attack graphs                                      |
 | `/env`            | `/e`               | Catalog / get / set / `default` for `CAI_*` and `CTF_*` variables |
 | `/exit`           | `/q`, `/quit`      | Exit the REPL                                                     |
 | `/flush`          | `/clear`           | Clear conversation history                                        |
 | `/graph`          | `/g`               | Interaction graph                                                 |
 | `/help`           | `/h`, `/?`         | Help and documentation                                            |
 | `/history`        | `/his`             | History with filters                                              |
 | `/load`           | `/l`               | Load JSONL into context                                           |
 | `/mcp`            | —                  | MCP servers                                                       |
 | `/memory`         | `/mem`             | Persistent memory (`~/.cai/memory`)                               |
 | `/merge`          | `/mrg`             | Merge parallel contexts                                           |
 | `/metadebug`      | `/md`              | Meta-agent debug (TUI)                                            |
 | `/model`          | `/mod`             | View or change model; `show` subcommand lists LiteLLM catalog     |
 | `/parallel`       | `/par`, `/p`       | Parallel agents                                                   |
 | `/queue`          | `/que`             | Sequential prompt queue                                           |
 | `/quickstart`     | `/qs`, `/quick`    | Quick start guide                                                 |
 | `/replay`         | —                  | Replay JSONL session                                              |
 | `/resume`         | `/r`               | Resume session                                                    |
 | `/sessions`       | `/sess`            | List recent sessions                                              |
 | `/settings`       | `/set`             | Interactive settings / FAQ                                        |
 | `/shell`          | `/s`, `$`          | Shell escape                                                      |
 | `/temperature`    | `/temp`            | Sampling temperature                                              |
 | `/topp`           | `/top_p`           | Top-p sampling                                                    |
 | `/virtualization` | `/virt`            | Docker environments                                               |
 | `/workspace`      | `/ws`              | Workspace management                                              |
 ---
 ## Commands registered
 The following modules are imported in `[src/cai/repl/commands/__init__.py](../../src/cai/repl/commands/__init__.py)` in a typical OSS snapshot: `agent`, `compact`, `cost`, `env`, `exit`, `flush`, `graph`, `help`, `history`, `load`, `mcp`, `memory`, `merge`, `model`, `parallel`, `quickstart`, `shell`, `virtualization`, `workspace`. Your checkout may load extra modules; use `/help` locally for the authoritative list.
 Commands such as `/resume`, `/queue`, `/continue`, `/settings`, `/api`, `/auth`, `/replay`, `/sessions`, `/ctr`, and `/metadebug` are listed above for completeness but may ship only in builds that add their modules to the REPL registry.
 ---
 ## Next steps
 - [CLI getting started](getting_started.md)
 - [CLI advanced usage](advanced_usage.md)
 - [CLI overview](cli_index.md)
 - [CAI API Backend](../api.md) — HTTP API when using `cai --api`
 ---
--- a/docs/cli/getting_started.md
+++ b/docs/cli/getting_started.md
@ -1,567 +0,0 @@
 # Getting Started with CAI CLI
 This guide will walk you through launching the CAI CLI for the first time and performing your first security assessment using the command-line interface.
 ## Prerequisites
 Before starting, ensure you have:
 - ✅ CAI installed (see [Installation Guide](../cai_installation.md))
 - ✅ Python 3.9+ installed
 - ✅ A valid `ALIAS_API_KEY` from [Alias Robotics](https://aliasrobotics.com)
 ## Step 1: Launch the CLI
 Open your terminal and run:
 ```bash
 cai
 ```
 You should see the CAI banner and prompt:
 ```
          CCCCCCCCCCCCC      ++++++++   ++++++++      IIIIIIIIII
       CCC::::::::::::C  ++++++++++       ++++++++++  I::::::::I
     CC:::::::::::::::C ++++++++++         ++++++++++ I::::::::I
    C:::::CCCCCCCC::::C +++++++++    ++     +++++++++ II::::::II
   C:::::C       CCCCCC +++++++     +++++     +++++++   I::::I
  C:::::C                +++++     +++++++     +++++    I::::I
  C:::::C                ++++                   ++++    I::::I
  C:::::C                 ++                     ++     I::::I
  C:::::C                  +   +++++++++++++++   +      I::::I
  C:::::C                    +++++++++++++++++++        I::::I
  C:::::C                     +++++++++++++++++         I::::I
   C:::::C       CCCCCC        +++++++++++++++          I::::I
    C:::::CCCCCCCC::::C         +++++++++++++         II::::::II
     CC:::::::::::::::C           +++++++++           I::::::::I
       CCC::::::::::::C             +++++             I::::::::I
          CCCCCCCCCCCCC               ++              IIIIIIIIII
                      Cybersecurity AI (CAI), v0.6.0
                          Bug bounty-ready AI
 CAI>
 ```
 The navigation bar at the bottom displays important system information including your current model, agent, cost tracking, and session details.
 ## Step 2: Configure Your API Key
 If your `ALIAS_API_KEY` is not configured, you'll see an authentication error. Configure it using one of these methods:
 ### Method 1: Using a `.env` file (Recommended)
 Create a `.env` file in your working directory:
 ```env
 ALIAS_API_KEY=ak_live_1234567890abcdef
 CAI_MODEL=alias1
 CAI_AGENT_TYPE=redteam_agent
 CAI_DEBUG=1
 CAI_PRICE_LIMIT=10.0
 ```
 ### Method 2: Environment Variables
 Set it directly in your terminal:
 ```bash
 export ALIAS_API_KEY="ak_live_1234567890abcdef"
 cai
 ```
 ### Method 3: Runtime configuration
 After launching CAI, use the **`/env`** catalog:
 ```bash
 CAI> /env set CAI_MODEL alias1
 ```
 To view the catalog with live values:
 ```bash
 CAI> /env list
 ```
 ## Step 3: Select Your Model
 CAI supports multiple AI models. For optimal performance and cost balance, we recommend `alias1`:
 ```bash
 CAI> /model alias1
 ```
 To see all available models:
 ```bash
 CAI> /model show
 ```
 ### Recommended Models
 | Model | Provider | Best For | Cost |
 |-------|----------|----------|------|
 | `alias1` | Alias Robotics | **Recommended** - Balanced performance | Medium |
 | `gpt-4o` | OpenAI | Complex reasoning and multi-modal | High |
 | `claude-3-5-sonnet-20241022` | Anthropic | Fast responses with good quality | High |
 | `o1-mini` | OpenAI | Reasoning tasks | Medium |
 > **💡 Tip**: You can change models at any time without losing your conversation history.
 ## Step 4: Choose Your Agent
 CAI provides specialized agents for different security tasks. Here's how to choose:
 ### Option 1: List All Available Agents
 ```bash
 CAI> /agent list
 ```
 This displays all agents with their descriptions and primary use cases.
 ### Option 2: Use the Selection Agent
 If you're unsure which agent to use, start with the `selection_agent`:
 ```bash
 CAI> /agent selection_agent
 CAI> I need to test a web application for SQL injection
 ```
 The agent will recommend the best agent for your task.
 ### Option 3: Choose Directly
 If you know which agent you need:
 ```bash
 CAI> /agent redteam_agent
 ```
 ### Common Agents and When to Use Them
 | Agent | Purpose | When to Use |
 |-------|---------|-------------|
 | `redteam_agent` | Offensive security testing | Default for penetration testing |
 | `bug_bounter_agent` | Bug bounty hunting | Finding high-value vulnerabilities in web apps |
 | `blueteam_agent` | Defensive security analysis | Security posture assessment and hardening |
 | `one_tool_agent` | Single-tool execution | Quick scans with specific tools |
 | `dfir_agent` | Digital forensics and incident response | Log analysis and forensic investigation |
 | `reverse_engineering_agent` | Binary analysis | Malware analysis, firmware reversing |
 | `network_security_analyzer_agent` | Network security assessment | Network scanning and traffic analysis |
 | `wifi_security_agent` | WiFi security testing | Wireless penetration testing |
 | `selection_agent` | Agent recommendation | **When unsure which agent to use** |
 > **💡 Pro Tip**: Start with `selection_agent` if you're new to CAI—it will guide you to the right agent for your task.
 ## Step 5: Start Your First Interaction
 Now you're ready to interact with CAI! Simply type your prompt and press **Enter**.
 ### Example 1: Basic Network Reconnaissance
 ```bash
 CAI> Scan 192.168.1.1 for open ports and services
 ```
 The agent will:
 - Process your request
 - Select and execute appropriate tools (e.g., nmap)
 - Display results in real-time
 - Provide analysis and recommendations
 ### Example 2: Web Application Testing
 ```bash
 CAI> /agent bug_bounter_agent
 CAI> Test https://example.com for common web vulnerabilities
 ```
 The agent will:
 - Perform reconnaissance
 - Test for OWASP Top 10 vulnerabilities
 - Execute security tools
 - Provide detailed findings
 ### Example 3: CTF Challenge
 ```bash
 # Set up CTF environment
 CAI> /env set CTF_NAME hackableii
 CAI> /env set CTF_CHALLENGE web_challenge
 # Start the challenge
 CAI> Analyze this CTF challenge and find the flag
 ```
 ### Understanding the Output
 As the agent works, you'll see:
 1. **Tool Execution**: Messages showing which tools are being launched
 2. **Tool Output**: Real-time results from executed commands
 3. **Agent Reasoning**: The agent's thought process (if `CAI_DEBUG=1`)
 4. **Final Analysis**: Summary, findings, and recommendations
 5. **Cost Tracking**: Updated costs in the navigation bar
 ## Step 6: Essential Commands
 Here are the most important commands to know:
 ### Getting Help
 ```bash
 # General help (quick guide; full environment-variable tables below)
 CAI> /help
 # Help for specific command
 CAI> /help agent
 # Long-form help for one environment variable
 CAI> /help var CAI_MODEL
 # Quick reference guide
 CAI> /quickstart
 ```
 ### Agent Management
 ```bash
 # List all agents
 CAI> /agent list
 # Switch to a specific agent
 CAI> /agent redteam_agent
 # Get info about current agent
 CAI> /agent info
 ```
 ### Model Management
 ```bash
 # View current model
 CAI> /model
 # Change model
 CAI> /model gpt-4o
 # List all available models (LiteLLM catalog)
 CAI> /model show
 ```
 ### Session Management
 ```bash
 # Save current conversation
 CAI> /save pentest_session.json
 # Save as Markdown report
 CAI> /save findings_report.md
 # Load previous conversation
 CAI> /load pentest_session.json
 ```
 ### View History and Costs
 ```bash
 # View conversation history
 CAI> /history
 # View last 20 messages
 CAI> /history 20
 # Check costs and token usage
 CAI> /cost
 ```
 ### Clear and Reset
 ```bash
 # Clear terminal output (keeps history)
 CAI> Ctrl+L
 # Flush conversation history
 CAI> /flush
 # Exit CAI
 CAI> /exit
 # or press Ctrl+D
 ```
 ## Step 7: Shell Command Execution
 CAI allows you to execute shell commands directly:
 ### Using /shell Command
 ```bash
 CAI> /shell nmap -sV 192.168.1.1
 ```
 ### Using $ Shortcut
 ```bash
 CAI> $ whoami
 CAI> $ ls -la
 CAI> $ nmap -sV localhost
 ```
 ### Interactive Tools
 For interactive tools, the agent will handle them appropriately:
 ```bash
 CAI> Run a comprehensive port scan on 192.168.1.0/24
 # Agent will execute nmap with appropriate flags
 ```
 ## Step 8: Working with Configuration
 ### View current configuration
 ```bash
 CAI> /env list
 ```
 This shows the environment catalog with numbers and current values. Use bare **`/env`** for a compact view of `CAI_*` / `CTF_*` only.
 ### Change configuration at runtime
 ```bash
 # Set by catalog index (from /env list)
 CAI> /env set 18 "5.0"
 # Or set by variable name
 CAI> /env set CAI_PRICE_LIMIT 5.0
 CAI> /env set CAI_MAX_TURNS 50
 ```
 ### Important Configuration Variables
 | Variable | Description | Example |
 |----------|-------------|---------|
 | `CAI_MODEL` | Default model to use | `alias1` |
 | `CAI_AGENT_TYPE` | Default agent | `redteam_agent` |
 | `CAI_DEBUG` | Debug level (0-2) | `1` |
 | `CAI_PRICE_LIMIT` | Maximum cost in USD | `10.0` |
 | `CAI_MAX_TURNS` | Maximum conversation turns | `50` |
 | `CAI_MAX_INTERACTIONS` | Maximum tool interactions | `100` |
 | `CAI_TRACING` | Enable OpenTelemetry tracing | `true` |
 | `CAI_GUARDRAILS` | Enable security guardrails | `true` |
 See the complete [Configuration Guide](../cai/getting-started/configuration.md) for all options.
 ## Step 9: Common Workflows
 ### Workflow 1: Quick Security Scan
 ```bash
 # Launch with specific agent
 CAI_AGENT_TYPE=redteam_agent cai
 # Execute scan
 CAI> Perform a quick security assessment of 192.168.1.100
 # Save results
 CAI> /save quick_scan_results.md
 ```
 ### Workflow 2: Bug Bounty Reconnaissance
 ```bash
 # Start with bug bounty agent
 CAI> /agent bug_bounter_agent
 # Reconnaissance
 CAI> Perform full reconnaissance on target.com
 # Test specific vulnerability
 CAI> Test the login form for SQL injection
 # Generate report
 CAI> Generate a detailed bug bounty report
 # Save session
 CAI> /save bugbounty_target_session.json
 ```
 ### Workflow 3: CTF Challenge
 ```bash
 # Configure CTF environment
 export CTF_NAME="hackableii"
 export CTF_CHALLENGE="web_app"
 export CAI_AGENT_TYPE="redteam_agent"
 # Launch and solve
 cai
 CAI> Analyze this CTF challenge and find the flag
 ```
 ### Workflow 4: Network Analysis
 ```bash
 CAI> /agent network_security_analyzer_agent
 # Analyze network
 CAI> Scan the network 192.168.1.0/24 for security issues
 # Analyze captured traffic
 CAI> Analyze this PCAP file for suspicious activity
 # View findings
 CAI> /history
 ```
 ## Step 10: Keyboard Shortcuts
 Master these shortcuts for faster navigation:
 | Shortcut | Action |
 |----------|--------|
 | `Tab` | Autocomplete commands and arguments |
 | `↑` / `↓` | Navigate through command history |
 | `Ctrl+C` | Interrupt current execution |
 | `Ctrl+L` | Clear terminal screen |
 | `Ctrl+Z` | Suspend process (resume with `fg`) |
 | `Ctrl+U` | Clear current input line |
 | `Ctrl+A` | Move cursor to start of line |
 | `Ctrl+E` | Move cursor to end of line |
 ## Common First-Time Issues
 ### Issue: API Key Not Valid
 **Solution**: 
 ```bash
 # Check your API key is set correctly
 CAI> /env | grep ALIAS_API_KEY
 # If not set, add it to .env file
 echo "ALIAS_API_KEY=your_key_here" >> .env
 ```
 ### Issue: Agent Not Responding
 **Solution**:
 ```bash
 # Cancel current operation
 Ctrl+C
 # Check agent is loaded
 CAI> /agent
 # Switch to a different agent
 CAI> /agent redteam_agent
 ```
 ### Issue: Command Not Found
 **Solution**:
 ```bash
 # Get help for available commands
 CAI> /help
 # Use Tab completion to see available commands
 CAI> /<Tab>
 # Check command syntax
 CAI> /help <command_name>
 ```
 ### Issue: Price Limit Reached
 **Solution**:
 ```bash
 # Check current costs
 CAI> /cost
 # Increase limit
 CAI> /env set CAI_PRICE_LIMIT 20.0
 # Or set it before launching
 CAI_PRICE_LIMIT=20.0 cai
 ```
 ### Issue: Max Turns Exceeded
 **Solution**:
 ```bash
 # Increase turn limit
 CAI> /env set CAI_MAX_TURNS 100
 # Or flush history and start fresh
 CAI> /flush
 ```
 ## Next Steps
 Congratulations! You've completed the basics of CAI CLI. Here's what to explore next:
 ### Learn More Commands
 - 📚 [Commands Reference](commands_reference.md) - Complete command documentation
 - 🚀 [Advanced Usage](advanced_usage.md) - Automation, scripting, and advanced features
 ### Explore Advanced Features
 - **Queue System**: Batch process multiple prompts
 - **Parallel Execution**: Run multiple agents simultaneously
 - **Memory Management**: Persistent context across sessions
 - **MCP Integration**: Connect external tools and services
 ### Specialized Workflows
 - **CTF Challenges**: Learn CTF-specific workflows
 - **Bug Bounty**: Master bug bounty hunting techniques
 - **Automation**: Script security assessments
 - **CI/CD Integration**: Integrate CAI into your pipeline
 ### Get Help
 - ❓ [FAQ](../cai_faq.md) - Common questions
 - 💬 [Discord](https://discord.gg/aliasrobotics) - Community support
 - 🐛 [GitHub Issues](https://github.com/aliasrobotics/cai/issues) - Report bugs
 ## Quick Reference Card
 ### Most Used Commands
 ```bash
 /agent list              # List all agents
 /agent <name>            # Switch agent
 /model <name>            # Change model
 /model show              # LiteLLM model catalog
 /env list                # Catalog + live values
 /help                    # Get help
 /save <file>             # Save session
 /load <file>             # Load session
 /cost                    # Show costs
 /history                 # View history
 /shell <cmd>             # Run shell command
 $ <cmd>                  # Shell shortcut
 /exit                    # Exit CAI
 ```
 ### Essential Workflows
 ```bash
 # Quick scan
 cai --prompt "scan target.com for vulnerabilities"
 # CTF mode
 CTF_NAME="challenge" cai
 # Bug bounty
 CAI_AGENT_TYPE=bug_bounter_agent cai
 # With initial setup
 CAI_MODEL=alias1 CAI_PRICE_LIMIT=10 cai
 ```
 ---
 *Last updated: November 2025 | CAI CLI v0.6+*
--- a/docs/context.md
+++ b/docs/context.md
@ -11,7 +11,7 @@ This is represented via the [`RunContextWrapper`][cai.sdk.agents.run_context.Run
 1. You create any Python object you want. A common pattern is to use a dataclass or a Pydantic object.
 2. You pass that object to the various run methods (e.g. `Runner.run(..., **context=whatever**))`).
-3. All your tool calls, lifecycle hooks, etc. will be passed a wrapper object, `RunContextWrapper[T]`, where `T` represents your context object type which you can access via `wrapper.context`.
+3. All your tool calls, lifecycle hooks etc will be passed a wrapper object, `RunContextWrapper[T]`, where `T` represents your context object type which you can access via `wrapper.context`.
 The **most important** thing to be aware of: every agent, tool function, lifecycle, etc for a given agent run must use the same _type_ of context.
--- a/docs/continue_mode.md
+++ b/docs/continue_mode.md
@ -321,35 +321,12 @@ These examples demonstrate:
 - Graceful interruption with Ctrl+C
 - Practical security use cases
 ## Combining with Session Resume
 The `--continue` flag works seamlessly with `--resume` to continue interrupted sessions autonomously:
 ```bash
 # Resume last session and continue working autonomously
 cai --resume --continue
 # Resume specific session and continue
 cai --resume abc12345 --continue
 # Resume from interactive selector and continue
 cai --resume list --continue
 ```
 This powerful combination:
 1. **Restores your previous session** with full conversation history
 2. **Automatically generates a continuation prompt** based on where you left off
 3. **Continues working autonomously** without waiting for user input
 For more details on session resume capabilities, see the [Session Resume](session_resume.md) documentation.
 ## Summary
 The `--continue` flag transforms CAI into an autonomous cybersecurity assistant capable of:
 - Working independently on complex tasks
 - Recovering from errors intelligently
 - Maintaining context across multiple operations
 - Resuming and continuing interrupted sessions with `--resume --continue`
 - Providing entertainment with continuous jokes
 Whether you're conducting security audits, hunting for bugs, or just want some cybersecurity humor, continue mode keeps your agent working until the job is done.
--- a/docs/environment_variables.md
+++ b/docs/environment_variables.md
@ -8,72 +8,58 @@ This comprehensive guide documents all environment variables available in CAI, i
 In current CAI releases, you can explore environment variables **from inside the interactive CLI** without leaving the session:
-| What you need | Command |
+
-|---------------|---------|
+| What you need                                                                        | Command                                                                                                 |
-| **Numbered catalog with live values** (what is set *now*) | `/env list` |
+| ------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- |
-| **Session keys only** (`CAI_*` / `CTF_*` in this process) | `/env` (no arguments) |
+| **Numbered list with live values** (what is set *now*)                               | `/env` or `/env list` for extended list of variables                                                    |
-| **Full reference tables** (defaults, allowed values, when they apply, extras) | `/help` — scroll past the quick guide; or `/help topics` for the overview first, then the same tables at the end |
+| **Full reference tables** (defaults, allowed values, when they apply, extras)        | `/help` — scroll past the quick guide to the tables (`/help topics` lists commands by category only, no env tables) |
-| **Long-form help for one variable** (examples, catalog index when listed, notes) | `/help var VARIABLE_NAME` (e.g. `/help var CAI_MODEL`, `/help var CAI_AVOID_SUDO`) |
+| **Long-form help for one variable** (examples, `/env list` index when listed, notes) | `/help var VARIABLE_NAME` (e.g. `/help var CAI_MODEL`)                                                  |
 Aliases such as `/h` for `/help` work the same way. This page remains the **canonical web reference**; the REPL output tracks the version you have installed.
 ---
 ## 📖 Fields explained (same model as `/help var NAME`)
 In the REPL, **`/help var VARIABLE_NAME`** expands each variable with the same ideas used below:
 | Field | Meaning |
 |-------|---------|
 | **Description** | What the variable controls (see the next table). |
 | **Values** | Value *type* or documented range (e.g. `bool`, `int 0–2`, `string`) — same notion as the **Values** column in the large `/help` tables. |
 | **When** | **Runtime** — often picked up on each use via `os.getenv`. **Restart** — typically read only at process start (new session recommended). **Mixed** — in `os.environ` but parts of CAI may cache until the next turn, agent switch, or restart. |
 | **Default** | Documented default when unset or as shipped. |
 **How to set (matches `/help` copy):**
 - Before launch: `export VAR=value` or a line in `.env`, then start CAI.
 - During a session: `/env set <#|NAME> <value…>`, `/env default`, or code updating `os.environ`.
 **Types (short):** *bool* — `true`/`false`, `1`/`0`, etc.; *string* — free text; *int* / *float* — numeric; ranges in **Values** are the usual bounds CAI documents; *secret* — treat like a string, never commit real keys.
 For **numbered catalog index**, **extra notes**, and **copy-paste examples** per variable, use **`/help var NAME`** in the REPL — the web page keeps one compact table for browsing.
 ---
 ## 📋 Complete Reference Table
-| Variable | Description | Values | When | Default |
+
-|----------|-------------|------|------|---------|
+| Variable                   | Description                                                                                                                                                                                                                                                                                                                                                                                      | Default                                        |
-| CTF_NAME | Name of the CTF challenge to run (e.g. "picoctf_static_flag") | string | Mixed | - |
+| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------- |
-| CTF_CHALLENGE | Specific challenge name within the CTF to test | string | Mixed | - |
+| CTF_NAME                   | Name of the CTF challenge to run (e.g. "picoctf_static_flag")                                                                                                                                                                                                                                                                                                                                    | -                                              |
-| CTF_SUBNET | Network subnet for the CTF container | string | Mixed | 192.168.3.0/24 |
+| CTF_CHALLENGE              | Specific challenge name within the CTF to test                                                                                                                                                                                                                                                                                                                                                   | -                                              |
-| CTF_IP | IP address for the CTF container | string | Mixed | 192.168.3.100 |
+| CTF_SUBNET                 | Network subnet for the CTF container                                                                                                                                                                                                                                                                                                                                                             | 192.168.3.0/24                                 |
-| CTF_INSIDE | Whether to conquer the CTF from within container | bool | Mixed | true |
+| CTF_IP                     | IP address for the CTF container                                                                                                                                                                                                                                                                                                                                                                 | 192.168.3.100                                  |
-| CAI_MODEL | Model to use for agents | string | Mixed | alias1 |
+| CTF_INSIDE                 | Whether to conquer the CTF from within container                                                                                                                                                                                                                                                                                                                                                 | true                                           |
-| CAI_DEBUG | Set debug output level (0: Only tool outputs, 1: Verbose debug output, 2: CLI debug output) | int 0–2 | Mixed | 1 |
+| CAI_MODEL                  | Model to use for agents                                                                                                                                                                                                                                                                                                                                                                          | alias1                                         |
-| CAI_BRIEF | Enable/disable brief output mode | bool | Mixed | false |
+| CAI_DEBUG                  | Set debug output level (0: Only tool outputs, 1: Verbose debug output, 2: CLI debug output)                                                                                                                                                                                                                                                                                                      | 1                                              |
-| CAI_MAX_TURNS | Maximum number of turns for agent interactions | int ≥1 | Mixed | inf |
+| CAI_BRIEF                  | Enable/disable brief output mode                                                                                                                                                                                                                                                                                                                                                                 | false                                          |
-| CAI_MAX_INTERACTIONS | Maximum number of interactions (tool calls, agent actions, etc.) allowed in a session. If exceeded, only CLI commands are allowed until increased. If force_until_flag=true, the session will exit | int ≥1 | Mixed | inf |
+| CAI_MAX_TURNS              | Maximum number of turns for agent interactions                                                                                                                                                                                                                                                                                                                                                   | inf                                            |
-| CAI_PRICE_LIMIT | Price limit for the conversation in dollars. If exceeded, only CLI commands are allowed until increased. If force_until_flag=true, the session will exit | float ≥0 | Mixed | 1 |
+| CAI_ORCHESTRATION_WORKER_MAX_TURNS | Max ``Runner`` turns for each specialist worker spawned by ``orchestration_agent`` tools (``run_specialist``, ``run_dual_approach_contest``, ``run_parallel_specialists``). Integer 1–32                                                                                                                                                                                                                                                                                                                                                   | 6                                              |
-| CAI_TRACING | Enable/disable OpenTelemetry tracing. When enabled, traces execution flow and agent interactions for debugging and analysis | bool | Restart | true |
+| CAI_ORCHESTRATION_MAS_HINT         | When ``true``, ``orchestration_agent`` may receive one synthetic ``user``-role nudge per ``Runner`` run if the user message looks multi-front but only ``run_specialist`` was invoked (suggests ``run_parallel_specialists`` / contest). Set ``false`` to disable                                                                                                                                                                                                                                                                                                                                         | true                                           |
-| CAI_AGENT_TYPE | Specify the agents to use (e.g., boot2root, one_tool, redteam_agent). Use "/agent" command in CLI to list all available agents | string | Mixed | redteam_agent |
+| CAI_MAX_INTERACTIONS       | Maximum number of interactions (tool calls, agent actions, etc.) allowed in a session. If exceeded, only CLI commands are allowed until increased. If force_until_flag=true, the session will exit                                                                                                                                                                                               | inf                                            |
-| CAI_STATE | Enable/disable stateful mode. When enabled, the agent will use a state agent to keep track of the state of the network and the flags found | bool | Mixed | false |
+| CAI_PRICE_LIMIT            | Price limit for the conversation in dollars. If exceeded, only CLI commands are allowed until increased. If force_until_flag=true, the session will exit                                                                                                                                                                                                                                         | 1                                              |
-| CAI_MEMORY | Enable/disable memory mode (episodic: use episodic memory, semantic: use semantic memory, all: use both episodic and semantic memory) | string | Mixed | false |
+| CAI_TRACING                | Enable/disable OpenTelemetry tracing. When enabled, traces execution flow and agent interactions for debugging and analysis                                                                                                                                                                                                                                                                      | true                                           |
-| CAI_MEMORY_ONLINE | Enable/disable online memory mode | bool | Mixed | false |
+| CAI_AGENT_TYPE             | Registered agent key. Defaults to `orchestration_agent` for default routing plus optional dual-approach contest; use `selection_agent` for the slimmer handoff-only router, or pin a specialist such as `redteam_agent`. Use "/agent" command in CLI to list all available agents                                                                                                                   | orchestration_agent                           |
-| CAI_MEMORY_OFFLINE | Enable/disable offline memory | bool | Mixed | false |
+| CAI_STATE                  | Enable/disable stateful mode. When enabled, the agent will use a state agent to keep track of the state of the network and the flags found                                                                                                                                                                                                                                                       | false                                          |
-| CAI_ENV_CONTEXT | Add environment context, dirs and current env available | bool | Mixed | true |
+| CAI_COMPACTED_MEMORY       | When true, inject `/compact` conversation summaries into agent system prompts                                                                                                                                                                                                                                                                                                                      | false                                          |
-| CAI_MEMORY_ONLINE_INTERVAL | Number of turns between online memory updates | int | Mixed | 5 |
+| CAI_ENV_CONTEXT            | Add environment context, dirs and current env available                                                                                                                                                                                                                                                                                                                                          | true                                           |
-| CAI_SUPPORT_MODEL | Model to use for the support agent | string | Mixed | o3-mini |
+| CAI_SUPPORT_MODEL          | Model to use for the support agent                                                                                                                                                                                                                                                                                                                                                               | o3-mini                                        |
-| CAI_SUPPORT_INTERVAL | Number of turns between support agent executions | int | Mixed | 5 |
+| CAI_SUPPORT_INTERVAL       | Number of turns between support agent executions                                                                                                                                                                                                                                                                                                                                                 | 5                                              |
-| CAI_STREAM | Enable/disable streaming output in rich panel | bool | Runtime | false |
+| CAI_STREAM                 | Enable/disable streaming output for LLM inference (token-by-token display). Does NOT affect tool output streaming                                                                                                                                                                                                                                                                                | false                                          |
-| CAI_TELEMETRY | Enable/disable telemetry | bool | Restart | true |
+| CAI_TOOL_STREAM            | Enable/disable streaming output for tool executions (real-time command output). Independent of CAI_STREAM                                                                                                                                                                                                                                                                                        | true                                           |
-| CAI_PARALLEL | Number of parallel agent instances to run. When set to values greater than 1, executes multiple instances of the same agent in parallel and displays all results | int 1–20 | Mixed | 1 |
+| CAI_DEBUG_TOOLS_VIZ        | Enable debug output for tool visualization and panel rendering. Shows detailed info about tool call display, deduplication, and streaming state                                                                                                                                                                                                                                                  | false                                          |
-| CAI_GUARDRAILS | Enable/disable security guardrails for agents. When set to "true", applies security guardrails to prevent potentially dangerous outputs and inputs | bool | Runtime | false |
+| CAI_SHOW_CACHE             | Show cache information and message history list. Displays prompt caching stats and the full message list sent to the model                                                                                                                                                                                                                                                                       | false                                          |
-| CAI_GCTR_NITERATIONS | Number of tool interactions before triggering GCTR (Generative Cut-The-Rope) analysis in bug_bounter_gctr agent. Only applies when using gctr-enabled agents | int | Mixed | 5 |
+| CAI_TELEMETRY              | Enable/disable telemetry                                                                                                                                                                                                                                                                                                                                                                         | true                                           |
-| CAI_ACTIVE_CONTAINER | Docker container ID where commands should be executed. When set, shell commands and tools execute inside the specified container instead of the host. Automatically set when CTF challenges start (if CTF_INSIDE=true) or when switching containers via /virtualization command | string | Mixed | - |
+| CAI_PARALLEL               | Number of parallel agent instances to run. When set to values greater than 1, executes multiple instances of the same agent in parallel and displays all results                                                                                                                                                                                                                                 | 1                                              |
-| CAI_TOOL_TIMEOUT | Override the default timeout for tool command executions in seconds. When set, this value overrides all default timeouts for shell commands and tool executions | int (s) | Runtime | varies (10s for interactive, 100s for regular) |
+| CAI_GUARDRAILS             | Enable/disable security guardrails for agents. When set to "true", applies security guardrails to prevent potentially dangerous outputs and inputs                                                                                                                                                                                                                                               | false                                          |
-| C99_API_KEY | API key for C99.nl subdomain discovery service. Required for using the C99 reconnaissance tool for DNS enumeration and subdomain discovery. Obtain from [C99.nl](https://c99.nl) | string | Mixed | - |
+| CAI_GCTR_NITERATIONS       | Number of tool interactions before triggering GCTR (Generative Cut-The-Rope) analysis in bug_bounter_gctr agent. Only applies when using gctr-enabled agents                                                                                                                                                                                                                                     | 5                                              |
 | CAI_ACTIVE_CONTAINER       | Docker container ID where commands should be executed. When set, shell commands and tools execute inside the specified container instead of the host. Automatically set when CTF challenges start (if CTF_INSIDE=true) or when attaching a container via `/virtualization` / `/virt` in the REPL                                                                                                                  | -                                              |
 | C99_API_KEY                | API key for C99.nl subdomain discovery service. Required for using the C99 reconnaissance tool for DNS enumeration and subdomain discovery. Obtain from [C99.nl](https://c99.nl)                                                                                                                                                                                                                 | -                                              |
 | CAI_TOOL_TIMEOUT           | Override the default timeout for tool command executions in seconds. When set, this value overrides all default timeouts for shell commands and tool executions                                                                                                                                                                                                                                  | varies (10s for interactive, 100s for regular) |
 | CAI_IDLE_TIMEOUT           | Maximum seconds a command can produce no output before being terminated. Useful for long-running commands like nmap scans that may have gaps between output lines                                                                                                                                                                                                                                | 100                                            |
 | CAI_CTX_TRUNC              | Enable context truncation for large tool outputs. When set to "true", automatically truncates large outputs (>50k chars) to prevent context overflow. JS/HTML/CSS/JSON files get aggressive truncation with preview only. Message history also applies position-based truncation when context exceeds 100k tokens or 60% usage                                                                   | false                                          |
 | CAI_DISPLAY_MAX_OUTPUT     | Show full tool output without truncation. When set to "true", displays complete tool output regardless of length. By default (false), outputs longer than 10,000 characters are truncated showing the first 5,000 and last 5,000 characters with "... TRUNCATED ..." in between. Useful for debugging format string exploits, large command outputs, or when you need to see the complete result | false                                          |
 ---
@ -87,14 +73,18 @@ For first-time users, these are the essential variables to configure:
 # Required: Model selection
 CAI_MODEL="alias1"                    # or gpt-4o, claude-sonnet-4.5, ollama/qwen2.5:72b
-# Recommended: Agent type
+# Recommended: Agent type (default CLI entry is orchestration_agent)
-CAI_AGENT_TYPE="redteam_agent"        # See available agents with /agent command
+CAI_AGENT_TYPE="orchestration_agent" # breadth-first + specialist tools; selection_agent = handoffs only
 # CAI_ORCHESTRATION_WORKER_MAX_TURNS=6   # per-worker turn cap when using orchestration_agent tools
 # CAI_ORCHESTRATION_MAS_HINT=true        # optional multi-front nudge for orchestration_agent
 # CAI_AGENT_TYPE="redteam_agent"        # pin a specialist when you know the toolkit
 # Optional but useful: Cost control
 CAI_PRICE_LIMIT="1"                   # Maximum spend in dollars
 ```
 **Related Documentation:**
 - [Installation Guide](cai/getting-started/installation.md)
 - [Configuration Guide](cai/getting-started/configuration.md)
@ -118,11 +108,13 @@ CTF_INSIDE="true"                     # Run agent inside container
 ```
 **Best Practices:**
 - Set `CTF_INSIDE=true` to run the agent inside the challenge container
 - Use `CAI_ACTIVE_CONTAINER` to manually specify which container to execute commands in
 - Combine with `CAI_STATE=true` to track discovered flags
 **Related Documentation:**
 - [CTF Benchmarks](benchmarking/jeopardy_ctfs.md)
 ---
@ -140,38 +132,33 @@ CAI_AGENT_TYPE="redteam_agent"        # Or create custom recon agent
 ```
 **Reconnaissance Tools:**
 - **C99 Tool**: Subdomain discovery and DNS enumeration via C99.nl API
 - Configure `C99_API_KEY` to enable the C99 reconnaissance tool
 - See [Tools Documentation](tools.md) for usage examples
 **Related Documentation:**
 - [Tools Documentation](tools.md#c99-tool)
 ---
-### 🧠 Memory & State Management
+### 🧠 Compacted memory and state
-For maintaining context across sessions and learning from past interactions:
+For carrying forward summarized context after `/compact`:
 ```bash
 # State tracking
 CAI_STATE="true"                      # Enable network state tracking
-# Memory modes
+# Inject /compact summaries into new agent prompts
-CAI_MEMORY="all"                      # Options: episodic, semantic, all, false
+CAI_COMPACTED_MEMORY="true"
 CAI_MEMORY_ONLINE="true"              # Enable online memory
 CAI_MEMORY_OFFLINE="true"             # Enable offline memory
 # Memory tuning
 CAI_MEMORY_ONLINE_INTERVAL="5"       # Turns between memory updates
 ```
-**Memory Modes Explained:**
+`CAI_MEMORY` and related Qdrant-style variables are deprecated and ignored by core CAI; use `CAI_COMPACTED_MEMORY` only.
- `episodic`: Remember specific past events and interactions
+
- `semantic`: Extract and store general knowledge
+**Related documentation:**
 - `all`: Combine both episodic and semantic memory
 **Related Documentation:**
 - [Advanced Features](tui/advanced_features.md)
 ---
@ -186,21 +173,19 @@ CAI_GUARDRAILS="true"                 # Prevent dangerous commands
 CAI_PRICE_LIMIT="1"                   # Maximum cost in dollars
 CAI_MAX_INTERACTIONS="inf"            # Maximum allowed interactions
 # Shell privilege policy (generic Linux tool)
 CAI_AVOID_SUDO="true"                 # Block sudo/su/pkexec/doas (hard block; see /help var CAI_AVOID_SUDO)
 # Debugging & monitoring
 CAI_DEBUG="1"                         # 0: minimal, 1: verbose, 2: CLI debug
 CAI_TRACING="true"                    # Enable OpenTelemetry tracing
 ```
 **Security Layers:**
 - **Guardrails**: Prompt injection detection and command validation
 - **`CAI_AVOID_SUDO`**: Blocks privilege escalation in the generic Linux shell tool; use `/help var CAI_AVOID_SUDO` for examples and session vs. launch notes
 - **Cost Limits**: Prevent runaway API usage
 - **Interaction Limits**: Control agent autonomy
 **Related Documentation:**
 - [Guardrails Documentation](guardrails.md)
 - [TUI Advanced Features](tui/advanced_features.md)
@ -213,24 +198,39 @@ For optimizing output, execution speed, and resource usage:
 ```bash
 # Output control
 CAI_BRIEF="true"                      # Concise output mode
-CAI_STREAM="false"                    # Disable streaming for faster processing
+CAI_STREAM="false"                    # Disable LLM inference streaming (default: false)
 CAI_TOOL_STREAM="true"                # Enable tool output streaming (default: true)
 # Context optimization
 CAI_ENV_CONTEXT="true"                # Include environment in context
 CAI_MAX_TURNS="50"                    # Limit conversation turns
 CAI_CTX_TRUNC="true"                  # Truncate large outputs to save context
 CAI_DISPLAY_MAX_OUTPUT="false"        # Show full output (set true to disable truncation)
 # Tool execution timeout
 CAI_TOOL_TIMEOUT="60"                 # Override default command timeouts (in seconds)
 CAI_IDLE_TIMEOUT="100"                # Max seconds without output before terminating (default: 100)
 # Telemetry
 CAI_TELEMETRY="true"                  # Enable usage analytics
 ```
 **Streaming Configuration:**
 - `CAI_STREAM`: Controls LLM inference streaming (token-by-token display). Default: `false`
 - `CAI_TOOL_STREAM`: Controls tool output streaming (real-time command output). Default: `true`
 - These are **independent** - you can have tool streaming enabled while LLM streaming is disabled
 **Performance Tips:**
 - Enable `CAI_BRIEF` for concise outputs in automated workflows
 - Set `CAI_MAX_TURNS` to prevent infinite loops
- Use `CAI_STREAM=false` when output display is not needed
+- Use `CAI_STREAM=false` (default) for faster LLM responses without token-by-token display
 - Use `CAI_TOOL_STREAM=true` (default) to see command output in real-time
 - Set `CAI_TOOL_TIMEOUT` to control command execution timeouts (default: 10s for interactive, 100s for regular commands)
 - Set `CAI_IDLE_TIMEOUT` to control how long a command can run without producing output before being terminated (default: 100s). Increase for slow network scans like nmap
 - Enable `CAI_CTX_TRUNC=true` when working with large files (JS/HTML/CSS) to prevent context overflow
 - Set `CAI_DISPLAY_MAX_OUTPUT=true` to see full tool output without truncation (useful for debugging format strings, large outputs)
 ---
@ -251,11 +251,13 @@ CAI_GCTR_NITERATIONS="5"             # For bug_bounty_gctr agent
 ```
 **Specialized Agent Variables:**
 - `CAI_GCTR_NITERATIONS`: Controls Cut-The-Rope analysis frequency in GCTR agents
 - `CAI_SUPPORT_MODEL`: Meta-agent for strategic planning
 - `CAI_PARALLEL`: Swarm-style parallel agent execution
 **Related Documentation:**
 - [Agents Documentation](agents.md)
 - [Teams & Parallel Execution](tui/teams_and_parallel_execution.md)
@ -274,11 +276,13 @@ CTF_INSIDE="true"                     # Auto-set CAI_ACTIVE_CONTAINER on CTF sta
 ```
 **Container Execution:**
 - When `CAI_ACTIVE_CONTAINER` is set, all shell commands execute inside that container
 - Automatically configured when starting CTF challenges with `CTF_INSIDE=true`
- Switch containers using `/virtualization` command in CLI
+- Switch containers using `/virtualization` or `/virt` in the REPL
 **Related Documentation:**
 - [Commands Reference](cai/getting-started/commands.md)
 ---
@ -289,7 +293,8 @@ For Terminal User Interface features and workflows:
 ```bash
 # TUI display
-CAI_STREAM="true"                     # Enable streaming in TUI panels
+CAI_STREAM="true"                     # Enable LLM inference streaming in TUI panels
 CAI_TOOL_STREAM="true"                # Enable tool output streaming (default)
 CAI_BRIEF="false"                     # Full output for interactive sessions
 # TUI workflows
@ -298,16 +303,52 @@ CAI_GUARDRAILS="false"                # Consider enabling for team workflows
 ```
 **TUI Recommendations:**
- Set `CAI_STREAM=true` for better interactive experience
+
 - Set `CAI_STREAM=true` for better interactive LLM response experience
 - Keep `CAI_TOOL_STREAM=true` (default) to see command output in real-time
 - Use built-in Teams feature instead of `CAI_PARALLEL`
 - Enable `CAI_GUARDRAILS` when coordinating multiple agents
 **Related Documentation:**
 - [TUI Documentation](tui/tui_index.md)
 - [TUI Getting Started](tui/getting_started.md)
 ---
 ### 🐛 Debugging & Development
 For debugging CAI internals and development:
 ```bash
 # Debug levels
 CAI_DEBUG="1"                         # 0: minimal, 1: verbose, 2: CLI debug
 # Tool visualization debugging
 CAI_DEBUG_TOOLS_VIZ="true"            # Debug tool panel rendering and deduplication
 # Cache and message debugging
 CAI_SHOW_CACHE="true"                 # Show cache stats and full message history list
 # Pricing debugging
 CAI_DEBUG_PRICING="1"                 # Log pricing calculations to debug_pricing.txt
 ```
 **Debug Variables Explained:**
 - `CAI_DEBUG`: General debug output level (0-2)
 - `CAI_DEBUG_TOOLS_VIZ`: Shows detailed info about tool call display, panel rendering, streaming state, and deduplication logic
 - `CAI_SHOW_CACHE`: Displays prompt caching statistics and the complete message list sent to the model. Useful for debugging context issues
 - `CAI_DEBUG_PRICING`: Writes detailed pricing calculations to `debug_pricing.txt` for cost analysis
 **When to Use:**
 - Use `CAI_DEBUG_TOOLS_VIZ=true` when tool outputs are not displaying correctly or duplicating
 - Use `CAI_SHOW_CACHE=true` when debugging context window issues or cache behavior
 - Use `CAI_DEBUG_PRICING=1` when investigating cost discrepancies
 ---
 ## 💡 Common Configuration Examples
 ### Example 1: Local Development with Ollama
@ -327,7 +368,7 @@ CTF_NAME="hackthebox_challenge"
 CTF_INSIDE="true"
 CAI_MODEL="alias1"
 CAI_STATE="true"
-CAI_MEMORY="all"
+CAI_COMPACTED_MEMORY="true"
 CAI_GUARDRAILS="true"
 CAI_PRICE_LIMIT="5"
 ```
@ -350,7 +391,8 @@ CAI_MODEL="alias0-fast"
 CAI_PARALLEL="5"
 CAI_BRIEF="true"
 CAI_MAX_TURNS="20"
-CAI_STREAM="false"
+CAI_STREAM="false"                    # LLM inference streaming off
 CAI_TOOL_STREAM="false"               # Tool streaming off for parallel (auto-disabled)
 ```
 ---
@ -386,6 +428,7 @@ See the [Configuration Guide](cai/getting-started/configuration.md) for more det
 There are three ways to configure environment variables:
 **1. `.env` file (Recommended)**
 ```bash
 # Add to .env file
 CAI_MODEL="alias1"
@ -393,11 +436,11 @@ CAI_PRICE_LIMIT="1"
 ```
 **2. Command-line**
 ```bash
 CAI_MODEL="gpt-4o" CAI_PRICE_LIMIT="2" cai
 ```
 **3. Runtime configuration**
-Use slash commands during a session: `/env`, `/env list`, `/env set …`, and the in-session help above (`/help`, `/help var …`). See [Commands Reference](cai/getting-started/commands.md) and [CLI Commands Reference](cli/commands_reference.md).
+Use slash commands during a session: `/env list`, `/env set …`, and the in-session help above (`/help`, `/help var …`). See [Commands Reference](cai/getting-started/commands.md).
--- a/docs/index.md
+++ b/docs/index.md
@ -35,7 +35,6 @@ A lightweight, ergonomic framework for building bug bounty-ready Cybersecurity A
 - ✅ **Unlimited `alias1` tokens** - Our state-of-the-art cybersecurity model that beats GPT-5 in benchmarks
 - ✅ **Mobile User Interface (iOS)** - Native iOS app for security testing on the go - **[Join TestFlight Beta](https://testflight.apple.com/join/nXZZD4Z5)**
 - ✅ **Terminal User Interface (TUI)** - Multi-agent parallel execution with visual monitoring *(Deprecated - Use Mobile UI)*
 - ✅ **Usage visibility** - `/cost`, compaction, and TUI cost indicators
 - ✅ **Zero Refusals** - Unrestricted AI specifically trained for offensive security
 - ✅ **European Hosting** - GDPR & NIS2 compliant with guaranteed data privacy
 - ✅ **Professional Support** - Dedicated technical assistance from security experts
@ -118,7 +117,7 @@ CAI's capabilities are validated through rigorous peer-reviewed research demonst
 ## Motivation
 ### Why CAI?
-The cybersecurity landscape is undergoing a dramatic transformation as AI becomes increasingly integrated into security operations. **We predict that by 2028, AI-powered security testing tools will outnumber human pentesters**. This shift represents a fundamental change in how we approach cybersecurity challenges. *AI is not just another tool - it's becoming essential for addressing complex security vulnerabilities and staying ahead of sophisticated threats. As organizations face more advanced cyberattacks, AI-enhanced security testing will be crucial for maintaining robust defenses.*
+The cybersecurity landscape is undergoing a dramatic transformation as AI becomes increasingly integrated into security operations. **We predict that by 2028, AI-powered security testing tools will outnumber human pentesters**. This shift represents a fundamental change in how we approach cybersecurity challenges. *AI is not just another tool - it's becoming essential for addressing complex security vulnerabilities and staying ahead of sophisticated threats. As organizations face more advanced cyber attacks, AI-enhanced security testing will be crucial for maintaining robust defenses.*
 This work builds upon prior efforts[1] and similarly, we believe that democratizing access to advanced cybersecurity AI tools is vital for the entire security community. That's why we're releasing Cybersecurity AI (`CAI`) as an open source framework. Our goal is to empower security researchers, ethical hackers, and organizations to build and deploy powerful AI-driven security tools. By making these capabilities openly available, we aim to level the playing field and ensure that cutting-edge security AI technology isn't limited to well-funded private companies or state actors.
--- a/docs/mcp.md
+++ b/docs/mcp.md
@ -29,17 +29,16 @@ async with MCPServerStdio(
 ## Using MCP servers
-MCP servers can be added to Agents. The Agents SDK will call `list_tools()` on the MCP servers each time the Agent is run. This makes the LLM aware of the MCP server's tools. When the LLM calls a tool from an MCP server, the SDK calls `call_tool()` on that server.
+MCP servers can be added to agents. The runner collects tools from each server (via `list_tools()`) when the agent runs, so the model can call them; each invocation uses the server's `call_tool()`.
 ```python
 ```python
 from cai.sdk.agents import Agent
 # mcp_server_1 and mcp_server_2 are connected MCPServerStdio / MCPServerSse instances.
 cybersecurity_lead = Agent(
    name="Cybersecurity Lead Agent",
-    instructions="Use the tools to solve the",
+    instructions="Use the tools to solve the task.",
-    mcp_servers=[mcp_server_1, mcp_server_2]
+    mcp_servers=[mcp_server_1, mcp_server_2],
 )
 ```
@ -51,7 +50,7 @@ If you want to invalidate the cache, you can call `invalidate_tools_cache()` on
 ## End-to-end examples
-View complete working examples at [examples/mcp](https://github.com/openai/openai-agents-python/tree/main/examples/mcp).
+See the `examples/mcp/` directory in the CAI repository for runnable scripts (stdio and SSE patterns).
 ## Tracing   
--- a/docs/mui/chat_features.md
+++ b/docs/mui/chat_features.md
@ -283,6 +283,7 @@ Save frequently used code:
 **Send Options**
 - Enter to send
 - Shift+Enter for new line
 - Alt+Enter for new line (terminal fallback)
 - Send button confirmation
 - Draft auto-save
--- a/docs/multi_agent.md
+++ b/docs/multi_agent.md
@ -22,139 +22,16 @@ While orchestrating via LLM is powerful, orchestrating via code makes tasks more
 - Using structured outputs to generate well formed data that you can inspect with your code. 
- Using a deterministic pattern: Breaking down a task into a series of smaller steps. Chaining multiple agents, each step can be performed by an agent, and the output of one agent is used as input to the next. 
+- Using a determinitstic pattern: Breaking down a task into a series of smaller steps. Chaining multiple agents, each step can be performed by an agent, and the output of one agent is used as input to the next. 
 - Using [Guardrails](guardrails.md) and LLM_as_judge: They are agents that evaluates and provides feedback, until they says the inputs/outputs passes certain criteria. The agent ensures inputs/outputs are appropriate.
- Parallelization of task: Running multiple agents in parallel. This is useful for speed when you have multiple tasks.
+- Paralelization of task: Running multiple agents in parallel. This is useful for speed when you have multiple tasks that don't depend on each other.
-## Running Agents in Parallel
+### CLI: `orchestration_agent` vs `selection_agent`
-When you have multiple tasks, you can run agents in parallel to improve performance and reduce overall execution time. This is particularly useful in security workflows where you need to perform multiple reconnaissance or analysis tasks simultaneously.
+In the CAI REPL, **`CAI_AGENT_TYPE`** defaults to **`orchestration_agent`**: a single entry agent that can stay in control while spawning **specialist workers** via tools (`run_specialist`, `run_dual_approach_contest`, `run_parallel_specialists`). Worker subprocesses each get their own `Runner` turn budget from **`CAI_ORCHESTRATION_WORKER_MAX_TURNS`** (1–32, default 6). Optionally, **`CAI_ORCHESTRATION_MAS_HINT`** (default `true`) adds at most one synthetic English user-line per top-level run when the user message looks multi-front but delegation stayed on a single specialist—so the model can consider parallel scouts or a contest.
-You have two options:
+**`selection_agent`** is an alternative entry profile: **handoffs only** to other agents, without those orchestration tools. Use **`/agent list`** / **`/agent select`** or **`/help agent`** for the live list and short routing notes; long-form env help: **`/help var CAI_AGENT_TYPE`**, **`/help var CAI_ORCHESTRATION_WORKER_MAX_TURNS`**, **`/help var CAI_ORCHESTRATION_MAS_HINT`**.
 1. **Use built-in parallel patterns** (available via `/agent list`)
 2. **Create your own custom pattern** using `agents.yml` configuration
 ### Option 1: Using Built-in Parallel Patterns
 CAI includes ready-to-use parallel patterns that you can select directly from the CLI.
 **View available patterns:**
 ```bash
 # Launch CAI and list all available patterns
 cai
 CAI> /agent list
 ```
 **Available parallel patterns:**
 | Pattern Name | Agents | Context | Description |
 |--------------|--------|---------|-------------|
 | **offsec_pattern** | redteam_agent + bug_bounter_agent | Split | Bug bounty and red team with different contexts for offensive security ops |
 | **blue_team_red_team_shared_context** | redteam_agent + blueteam_agent | Shared | Red and blue team agents sharing the same message history |
 | **blue_team_red_team_split_context** | redteam_agent + blueteam_agent | Split | Red and blue team agents with separate contexts for independent analysis |
 | **purple_team_gctr** ⭐ | redteam_agent + blueteam_agent (enhanced with G-CTR) | Shared | Combines red and blue team agents with shared GCTR tracking for unified game-theoretic analysis (⭐ this is a [CAI PRO](https://aliasrobotics.com/cybersecurityai.php) capability)|
 **To use a pattern:**
 ```bash
 # Start CAI and select a pattern
 cai
 # List available patterns
 CAI> /agent list
 # Select a parallel pattern by number or name
 CAI> /agent 23
 # or
 CAI> /agent offsec_pattern
 # Now enter your prompt and both agents will work in parallel
 CAI> Analyze https://example.com for vulnerabilities
 ```
 **How parallel patterns work:**
 - **Split context**: Each agent has its own message history and works independently
 - **Shared context**: Both agents see the same message history and can build on each other's work
 **Example workflow with offsec_pattern:**
 ```bash
 CAI> /agent offsec_pattern
 CAI> Find vulnerabilities in https://target.com
 # Both redteam_agent and bug_bounter_agent will analyze the target
 # Each provides their perspective (red team exploitation vs bug bounty)
 # You get results from both agents in parallel
 ```
 ### Option 2: Create Your Own Pattern with agents.yml
 For a simpler approach, use the `agents.yml` configuration file to run multiple agents in parallel without writing Python code.
 **1. Copy the example configuration:**
 ```bash
 cp agents.yml.example agents.yml
 ```
 **2. Configure your parallel agents in `agents.yml`:**
 **Example with unified context:**
 ```yaml
 parallel_agents:
  # Define 2 or more agents to run in parallel
  - name: one_tool_agent
    model: alias1
    prompt: "Focus on finding vulnerabilities"
    unified_context: false  # Each agent has its own message history
  - name: blueteam_agent
    model: alias1
    prompt: "Focus on defensive security"
    unified_context: false
 ```
 **Example with Shared context:**
 ```yaml
 parallel_agents:
  - name: redteam_agent
    unified_context: true  # Agents share message history
  - name: blueteam_agent
    unified_context: true  # Can see what redteam agent did
 ```
 **3. Launch CAI:**
 ```bash
 # Auto-loads agents.yml from current directory
 cai
 # Or load a different configuration file
 cai --yaml agent_custom.yml
 # Or specify a full path
 cai --yaml /path/to/my_agents.yml
 ```
 **How it works:**
 - When 2 or more agents are configured, parallel mode is automatically enabled
 - The agents will be available for selection when you enter a prompt
 - Each agent can have its own model, prompt, and context settings
 **Configuration options:**
 - `name`: The agent type (e.g., `redteam_agent`, `bug_bounter_agent`)
 - `model`: Optional model override (e.g., `alias1`, `alias0`)
 - `prompt`: Optional additional instructions for the agent
 - `unified_context`: Set to `true` to share message history between agents (default: `false`)
 This is separate from **`/parallel`** (multiple REPL slots with **`CAI_PARALLEL`**), which runs independent agent instances side by side.
--- a/docs/other_cli/claude_code.md
+++ b/docs/other_cli/claude_code.md
@ -1,105 +0,0 @@
 # Claude Code
 !!! danger "Third-Party Scaffolding — Privacy & Security Warning"
    Third-party scaffoldings may be serving your data outside your environment.
    For **privacy** and **cybersecurity refusal optimization**, use **CAI** to obtain the best performance.
 !!! warning "Support Disclaimer"
    Alias Robotics **does not provide support** for developments or integrations related to Claude Code. This page documents API compatibility only. Alias simply allows usage of the Alias API through your preferred scaffolding.
 ---
 ## Step 1: Install Claude Code
 ### Prerequisites
 - **Node.js 18** or newer
 - For **macOS**, use [nvm](https://github.com/nvm-sh/nvm) to install Node.js — installing the package directly may cause permission issues
 - For **Windows**, additionally install [Git for Windows](https://gitforwindows.org/)
 ```bash
 # Install Claude Code
 npm install -g @anthropic-ai/claude-code
 # Navigate to your project
 cd your-awesome-project
 # Launch
 claude
 ```
 !!! note
    If macOS users encounter permission issues during installation, use `nvm` to install Node.js.
 ---
 ## Step 2: Configure the Alias API
 ### 1. Get your Alias API Key
 An `ALIAS_API_KEY` (format: `sk-...`) can be obtained from either of the following:
 - **[CAI PRO](https://aliasrobotics.com/cybersecurityai.php)** — full cybersecurity AI platform with access to `alias1` and other models.
 - **[Alias LLMs](https://aliasrobotics.com/aliasLLMs.php)** — acquire `alias2-mini` and other Alias language models directly.
 ### 2. Configure Environment Variables
 Set up environment variables using one of the following methods for macOS/Linux or Windows.
 !!! note
    Some commands show no output when setting environment variables — that's normal as long as no errors appear. A new terminal window may be required for the changes to take effect.
 #### macOS & Linux
 Edit the Claude Code configuration file `~/.claude/settings.json`. Add or modify the `env` fields `ANTHROPIC_BASE_URL` and `ANTHROPIC_AUTH_TOKEN`.
 Replace `your_alias_api_key` with the API Key you obtained in the previous step.
 ```json
 {
    "env": {
        "ANTHROPIC_AUTH_TOKEN": "your_alias_api_key",
        "ANTHROPIC_BASE_URL": "https://api.aliasrobotics.com:666/",
        "API_TIMEOUT_MS": "3000000"
    }
 }
 ```
 #### Windows Cmd
 Run the following commands in Cmd. Replace `your_alias_api_key` with the API Key you obtained in the previous step.
 ```cmd
 setx ANTHROPIC_AUTH_TOKEN your_alias_api_key
 setx ANTHROPIC_BASE_URL https://api.aliasrobotics.com:666/
 ```
 #### Windows PowerShell
 Run the following commands in PowerShell. Replace `your_alias_api_key` with the API Key you obtained in the previous step.
 ```powershell
 [System.Environment]::SetEnvironmentVariable('ANTHROPIC_AUTH_TOKEN', 'your_alias_api_key', 'User')
 [System.Environment]::SetEnvironmentVariable('ANTHROPIC_BASE_URL', 'https://api.aliasrobotics.com:666/', 'User')
 ```
 ---
 ## Step 3: Start with Claude Code
 Once the configuration is complete, start using Claude Code in your terminal:
 ```bash
 cd your-project-directory
 claude
 ```
 Token usage and billing will appear in your Alias account.
 ---
 ## Related
 - [CAI PRO Quickstart](../cai_pro_quickstart.md)
 - [Available Models](../cai_list_of_models.md)
 - [Environment Variables](../environment_variables.md)
--- a/docs/other_cli/codex.md
+++ b/docs/other_cli/codex.md
@ -1,72 +0,0 @@
 # Codex CLI
 !!! danger "Third-Party Scaffolding — Privacy & Security Warning"
    Third-party scaffoldings may be serving your data outside your environment.
    For **privacy** and **cybersecurity refusal optimization**, use **CAI** to obtain the best performance.
 [Codex CLI](https://github.com/openai/codex) is OpenAI's open-source terminal-based AI coding agent. Because it uses the **OpenAI API format** natively, it can connect directly to the Alias API without any proxy.
 !!! warning "Support Disclaimer"
    Alias Robotics **does not provide support** for developments or integrations related to Codex CLI. This page documents API compatibility only. Alias simply allows usage of the Alias API through your preferred scaffolding.
 ---
 ## Setup
 ### 1. Get your Alias API Key
 An `ALIAS_API_KEY` (format: `sk-...`) can be obtained from either of the following:
 - **[CAI PRO](https://aliasrobotics.com/cybersecurityai.php)** — full cybersecurity AI platform with access to `alias1` and other models.
 - **[Alias LLMs](https://aliasrobotics.com/aliasLLMs.php)** — acquire `alias2-mini` and other Alias language models directly.
 ### 2. Install Codex CLI
 ```bash
 npm install -g @openai/codex
 ```
 ### 3. Configure environment variables
 ```bash
 export OPENAI_API_KEY="sk-your-alias-api-key-here"
 export OPENAI_BASE_URL="https://api.aliasrobotics.com:666/"
 ```
 To persist these, add them to your shell profile (`~/.zshrc`, `~/.bashrc`, etc.):
 ```bash
 echo 'export OPENAI_API_KEY="sk-your-alias-api-key-here"' >> ~/.zshrc
 echo 'export OPENAI_BASE_URL="https://api.aliasrobotics.com:666/"' >> ~/.zshrc
 source ~/.zshrc
 ```
 ### 4. Run Codex with an Alias model
 ```bash
 codex --model alias1
 ```
 Or set the model inline per session:
 ```bash
 OPENAI_API_KEY="sk-your-alias-api-key-here" \
 OPENAI_BASE_URL="https://api.aliasrobotics.com:666/" \
 codex --model alias1
 ```
 ---
 ## Notes
 - The Alias API is fully OpenAI-compatible — no additional configuration is required beyond pointing the base URL and API key.
 - Use `alias1` for best cybersecurity performance, or `alias0` for a faster, lighter alternative.
 - Token usage and billing appear in your Alias account dashboard.
 ---
 ## Related
 - [CAI PRO Quickstart](../cai_pro_quickstart.md)
 - [Available Models](../cai_list_of_models.md)
 - [Environment Variables](../environment_variables.md)
--- a/docs/other_cli/opencode.md
+++ b/docs/other_cli/opencode.md
@ -1,91 +0,0 @@
 # OpenCode
 !!! danger "Third-Party Scaffolding — Privacy & Security Warning"
    Third-party scaffoldings may be serving your data outside your environment.
    For **privacy** and **cybersecurity refusal optimization**, use **CAI** to obtain the best performance.
 [OpenCode](https://opencode.ai) is an open-source, terminal-based AI coding assistant. It supports OpenAI-compatible providers, which means the Alias API can be plugged in directly without any proxy.
 !!! warning "Support Disclaimer"
    Alias Robotics **does not provide support** for developments or integrations related to OpenCode. This page documents API compatibility only. Alias simply allows usage of the Alias API through your preferred scaffolding.
 ---
 ## Setup
 ### 1. Get your Alias API Key
 An `ALIAS_API_KEY` (format: `sk-...`) can be obtained from either of the following:
 - **[CAI PRO](https://aliasrobotics.com/cybersecurityai.php)** — full cybersecurity AI platform with access to `alias1` and other models.
 - **[Alias LLMs](https://aliasrobotics.com/aliasLLMs.php)** — acquire `alias2-mini` and other Alias language models directly.
 ### 2. Install OpenCode
 ```bash
 npm install -g opencode-ai
 ```
 Or via Homebrew (macOS):
 ```bash
 brew install sst/tap/opencode
 ```
 ### 3. Configure the Alias provider
 OpenCode uses a `~/.config/opencode/config.json` file. Add a custom OpenAI-compatible provider pointing to the Alias API:
 ```json
 {
  "provider": {
    "alias": {
      "api": "https://api.aliasrobotics.com:666/",
      "name": "Alias Robotics",
      "env": ["ALIAS_API_KEY"]
    }
  },
  "model": "alias/alias1"
 }
 ```
 Then export your key:
 ```bash
 export ALIAS_API_KEY="sk-your-alias-api-key-here"
 ```
 ### 4. Run OpenCode
 ```bash
 opencode
 ```
 OpenCode will pick up the configured provider and route requests to the Alias API.
 ---
 ## Alternative: environment variable approach
 If you prefer not to edit the config file, OpenCode also respects standard OpenAI environment variables:
 ```bash
 export OPENAI_API_KEY="sk-your-alias-api-key-here"
 export OPENAI_BASE_URL="https://api.aliasrobotics.com:666/"
 opencode --model alias1
 ```
 ---
 ## Notes
 - Use `alias1` for best cybersecurity performance, or `alias0` for a faster, lighter alternative.
 - Token usage and billing appear in your Alias account dashboard.
 ---
 ## Related
 - [CAI PRO Quickstart](../cai_pro_quickstart.md)
 - [Available Models](../cai_list_of_models.md)
 - [Environment Variables](../environment_variables.md)
--- a/docs/providers/azure.md
+++ b/docs/providers/azure.md
@ -1,84 +0,0 @@
 # Azure OpenAI configuration
 > This guide shows how to run CAI against Azure-hosted OpenAI's models
 ## Prerequisites
 - Azure subscription with **Azure OpenAI** access.
 - A **deployed model** in Azure AI Portal (e.g., a deployment named `gpt-4o`).  
  See Microsoft docs on creating the resource & deploying models.  
  - [Create resource & deploy](https://learn.microsoft.com/azure/ai-foundry/openai/how-to/create-resource)
  - [Working with models](https://learn.microsoft.com/azure/ai-foundry/openai/how-to/working-with-models)
 #### 1. Deploy the base model
 In Azure AI Portal, go to **Deployments** and deploy the requested base model (e.g., gpt-4o).
 #### 2. Get the deployment URL
 From **Deployments**, select your deployment and copy the endpoint in this form: 
 `https://<your-resource>.openai.azure.com/openai/deployments/<deployment-name>/chat/completions?api-version=2025-01-01-preview`
 Set this value as `AZURE_API_BASE` in your `.env`.  
 **Note:** CAI uses the OpenAI SDK style `base_url + /chat/completions`. For Azure, providing the full endpoint above (including `chat/completions?api-version=...`) ensures correct routing.
 #### 3. Get your API key
 From your Azure OpenAI resource home page (it is displayed on the resource home page, along with the subscription ID, resource name, etc.). Put it in `.env` as `AZURE_API_KEY`.
 #### 4. Complete your `.env`
 `OPENAI_API_KEY` must NOT be empty (use any placeholder like `"dummy"`). 
 Example of good configured `.env`:
 ```bash
 OPENAI_API_KEY="dummy"
 AZURE_API_KEY="your_subscription_api_key"
 AZURE_API_BASE="https://<your-resource>.openai.azure.com/openai/deployments/<deployment-name>/chat/completions?api-version=2025-01-01-preview"
 # Optional (if your setup expects it):
 # AZURE_API_VERSION="2025-01-01-preview"
 ANTHROPIC_API_KEY=""
 OLLAMA=""
 PROMPT_TOOLKIT_NO_CPR=1
 ```
 #### 5. Start CAI and select the model
 Launch CAI and select the Azure model:
 ```vbnet
 CAI> /model azure/<model-name>
 ╭─────────────────────────────────────────────────── Model Changed ────────────────────────────────────────────────────╮
 │ Model changed to: azure/<model-name>                                                                               │
 │ Note: This will take effect on the next agent interaction                                                                │
 ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
 ```
 From this point you are interacting with your Azure-hosted OpenAI model.
 > ⚠️ Remember: you must select the model each time you start CAI.
 > EXTRA configuration:
 You can set the variable `CAI_MODEL` to avoid the need for repeated model setup during initialization.
 ```bash
 CAI_MODEL=azure/<model-name-deployed>
 ```
 ## Troubleshooting
 - 404 or “deployment not found”: Ensure you have correctly copied the URL of the deployed model.
 Error example:
 ```sh
 ERROR:cai.cli:Error in main loop: litellm.APIError: AzureException APIError - Resource not found
 openai.NotFoundError: Error code: 404 - {'error': {'code': '404', 'message': 'Resource not found'}}
 ```
 - 401: verify `AZURE_API_KEY` and that your region has access to the chosen model.
 Error example:
 ```sh
 ERROR:cai.cli:Error in main loop: litellm.AuthenticationError: AzureException AuthenticationError - Access denied due to invalid subscription key or wrong API endpoint. Make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.
 openai.AuthenticationError: Error code: 401 - {'error': {'code': '401', 'message': 'Access denied due to invalid subscription key or wrong API endpoint. Make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.'}}
 ```
 - Time-outs / rate limits: check Azure usage and quota.
--- a/docs/providers/lm_studio
+++ b/docs/providers/lm_studio
@ -1,11 +0,0 @@
 # LM Studio Configuration
 #### [LM Studio Integration](https://lmstudio.ai/)
 For local models using LM Studio, add the following to your .env:
 ```bash
 CAI_MODEL=deepseek-r1-0528-qwen3-8b #do not use the prefix, just the model name eg. deepseek/deepseek-r1-0528-qwen3-8b should be deepseek-r1-0528-qwen3-8b
 OLLAMA_API_BASE=http://localhost:1234/v1 # note, maybe you have a different endpoint (1234 is the default value for LM Studio)
 ```
 Make sure that the Ollama server is running and accessible at the specified base URL. You can swap the model with any other supported by your local Ollama instance.
--- a/docs/providers/ollama_cloud.md
+++ b/docs/providers/ollama_cloud.md
@ -25,7 +25,7 @@ cai
 ## Available Models
-View in CAI with `/model show` under "Ollama Cloud" category:
+View in CAI with `/model show` (predefined list includes Ollama Cloud models):
 - `ollama_cloud/gpt-oss:120b` - General purpose 120B model
 - `ollama_cloud/llama3.3:70b` - Llama 3.3 70B
--- a/docs/providers/openrouter.md
+++ b/docs/providers/openrouter.md
@ -1,11 +0,0 @@
 # OpenRouter Configuration
 #### [OpenRouter Integration](https://openrouter.ai/)
 To enable OpenRouter support in CAI, you need to configure your environment by adding specific entries to your `.env` file. This setup ensures that CAI can interact with the OpenRouter API, facilitating the use of sophisticated models like Meta-LLaMA. Here’s how you can configure it:
 ```bash
 CAI_MODEL=openrouter/meta-llama/llama-4-maverick
 OPENROUTER_API_KEY=<sk-your-key>  # note, add yours
 OPENROUTER_API_BASE=https://openrouter.ai/api/v1
 ```
--- a/docs/queue_file_feature.md
+++ b/docs/queue_file_feature.md
@ -0,0 +1,92 @@
 # CAI Queue File Feature
 ## Overview
 CAI now supports automatically loading and executing a queue of prompts from a text file on startup. This is useful for batch processing, automated workflows, or pre-loading common tasks. The prompts will be executed automatically one by one without requiring user interaction.
 ## Usage
 ### 1. Set the Environment Variable
 ```bash
 export CAI_QUEUE_FILE="/path/to/your/prompts.txt"
 ```
 ### 2. Create a Queue File
 Create a text file with one prompt per line:
 ```text
 # Comments start with # and are ignored
 /help
 What are your cybersecurity capabilities?
 /agent list
 Scan the network 192.168.1.0/24
 Check for vulnerabilities in https://example.com
 $ nmap -sV localhost
 /history
 Generate a security report
 ```
 ### 3. Start CAI
 The prompts will be automatically loaded and executed when you start CAI:
 ```bash
 # Regular mode - prompts run automatically
 CAI_QUEUE_FILE=~/my_prompts.txt cai
 # TUI mode - prompts run automatically
 CAI_QUEUE_FILE=~/my_prompts.txt cai --tui
 ```
 CAI will:
 1. Load all prompts from the file
 2. Display a message showing how many prompts were loaded
 3. Automatically start processing each prompt in order
 4. Return to normal interactive mode when the queue is empty
 ### 4. Manual Loading
 You can also load a queue file manually using the queue command:
 ```bash
 CAI> /queue load ~/another_prompts.txt
 ```
 ## Features
 - **Auto-loading**: Queue file is loaded automatically on startup if `CAI_QUEUE_FILE` is set
 - **Auto-execution**: Prompts are executed automatically in sequence without user interaction
 - **Comments**: Lines starting with `#` are ignored
 - **Empty lines**: Blank lines are skipped
 - **Any prompt type**: Supports commands (`/help`), shell commands (`$ ls`), bare `?` (CLI headless input shortcuts), and regular prompts
 - **Works in both modes**: Compatible with regular CLI and TUI modes
 - **Seamless transition**: Returns to interactive mode after queue is processed
 ## Example Queue File
 ```text
 # Security Assessment Workflow
 # First, check the environment
 /agent list
 /model
 # Network reconnaissance
 Scan the network 192.168.1.0/24 for open ports
 $ nmap -sV -p- 192.168.1.1
 # Web application testing
 Check https://example.com for common vulnerabilities
 Test for SQL injection on the login form
 Analyze the SSL/TLS configuration
 # Reporting
 Generate a comprehensive security report
 /history
 ```
 ## Tips
 - Use queue files to standardize workflows across team members
 - Create different queue files for different types of assessments
 - Combine with parallel mode for concurrent execution
 - Queue items are processed in order, one at a time
 - Add `/exit` at the end to quit CAI after processing
 - You can interrupt processing at any time with Ctrl+C
 - The queue continues from where it left off if you resume
--- a/docs/session_resume.md
+++ b/docs/session_resume.md
@ -1,355 +0,0 @@
 # Session Resume
 ## Overview
 CAI provides powerful session resume capabilities that allow you to continue where you left off. Whether you were in the middle of a security audit, bug bounty session, or complex analysis, you can seamlessly restore your conversation history and pick up exactly where you stopped.
 The session resume system automatically saves all your interactions to JSONL log files and provides multiple ways to restore them:
 - **`--resume`**: Resume from specific session or interactive selector
 - **`--resume --continue`**: Resume AND continue autonomously
 - **Interactive Selector**: Visual session browser with pagination
 ## Quick Start
 ```bash
 # Resume the last session
 cai --resume
 # Resume the last session and continue autonomously
 cai --resume --continue
 # Interactive session selector
 cai --resume list
 # Resume a specific session by ID
 cai --resume abc12345
 # Resume from a specific log file
 cai --resume /path/to/session.jsonl
 ```
 ## Session Resume Options
 ### Resume Last Session
 ```bash
 cai --resume
 # or
 cai --resume last
 ```
 This automatically finds and loads the most recent session that contains messages. Empty sessions are skipped.
 ### Interactive Session Selector
 ```bash
 cai --resume list
 ```
 Opens an interactive menu with:
 - **Arrow key navigation** (`↑`/`↓` or `j`/`k`)
 - **Page navigation** (`←`/`→` or `h`/`l`)
 - **Session preview** showing last assistant response
 - **Cost and token tracking** per session
 - **Model information** for each session
 ```
 ╭──────────────────────────────────────────────────────────────────────────────╮
 │  ↻ Select a session to resume                                                │
 │  ↑/↓/j/k navigate  │  ←/→/h/l pages  │  Enter select  │  q/Esc cancel       │
 ╰──────────────────────────────────────────────────────────────────────────────╯
   Page 1+ │ 10 sessions │ → next
   ID       │ Date       │ Model        │ Msgs    │ Cost
   ─────────┼────────────┼──────────────┼─────────┼────────
 ❯ abc12345 │ 01-12 15:30 │ claude-sonnet │  42 msgs │   $2.35 ★ LATEST
   def67890 │ 01-12 14:15 │ gpt-4         │  28 msgs │   $1.80
   ghi11223 │ 01-11 20:00 │ claude-opus   │ 156 msgs │  $12.50
   ──────────────────────────────────────────────────────────────────
   Preview:
   The vulnerability analysis is complete. I found 3 critical issues:
   1. SQL injection in user.py line 45...
 ```
 ### Resume with Continue Mode
 ```bash
 cai --resume --continue
 # or
 cai --resume -c
 ```
 This powerful combination:
 1. **Restores your previous session** with full conversation history
 2. **Automatically generates a continuation prompt** based on context
 3. **Continues working autonomously** without waiting for user input
 Perfect for:
 - Resuming long-running security audits
 - Continuing interrupted penetration tests
 - Picking up complex analysis tasks
 ### Resume Specific Session
 ```bash
 # By session ID (first 8 characters)
 cai --resume abc12345
 # By full log file path
 cai --resume logs/cai_20240112_153045.jsonl
 # From custom logs directory
 cai --resume my_session --logpath ~/custom_logs/
 ```
 ## What Gets Restored
 When you resume a session, CAI restores:
 | Component | Description |
 |-----------|-------------|
 | **Message History** | All user messages and agent responses |
 | **Tool Calls** | Complete record of tools used and their outputs |
 | **Agent Context** | The agent's understanding of the task |
 | **Session Statistics** | Total cost, tokens used, active time |
 | **Parallel Agent Config** | Multi-agent configurations (if applicable) |
 ### Session Statistics Display
 ```
 ↻ Resuming session
 claude-sonnet │ Tokens: 45,230in/12,450out │ $3.45 │ 25.3s active
 [Session content displayed here...]
 Session restored. Continue where you left off.
 Restored session stats: $3.4500, 45230in/12450out tokens
 Loaded 156 messages into agent history
 ```
 ## Custom Logs Directory
 Use `--logpath` to work with sessions stored in custom directories:
 ```bash
 # Resume from custom directory
 cai --resume list --logpath ~/projects/security_audits/logs/
 # Resume last session from custom directory
 cai --resume --logpath /shared/team_sessions/
 ```
 The `--logpath` option:
 - Recursively searches all subdirectories for `.jsonl` files
 - Works with both `--resume list` and `--resume last`
 - Supports absolute and relative paths
 ## Parallel Agent Sessions
 When resuming a session that used multiple parallel agents, CAI automatically detects and offers to restore the parallel configuration:
 ```
 The session used 3 parallel agents:
  - CTF agent
  - Code Analyzer agent
  - Security Researcher agent
 Set up the same parallel agent configuration? (y/n):
 ```
 If you choose yes, the parallel agent configuration is restored and you can continue working with the same multi-agent setup.
 ## Session Log Format
 Sessions are stored as JSONL (JSON Lines) files in the `logs/` directory:
 ```
 logs/
 ├── last -> cai_20240112_153045.jsonl  # Symlink to most recent
 ├── cai_20240112_153045.jsonl
 ├── cai_20240112_140000.jsonl
 └── cai_20240111_200000.jsonl
 ```
 Each log file contains:
 - Session metadata (ID, timestamps, model info)
 - Complete message history
 - Tool calls and responses
 - Token usage and cost tracking
 - Timing metrics (active/idle time)
 ## Environment Variables
 ```bash
 # Custom default logs directory
 export CAI_LOGS_DIR=~/my_logs
 # Enable debug output for resume operations
 export CAI_DEBUG=2
 ```
 ## Programmatic Usage
 ### Python API
 ```python
 from cai.repl.session_resume import (
    resume_session,
    find_last_session_log,
    interactive_session_selector,
    load_session_into_agent
 )
 # Find and display session
 log_path = find_last_session_log()
 messages, used_path, parallel_agents = resume_session(log_path)
 # Load into agent
 from cai.agents import get_agent_by_name
 agent = get_agent_by_name("ctf_agent")
 load_session_into_agent(agent, messages, log_path=used_path)
 ```
 ### List Recent Sessions
 ```python
 from cai.repl.session_resume import list_recent_sessions
 sessions = list_recent_sessions(limit=10)
 for session in sessions:
    print(f"{session['session_id'][:8]} - {session['model']} - ${session['total_cost']:.2f}")
 ```
 ## Best Practices
 ### 1. Regular Session Checkpoints
 For long-running tasks, the session is automatically saved after each interaction. You can safely interrupt with `Ctrl+C` and resume later.
 ### 2. Descriptive Initial Prompts
 When starting a session you plan to resume later, use descriptive prompts that provide context:
 ```bash
 # Good - Clear context for resumption
 cai --prompt "Security audit of user authentication in project X, focusing on SQL injection and XSS"
 # Less helpful for resumption
 cai --prompt "check auth"
 ```
 ### 3. Use Resume + Continue for Autonomous Work
 ```bash
 # Start a long task
 cai --continue --prompt "comprehensive security audit of the entire codebase"
 # Later, resume and let it continue working
 cai --resume --continue
 ```
 ### 4. Organize Sessions with Custom Paths
 ```bash
 # Keep different projects separate
 cai --prompt "audit project A" --logpath ~/logs/project_a/
 cai --prompt "audit project B" --logpath ~/logs/project_b/
 # Resume specific project
 cai --resume --logpath ~/logs/project_a/
 ```
 ## Troubleshooting
 ### Issue: "No previous session found"
 **Cause**: No valid session logs exist in the logs directory.
 **Solutions**:
 - Check the `logs/` directory exists and contains `.jsonl` files
 - Use `--logpath` to specify a custom directory
 - Ensure previous sessions completed at least one interaction
 ### Issue: Session loads but context seems lost
 **Cause**: The model's context window may be exceeded.
 **Solutions**:
 - Resume with a model that has a larger context window
 - The session will work but older messages may be truncated by the model
 ### Issue: Parallel agents not detected
 **Cause**: The original session may not have used the parallel agent format.
 **Solutions**:
 - Manually configure parallel agents with `/parallel` command after resuming
 - Check that the original session used proper parallel agent configuration
 ### Issue: Cost tracking shows $0.00 after resume
 **Cause**: Session stats couldn't be restored from the log file.
 **Solutions**:
 - This is cosmetic; the actual costs are still in the log file
 - Check log file format is valid JSONL
 ## Technical Details
 ### Session Resume Flow
 ```mermaid
 graph TD
    A[cai --resume] --> B{Resume type?}
    B -->|last| C[Find last session log]
    B -->|list| D[Interactive selector]
    B -->|path/id| E[Find specific session]
    C --> F[Load messages from JSONL]
    D --> F
    E --> F
    F --> G[Display session content]
    G --> H[Restore session stats]
    H --> I[Load into agent history]
    I --> J{--continue flag?}
    J -->|Yes| K[Generate continuation prompt]
    J -->|No| L[Wait for user input]
    K --> M[Auto-continue working]
 ```
 ### Core Components
 | File | Purpose |
 |------|---------|
 | `src/cai/repl/session_resume.py` | Main resume functionality |
 | `src/cai/sdk/agents/run_to_jsonl.py` | JSONL parsing and token stats |
 | `src/cai/cli.py` | CLI integration and `--resume` handling |
 ### Log File Structure
 ```json
 {"event": "session_start", "session_id": "abc12345", "timestamp": "2024-01-12T15:30:45Z"}
 {"object": "chat.completion", "model": "claude-sonnet", "messages": [...], "agent_name": "CTF agent"}
 {"event": "tool_call", "name": "generic_linux_command", "arguments": {...}}
 {"event": "session_end", "cost": {"total_cost": 3.45}, "timing_metrics": {...}}
 ```
 ## Summary
 Session resume in CAI provides:
 - **Seamless continuation** of interrupted work
 - **Full context restoration** including tools and agent state
 - **Interactive session browsing** with preview and filtering
 - **Autonomous resumption** with `--resume --continue`
 - **Multi-agent support** for parallel session restoration
 - **Flexible log management** with custom directories
 Whether you're conducting security audits, running penetration tests, or performing complex analysis, session resume ensures you never lose your progress.
--- a/docs/stylesheets/extra.css
+++ b/docs/stylesheets/extra.css
@ -514,106 +514,3 @@
        grid-template-columns: 1fr;
    }
 }
 /* ====================
   Enhanced Navigation Styling - Refined & Professional
   Only affects left sidebar navigation, not TOC
   ==================== */
 /* Target ONLY the primary (left) sidebar navigation */
 .md-sidebar--primary .md-sidebar__scrollwrap {
    /* Main navigation sections - subtle but clear hierarchy */
 }
 /* Main sections (Getting Started, CAI PRO, User Interfaces, etc.) */
 .md-sidebar--primary .md-nav--primary > .md-nav__list > .md-nav__item > .md-nav__link {
    color: #529d86 !important;
    font-weight: 600 !important;
    font-size: 0.75rem !important;
    text-transform: uppercase;
    letter-spacing: 0.03em;
    padding-top: 0.6rem !important;
    padding-bottom: 0.4rem !important;
    margin-top: 0.3rem;
    border-left: 2px solid transparent;
    transition: all 0.15s ease;
 }
 /* Hover effect for main sections - subtle */
 .md-sidebar--primary .md-nav--primary > .md-nav__list > .md-nav__item > .md-nav__link:hover {
    border-left-color: #529d86;
    background-color: rgba(82, 157, 134, 0.04);
 }
 /* Active main section - slightly more visible */
 .md-sidebar--primary .md-nav--primary > .md-nav__list > .md-nav__item > .md-nav__link--active {
    color: #428072 !important;
    border-left-color: #529d86;
    background-color: rgba(82, 157, 134, 0.06);
    font-weight: 700 !important;
 }
 /* Subsections (TUI, CLI, etc.) - keep normal weight */
 .md-sidebar--primary .md-nav__item--nested > .md-nav__link {
    font-size: 0.7rem !important;
    font-weight: 500 !important;
 }
 /* Regular links - no changes to size */
 .md-sidebar--primary .md-nav__link {
    font-size: 0.7rem;
 }
 /* Active page in navigation - green highlight */
 .md-sidebar--primary .md-nav__link--active {
    color: #529d86 !important;
    font-weight: 500;
 }
 /* Spacing between sections - subtle */
 .md-sidebar--primary .md-nav--primary > .md-nav__list > .md-nav__item {
    margin-bottom: 0.2rem;
 }
 /* Special styling for CAI PRO section - very subtle gradient */
 .md-sidebar--primary .md-nav__item > .md-nav__link[title*="CAI PRO"],
 .md-sidebar--primary .md-nav__item > .md-nav__link[title*="🚀"] {
    background: linear-gradient(90deg, rgba(82, 157, 134, 0.05) 0%, rgba(82, 157, 134, 0.01) 100%);
    border-left-width: 3px;
 }
 /* Table of Contents (right sidebar) - Apply color scheme, keep sizes */
 .md-sidebar--secondary .md-nav__link {
    font-size: inherit !important;
    text-transform: none !important;
    transition: color 0.15s ease;
 }
 /* TOC - Main headings (h1, h2) with green color */
 .md-sidebar--secondary .md-nav__list > .md-nav__item > .md-nav__link {
    color: #529d86 !important;
    font-weight: 600 !important;
 }
 /* TOC - Sub-headings (h3, h4) with darker gray */
 .md-sidebar--secondary .md-nav__list .md-nav__item .md-nav__item > .md-nav__link {
    color: #5a5a5a !important;
    font-weight: 500 !important;
 }
 /* TOC - Hover effect */
 .md-sidebar--secondary .md-nav__link:hover {
    color: #428072 !important;
 }
 /* TOC - Active/current section */
 .md-sidebar--secondary .md-nav__link--active {
    color: #529d86 !important;
    font-weight: 700 !important;
 }
 /* Smooth transitions for better UX */
 .md-sidebar--primary .md-nav__link,
 .md-sidebar--secondary .md-nav__link {
    transition: color 0.15s ease, background-color 0.15s ease, border-color 0.15s ease;
 }
--- a/docs/tui/advanced_features.md
+++ b/docs/tui/advanced_features.md
@ -25,17 +25,17 @@ In-Context Learning allows agents to learn from previous interactions by loading
 **Load a previous session**:
 ```bash
-/load path/to/session.json
+/load path/to/session.jsonl
 ```
 **Load into specific terminal**:
 ```bash
-T2:/load previous_pentest.json
+T2:/load previous_pentest.jsonl
 ```
 **Save current session**:
 ```bash
-/save my_assessment.json
+/save my_assessment.jsonl
 ```
 ### Best Practices
@ -129,11 +129,15 @@ Sessions contain:
 ### Session Commands
 ```bash
-# Save current session
+# Save as JSONL (reload with /load)
-/save assessment_name.json
+/save assessment_name.jsonl
-# Load existing session
+# Save as Markdown (report / sharing; not for /load)
-/load assessment_name.json
+/save assessment_name.md
 # Load JSONL back into the session
 /load assessment_name.jsonl
 ```
 ### Multi-Session Workflows
@ -141,13 +145,13 @@ Combine sessions for complex assessments:
 ```bash
 # Load reconnaissance from previous day
-/load day1_recon.json
+/load day1_recon.jsonl
 # Continue with exploitation
 # ... work ...
 # Save combined results
-/save day2_exploitation.json
+/save day2_exploitation.jsonl
 ```
 ---
--- a/docs/tui/commands_reference.md
+++ b/docs/tui/commands_reference.md
@ -1,13 +1,13 @@
 # CAI TUI Commands Reference
 !!! warning "Documentation freshness"
    This page is **likely out of date**. **Maintained command reference:** **[CLI docs](../cai/getting-started/commands.md)** and **`docs/cai/`** — use those when in doubt.
 > **⚡ CAI-Pro Exclusive Feature**  
 > The Terminal User Interface (TUI) is available exclusively in **CAI-Pro**. To access this feature and unlock advanced multi-agent workflows, visit [Alias Robotics](https://aliasrobotics.com/cybersecurityai.php) for more information.
 ---
 !!! tip "Recommended: use the CLI REPL"
    The command matrix for every `cai` binary flag and REPL slash command is maintained in the **[CLI commands reference](../cli/commands_reference.md)**. **When in doubt—especially for scripting, copy-paste examples, or comparing subcommands—use the plain CLI REPL** and the CLI reference. The TUI shares the same underlying engine; this document focuses on **multi-terminal UX**, keyboard shortcuts, palette actions, and behaviors that differ from the CLI.
 This comprehensive guide documents all commands available in the CAI Terminal User Interface (TUI), including command palette actions, keyboard shortcuts, and CLI-style commands.
 ---
@ -26,40 +26,19 @@ CAI TUI commands are organized into the following categories:
 ---
 ## CLI parity and TUI-only differences
 The [CLI commands reference](../cli/commands_reference.md) is the **authoritative** list for REPL slash commands. The TUI reuses the same slash commands, but a few interactions differ:
 | Situation                                  | CLI REPL                                           | TUI                                                                                                                |
 | ------------------------------------------ | -------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ |
 | `/flush` or `/clear` with **no** arguments | Interactive menu of agents that still have history | Clears history for the **focused terminal only**                                                                   |
 | `/agent` with no arguments                 | Same as `/agent current`                           | Shows the active agent configuration across terminals where applicable                                             |
 | Screen vs history                          | N/A                                                | **Ctrl+L** / visual “clear” only affects scrollback; it does **not** replace `/flush` for wiping message history |
 For binary flags such as `--tui`, `--yaml`, or `--api`, see [Binary `cai` CLI flags](../cli/commands_reference.md#binary-cai-cli-flags).
 ---
 ## Agent Management
 ### `/agent` or `/a`
 Switch between agents or list all available agents.
 !!! note "CLI reference vs TUI examples"
    The CLI reference documents `/agent` with explicit subcommands (`list`, `select`, `info`, `current`, …). In the TUI you will often see the shorthand `/agent <name>`; on many builds that is equivalent to `/agent select <name>`. For exact syntax and flags, follow the **[CLI commands reference](../cli/commands_reference.md#agent-a)**.
 **Syntax**:
 ```
 /agent [agent_name]
 /a [agent_name]
 ```
 **Examples**:
 ```bash
 # List all available agents
 /agent
@ -72,7 +51,6 @@ Switch between agents or list all available agents.
 ```
 **Available Agents**:
 - `redteam_agent` - Offensive security testing and penetration testing
 - `blueteam_agent` - Defensive security analysis and hardening
 - `bug_bounter_agent` - Bug bounty hunting and vulnerability research
@ -97,7 +75,6 @@ Switch between agents or list all available agents.
 - `offsec_pattern` - Offensive security pattern orchestration
 **Notes**:
 - Agent changes are immediate and affect only the active terminal
 - Each terminal can run a different agent simultaneously
 - Agent context is preserved when switching between terminals
@ -111,7 +88,6 @@ Switch between agents or list all available agents.
 CAI TUI uses model dropdowns in each terminal header for model management. Models are configured via environment variables and aliases.
 **Available Models**:
 - `alias1` - Cybersecurity focus model [Recommended]
 - `gpt-4o` - OpenAI GPT-4 Optimized
 - `gpt-4-turbo` - OpenAI GPT-4 Turbo
@ -120,13 +96,11 @@ CAI TUI uses model dropdowns in each terminal header for model management. Model
 - `o1-preview` - OpenAI O1 Preview
 **How to Change Models**:
 1. Click the model dropdown in any terminal header
 2. Select desired model from the list
 3. Model change takes effect immediately for that terminal
 **Environment Variables**:
 ```bash
 export CAI_MODEL=gpt-4o              # Set default model
 export CAI_OPENAI_API_KEY=sk-...    # OpenAI API key
@ -134,7 +108,6 @@ export CAI_ANTHROPIC_API_KEY=sk-... # Anthropic API key
 ```
 **Notes**:
 - Each terminal can use a different model
 - Model costs are tracked separately per terminal
 - Switching models mid-conversation preserves history
@ -150,13 +123,11 @@ Send commands to specific terminals using either the prefix notation or the flag
 #### Method 1: Prefix Notation
 **Syntax**:
 ```
 T<terminal_number>:<command>
 ```
 **Examples**:
 ```bash
 # Switch agent in Terminal 2
 T2:/agent blueteam_agent
@ -170,7 +141,6 @@ T1:/clear
 # Execute command in Terminal 4
 T4:scan target.com for vulnerabilities
 ```
 #### Method 2: Flag Notation
 **Syntax**:
@ -181,7 +151,6 @@ T4:scan target.com for vulnerabilities
 ```
 **Examples**:
 ```bash
 # Switch agent in Terminal 2
 /agent blueteam_agent t2
@ -200,7 +169,6 @@ Scan target.com for XSS vulnerabilities t2
 ```
 **Supported Flags**:
 - `t1` - Target Terminal 1
 - `t2` - Target Terminal 2
 - `t3` - Target Terminal 3
@ -208,7 +176,6 @@ Scan target.com for XSS vulnerabilities t2
 - (Additional terminals if configured: `t5`, `t6`, etc.)
 **Notes**:
 - Both methods achieve the same result
 - Flag notation is more concise for quick commands
 - Prefix notation is clearer for complex prompts
@ -219,7 +186,6 @@ Scan target.com for XSS vulnerabilities t2
 **Keyboard Shortcut**: Click the `[+]` button in the top bar
 **Notes**:
 - New terminals start with `redteam_agent` by default
 - Maximum recommended terminals: 4 (for optimal UX)
 - Terminals beyond 4 use scrollable layout
@ -228,18 +194,16 @@ Scan target.com for XSS vulnerabilities t2
 ## History and Memory
-### `/history [number] [agent_name]` or `/his`
+### `/history [number] [agent_name]` or `/h`
-Display conversation history for the current or specified agent. (Use `/his` — `/h` is reserved for `/help` on builds that follow the documented alias table.)
+Display conversation history for the current or specified agent.
 **Syntax**:
 ```
 /history [number] [agent_name]
 ```
 **Examples**:
 ```bash
 # Show last 10 messages
 /history
@ -251,27 +215,25 @@ Display conversation history for the current or specified agent. (Use `/his` —
 /history 10 redteam_agent
 # Compact syntax
-/his 5
+/h 5
 ```
 **Notes**:
 - Default shows last 10 interactions
 - History includes both user prompts and agent responses
 - History is terminal-specific
 - In the REPL, `/history` also supports subcommands such as `all`, `agent`, `search`, and `index` (see `/help history`). To export conversations, use **`/save <file>`** (`.jsonl` for `/load`, `.md` for a readable report). The old **`/history export`** command was removed (using it prints a deprecation hint).
-### `/flush [agent_name|all]` or `/clear`
+### `/flush [agent_name|all]`
-Clear agent message history (`/clear` is an alias of `/flush` for history, not the same as clearing the screen—see below).
+Clear agent message history.
 **Syntax**:
 ```
 /flush [agent_name|all]
 ```
 **Examples**:
 ```bash
 # Flush current agent history
 /flush
@ -284,18 +246,15 @@ Clear agent message history (`/clear` is an alias of `/flush` for history, not t
 ```
 **Notes**:
 - Flushing is irreversible
 - Agent context window is reset
 - Useful for starting fresh conversations
 - **TUI:** with **no** arguments, `/flush` typically affects only the **focused** terminal (see [CLI parity and TUI-only differences](#cli-parity-and-tui-only-differences)). The CLI may instead show a picker—confirm with `/help flush` on your build.
 ### `/memory [subcommand]` or `/mem`
 Advanced memory management for agents.
 **Syntax**:
 ```
 /memory <subcommand>
 /mem <subcommand>
@ -304,72 +263,55 @@ Advanced memory management for agents.
 **Subcommands**:
 #### `list`
 Show all saved memories.
 ```bash
 /memory list
 ```
 #### `save [name]`
 Save current conversation as a memory.
 ```bash
 /memory save "Authentication bypass research"
 /mem save pentest_findings
 ```
 #### `apply <memory_id>`
 Apply a saved memory to the current agent.
 ```bash
 /memory apply mem_12345
 ```
 #### `show <memory_id>`
 Display the content of a specific memory.
 ```bash
 /memory show mem_12345
 ```
 #### `delete <memory_id>`
 Remove a memory permanently.
 ```bash
 /memory delete mem_12345
 ```
 #### `merge <id1> <id2> [name]`
 Combine two memories into one.
 ```bash
 /memory merge mem_12345 mem_67890 "Combined pentesting notes"
 ```
 #### `compact`
 AI-powered memory summarization.
 ```bash
 /memory compact
 ```
 #### `status`
 Show memory system status and statistics.
 ```bash
 /memory status
 ```
 **Notes**:
 - Memories persist across sessions
 - Useful for resuming long-term research projects
 - AI-powered summarization reduces token usage
@ -380,86 +322,64 @@ Show memory system status and statistics.
 ### `/save <filename>`
-Save the current conversation to a file.
+Save all agent conversation histories in one of two formats (chosen by file extension):
 | Extension | Purpose |
 |-----------|---------|
 | **`.jsonl`** (default style) | One JSON object per line (`agent`, `role`, `content`, plus tool fields). **Reload with `/load`.** |
 | **`.md`** or **`.markdown`** | Human-readable Markdown (sections per agent, `### role` headings, fenced content). **Not** for `/load`. |
 **Syntax**:
 ```
 /save <filename>
 ```
 **Supported Formats**:
 - JSON (`.json`)
 - Markdown (`.md`)
 **Examples**:
 ```bash
-# Save as JSON
+/save pentest_session.jsonl
-/save pentest_session.json
+/save ~/Documents/cai_sessions/project_alpha.jsonl
-
+/save executive_summary.md
-# Save as Markdown
+/save findings.markdown
 /save findings_report.md
 # Save with full path
 /save ~/Documents/cai_sessions/project_alpha.json
 ```
 **Notes**:
-
+- Distinct from `/memory save` (that writes summarized memory markdown under `.cai/memory`)
- Saves all terminal conversations
+- Paths such as `~/file.jsonl` are expanded to your home directory; parent folders are created if they do not exist yet
 - Includes agent names, models, and timestamps
 - Cost information is preserved
 ### `/load <filename>` or `/l`
-Load a previously saved conversation.
+Load conversation messages from **JSONL** (e.g. a `/save` **`.jsonl`** file, or compatible session logs — not **`.md`** exports).
 **Syntax**:
 ```
 /load <filename>
 /l <filename>
 ```
 **Examples**:
 ```bash
-# Load JSON session
+/load pentest_session.jsonl
-/load pentest_session.json
+/l ~/cai_sessions/old_session.jsonl
 # Load Markdown report
 /load findings_report.md
 # Compact syntax
 /l ~/cai_sessions/old_session.json
 ```
 **Notes**:
-
+- Restores message history into the session (see `/help load` for agent- and parallel-specific forms)
- Restores agent context and history
+- Only **`/save` `.jsonl`** files round-trip with `/load`; Markdown exports are for reading or sharing
 - Compatible with both JSON and Markdown formats
 - Loading does not affect current cost tracking
 ---
 ## Utility Commands
 For spend and token usage use **`/cost`** (and the per-terminal cost UI in the TUI). When threads grow too long, use **`/compact`**. See the [CLI commands reference](../cli/commands_reference.md) for the full command list.
 ### `/cost [agent_name]`
 Display API usage costs and token statistics.
 **Syntax**:
 ```
 /cost [agent_name]
 ```
 **Examples**:
 ```bash
 # Show costs for active terminal
 /cost
@ -472,7 +392,6 @@ Display API usage costs and token statistics.
 ```
 **Output Includes**:
 - Total cost (USD)
 - Input tokens used
 - Output tokens used
@ -484,15 +403,15 @@ Display API usage costs and token statistics.
 Get help for commands.
-**Syntax**:
+**CLI headless only:** typing **`?`** alone (no leading slash) shows a compact **input shortcuts** panel. In the TUI, **`?`** is sent as normal chat text. **`/?`** remains an alias for **`/help`** (with slash).
 **Syntax**:
 ```
 /help [command]
 /? [command]
 ```
 **Examples**:
 ```bash
 # General help
 /help
@ -500,6 +419,7 @@ Get help for commands.
 # Help for specific command
 /help agent
 /help parallel
 /help mcp
 /? mcp
 ```
@ -508,13 +428,11 @@ Get help for commands.
 Display environment variables relevant to CAI.
 **Syntax**:
 ```
 /env
 ```
 **Output Includes**:
 - `CAI_MODEL` - Default model
 - `CAI_AGENT_TYPE` - Default agent
 - `CAI_MAX_TURNS` - Maximum interaction turns
@ -529,14 +447,12 @@ Display environment variables relevant to CAI.
 Execute shell commands directly from the TUI.
 **Syntax**:
 ```
 /shell <command>
 $<command>
 ```
 **Examples**:
 ```bash
 # List files
 /shell ls -la
@ -549,21 +465,19 @@ $nmap -sV 192.168.1.1
 ```
 **Notes**:
 - Commands execute in the system shell
 - Output is displayed in the terminal
 - Use with caution - no sandboxing
-### Stopping work in the TUI
+### Interrupting agent execution
-Use **Ctrl+C** to cancel the **current agent turn** or tool call in the focused terminal. Partial responses may be discarded depending on the tool; message history is preserved unless you also `/flush`. To signal an external PID, use **`/shell`** or **`$`** with the usual OS tools.
+There is **no** `/kill` slash-command in CAI. To stop the agent mid-turn in the TUI, use **`Ctrl+C`** on the focused terminal (or press **`Escape` twice** for a broader cancel, depending on your layout). Partial streaming output may be discarded; conversation context in memory is otherwise preserved unless you clear it with `/flush`.
-### `/clear` (scrollback)
+### `/clear`
-Clear **visual** terminal output (scrollback) in the TUI.
+Clear the terminal output.
 **Syntax**:
 ```
 /clear
 ```
@ -571,21 +485,16 @@ Clear **visual** terminal output (scrollback) in the TUI.
 **Keyboard Shortcut**: `Ctrl+L`
 **Notes**:
 - Clears visual output only
- **Does not** wipe conversation history—use `/flush` / `/clear` in the history sense (see above)
+- Conversation history is preserved
 - Cost tracking continues
 ### `/exit` or `/quit` or `/q`
-Leave the TUI session cleanly (telemetry flush, session teardown).
+**Keyboard Shortcut**: `Ctrl+Q`
 **Keyboard Shortcut**: `Ctrl+Q` (depending on build; confirm in-app shortcuts)
 **Notes**:
-
+- Unsaved sessions will be lost
- Unsaved work in buffers may be lost—`/save` or export logs first if needed
+- Graceful shutdown of all terminals
 - Shuts down all terminals in the layout
 ---
@ -598,7 +507,6 @@ Access the command palette for quick command search and execution.
 **Keyboard Shortcut**: `Ctrl+P`
 **Features**:
 - Fuzzy search for commands
 - Command descriptions
 - Keyboard navigation (arrow keys, Enter)
@ -613,6 +521,7 @@ Show or hide the sidebar.
 **Alternative**: Click the `[≡]` button in the top bar
 ### Clear Input
 Clear the prompt input field.
@ -620,7 +529,6 @@ Clear the prompt input field.
 **Keyboard Shortcut**: `Ctrl+U`
 **Use Cases**:
 - Parallel agent execution
 - Comparing agent responses
 - Team-based workflows
@ -630,7 +538,6 @@ Clear the prompt input field.
 Cancel running operations.
 **Keyboard Shortcuts**:
 - `Ctrl+C` - Cancel execution in focused terminal
 - `Escape` - Cancel all running agents (press twice to exit)
@ -638,7 +545,6 @@ Cancel running operations.
 ## Next Steps
 - [CLI commands reference](../cli/commands_reference.md) — recommended for exact REPL syntax
 - [Terminals Management](terminals_management.md) - Advanced multi-terminal workflows
 - [Keyboard Shortcuts](keyboard_shortcuts.md) - Complete keyboard reference
 - [User Interface Guide](user_interface.md) - Visual components and layouts
@ -647,4 +553,5 @@ For questions or issues, visit [CAI GitHub Issues](https://github.com/aliasrobot
 ---
-*TUI UX and shortcuts; authoritative slash-command tables: [CLI commands reference](../cli/commands_reference.md).*
+*Last updated: October 2025 | CAI TUI v0.6+*
--- a/docs/tui/getting_started.md
+++ b/docs/tui/getting_started.md
@ -253,27 +253,31 @@ Learn more about Teams and Parallel Execution in the full TUI documentation.
 ## Step 8: Saving Your Work
-To save your conversation for later:
+To save all agent histories:
 - **JSONL** (reload later with `/load`):
 ```
-/save my-assessment.json
+/save my-assessment.jsonl
 ```
-Or in Markdown format:
+- **Markdown** (readable report or handoff document; not for `/load`):
 ```
 /save my-assessment.md
 ```
-Files are saved in your current working directory.
+Files are written to the path you give (relative paths use the current working directory). This is separate from `/memory save`, which stores summarized memory under `.cai/memory`.
 ### Loading a Saved Session
 Use a **`.jsonl`** file from `/save` (or a compatible session log):
 ```
-/load my-assessment.json
+/load my-assessment.jsonl
 ```
-This restores the conversation history for the current terminal.
+This merges the saved messages into the session (see `/help load` for targeting a specific agent or parallel slot).
 ## Step 9: Monitoring Costs
--- a/docs/tui/terminals_management.md
+++ b/docs/tui/terminals_management.md
@ -339,8 +339,8 @@ When you select a team:
 To create custom team configurations:
 1. Manually configure each terminal with desired agents
-2. Save the session: `/save my_custom_team.json`
+2. Save the session: `/save my_custom_team.jsonl` (or `/save summary.md` for a readable Markdown export)
-3. Load it later: `/load my_custom_team.json`
+3. Load it later with `/load my_custom_team.jsonl` (JSONL only; `.md` is not for `/load`)
 ---
@ -584,7 +584,7 @@ Leverage the 11 built-in teams instead of manual configuration.
 Save sessions with descriptive names:
 ```bash
-/save 2025-10-27_webapp_pentest_team3.json
+/save 2025-10-27_webapp_pentest_team3.jsonl
 ```
 ### 4. Monitor Costs Per Terminal
--- a/docs/tui/troubleshooting.md
+++ b/docs/tui/troubleshooting.md
@ -105,9 +105,9 @@ Common issues and solutions when using CAI TUI.
 **Symptom**: `/load` command fails
 **Solutions**:
- Verify file path is correct
+- Verify file path is correct (paths like `~/file.jsonl` are expanded automatically)
- Check JSON format validity
+- Use **`.jsonl`** from `/save` or compatible session logs — **not** `/save` **`.md`** exports (those are read-only reports)
- Ensure file permissions
+- Ensure file permissions and that the file exists
 ### Stats Not Updating
--- a/docs/tui/tui_index.md
+++ b/docs/tui/tui_index.md
@ -1,5 +1,8 @@
 # CAI Terminal User Interface (TUI)
 !!! warning "Documentation freshness"
    This TUI section is **likely out of date**. **Maintained documentation** for commands and behaviour lives under the **[CLI docs](../cai/getting-started/commands.md)** and the rest of the **`docs/cai/`** tree; prefer those sources when something disagrees with this guide.
 > **⚠️ DEPRECATED - Superseded by Mobile UI**  
 > The Terminal User Interface (TUI) has been deprecated in favor of the new **[Mobile UI](../mui/mui_index.md)** for CAI-Pro users.  
 > While the TUI remains functional for existing users, all new features and development efforts are focused on the Mobile UI.  
@ -171,8 +174,8 @@ See the complete [Keyboard Shortcuts Reference](keyboard_shortcuts.md) for all s
 | `/model <name>` | Change model |
 | `/queue` | Show prompt queue |
 | `/cost` | Show costs and tokens |
-| `/save <file>` | Save conversation |
+| `/save <file>` | Save as `.jsonl` (for `/load`) or `.md` (report) |
-| `/load <file>` | Load conversation |
+| `/load <file>` | Load conversation JSONL (not `.md` exports) |
 See the complete [Commands Reference](commands_reference.md) for all commands.
--- a/docs/tui/user_interface.md
+++ b/docs/tui/user_interface.md
@ -389,9 +389,9 @@ The input area at the bottom provides prompt entry and management:
 - Auto-scrolling for long text
 **Keyboard Shortcuts**:
- `Enter`: Submit prompt (single-line mode)
+- `Enter`: Submit prompt
- `Shift+Enter`: New line (multi-line mode)
+- `Shift+Enter`: New line (terminals with extended keyboard protocols)
- `Ctrl+Enter`: Submit multi-line prompt
+- `Alt+Enter`: New line (universal fallback)
 - `Ctrl+U`: Clear input
 - `Up/Down`: Navigate command history
@ -401,8 +401,8 @@ The TUI provides intelligent autocompletion for:
 **Commands**:
 - `/clear` - Clear terminal
- `/save` - Save session
+- `/save` - Save as `.jsonl` (for `/load`) or `.md` (readable report)
- `/load` - Load session
+- `/load` - Load conversation JSONL (not Markdown exports)
 - `/help` - Show help
 - `/agent` - Switch agent
 - `/model` - Switch model
@ -459,9 +459,8 @@ Press `Ctrl+P` or click the menu button to open the command palette, which provi
 Available commands include:
 - `clear` - Clear terminal output
- `save` - Save current session
+- `save` - Save as JSONL or Markdown (`/save file.jsonl` or `/save report.md`)
- `load` - Load previous session
+- `load` - Load JSONL conversation (`/load`; use `.jsonl` from `/save`)
 - `export` - Export conversation
 - `reset` - Reset agent context
 - `help` - Show help information
--- a/docs/tui_command_analysis.md
+++ b/docs/tui_command_analysis.md
@ -0,0 +1,161 @@
 # CAI TUI Command Analysis: Agent, Model, and Parallel Commands
 ## Overview
 This document analyzes how CAI commands like `/agent`, `/model`, and `/parallel` work in the TUI context, focusing on the distinction between global state and terminal-specific state.
 ## Architecture
 ### Key Components
 1. **CommandHandler** (`src/cai/tui/components/command_handler.py`)
   - Handles CLI command execution within the TUI
   - Intercepts console output and redirects to appropriate terminal widgets
   - Has special handling for `/agent` and `/model` commands
 2. **SessionManager** (`src/cai/tui/core/session_manager.py`)
   - Manages overall TUI session state
   - Coordinates multiple terminal runners
   - Handles parallel mode coordination
   - Methods: `update_model()`, `switch_agent()`
 3. **TerminalRunner** (`src/cai/tui/core/terminal_runner.py`)
   - Manages agent execution within a single terminal
   - Each terminal has its own agent instance and message history
   - Methods: `switch_agent()`, `update_model()`
 4. **REPL Commands** (`src/cai/repl/commands/`)
   - `/agent` - Agent selection and management
   - `/model` - Model selection
   - `/parallel` - Parallel agent configuration
 ## Command Behavior Analysis
 ### 1. `/agent` Command
 **Current Implementation:**
 - Modifies global environment variable `CAI_AGENT_TYPE`
 - Updates `AGENT_MANAGER` global state
 - Handles parallel pattern loading into `PARALLEL_CONFIGS`
 **TUI Context Issues:**
 - The command modifies global state that affects all terminals
 - No per-terminal agent selection mechanism
 - CommandHandler has special handling but only updates local state
 **Per-Terminal Requirements:**
 - Need to call `session_manager.switch_agent(agent_name, terminal_number)`
 - Should not modify global `CAI_AGENT_TYPE` when in TUI mode
 - Each terminal should maintain its own agent configuration
 ### 2. `/model` Command
 **Current Implementation:**
 - Modifies global environment variable `CAI_MODEL`
 - Affects all future agent interactions globally
 **TUI Context Behavior:**
 - CommandHandler detects `/model` commands
 - Calls `session_manager.update_model(model_name)`
 - SessionManager updates all terminal runners with the new model
 - This is actually appropriate for model changes (typically want all terminals to use same model)
 **Working Correctly:**
 - Model updates propagate to all terminals as expected
 - Each terminal runner updates its agent's model recursively
 ### 3. `/parallel` Command
 **Current Implementation:**
 - Manages `PARALLEL_CONFIGS` global list
 - Sets `CAI_PARALLEL` and `CAI_PARALLEL_AGENTS` environment variables
 - Configures agents for parallel execution
 **TUI Context Behavior:**
 - SessionManager detects parallel mode via `is_parallel_mode` flag
 - Distributes parallel agents across terminals (Terminal 1 = P1, Terminal 2 = P2, etc.)
 - Uses `PARALLEL_CONFIGS` to determine agent assignments
 **Issues:**
 - Parallel mode is global, not per-terminal
 - Cannot have different parallel configurations in different terminals
 - `/agent` command that loads parallel patterns affects all terminals
 ## State Management
 ### Global State (Shared Across All Terminals)
 - Environment variables: `CAI_AGENT_TYPE`, `CAI_MODEL`, `CAI_PARALLEL`
 - `PARALLEL_CONFIGS` list
 - `AGENT_MANAGER` registry
 - `PARALLEL_ISOLATION` histories
 ### Per-Terminal State
 - `TerminalRunner.agent` - Agent instance
 - `TerminalRunner.message_history` - Conversation history
 - `TerminalRunner.config` - Terminal configuration including agent_name and model
 - `TerminalConfig.parallel_config` - Parallel agent assignment
 ## Recommended Fixes
 ### 1. Make `/agent` Terminal-Aware in TUI Mode
 ```python
 # In command_handler.py, enhance the special handling:
 if cmd_name.lower() in ["/agent", "/a"] and args:
    if args[0] == "select" and len(args) > 1:
        agent_name = args[1]
        terminal_number = self.terminal_number  # Need to add this
        # Don't modify global state in TUI mode
        if hasattr(self, 'session_manager') and self.session_manager:
            asyncio.create_task(
                self.session_manager.switch_agent(agent_name, terminal_number)
            )
 ```
 ### 2. Add Terminal-Specific Agent Command
 Create a new command like `/terminal-agent` or modify `/agent` to accept terminal number:
 - `/agent select <name> --terminal 2` - Select agent for specific terminal
 - `/agent select <name>` - In TUI, affects only current terminal
 ### 3. Enhance Parallel Mode for TUI
 - Allow parallel configurations to be terminal-set specific
 - Each terminal could have its own parallel group
 - Terminal 1-3 could run one parallel set, Terminal 4-6 another
 ### 4. Add TUI Context Detection
 Commands should detect TUI mode and behave differently:
 ```python
 def is_tui_mode():
    return os.getenv("CAI_TUI_MODE") == "true"
 # In agent command:
 if not is_tui_mode():
    # Current behavior - modify global state
    os.environ["CAI_AGENT_TYPE"] = agent_key
 else:
    # TUI behavior - notify session manager
    # Don't modify global state
 ```
 ## Current Workarounds
 1. **For Agent Selection in Specific Terminal:**
   - Use the terminal's direct execution instead of commands
   - Manually reinitialize terminal runners after global changes
 2. **For Model Changes:**
   - The current implementation works well for global model updates
   - Use `/model` command normally
 3. **For Parallel Mode:**
   - Configure parallel agents before starting terminals
   - Use environment variables to pre-configure
   - Parallel mode affects all terminals as designed
 ## Conclusion
 The TUI command system currently relies heavily on global state inherited from the CLI design. To support true multi-terminal workflows, commands need to be enhanced with terminal-awareness and the ability to modify per-terminal state rather than global state. The `/model` command works well as-is since model changes are typically desired globally, but `/agent` and `/parallel` need terminal-specific implementations.
--- a/examples/basic/usage_tracking_example.py
+++ b/examples/basic/usage_tracking_example.py
@ -9,20 +9,21 @@ import json
 import os
 from pathlib import Path
 def display_usage_stats():
    """Display the current global usage statistics"""
    usage_file = Path.home() / ".cai" / "usage.json"
-    
+
    if not usage_file.exists():
        print("No usage data found yet. Run CAI to start tracking usage.")
        return
-    
+
    try:
-        with open(usage_file, 'r') as f:
+        with open(usage_file, "r") as f:
            usage_data = json.load(f)
-        
+
        print("\n=== CAI Global Usage Statistics ===\n")
-        
+
        # Display global totals
        totals = usage_data.get("global_totals", {})
        print("📊 Overall Usage:")
@ -31,21 +32,23 @@ def display_usage_stats():
        print(f"  Total Requests: {totals.get('total_requests', 0)}")
        print(f"  Total Input Tokens: {totals.get('total_input_tokens', 0):,}")
        print(f"  Total Output Tokens: {totals.get('total_output_tokens', 0):,}")
-        print(f"  Total Tokens: {totals.get('total_input_tokens', 0) + totals.get('total_output_tokens', 0):,}")
+        print(
-        
+            f"  Total Tokens: {totals.get('total_input_tokens', 0) + totals.get('total_output_tokens', 0):,}"
        )
        # Display model usage
        model_usage = usage_data.get("model_usage", {})
        if model_usage:
            print("\n🤖 Usage by Model:")
-            for model, stats in sorted(model_usage.items(), 
+            for model, stats in sorted(
-                                     key=lambda x: x[1].get('total_cost', 0), 
+                model_usage.items(), key=lambda x: x[1].get("total_cost", 0), reverse=True
-                                     reverse=True):
+            ):
                print(f"\n  {model}:")
                print(f"    Cost: ${stats.get('total_cost', 0):.4f}")
                print(f"    Requests: {stats.get('total_requests', 0)}")
                print(f"    Input Tokens: {stats.get('total_input_tokens', 0):,}")
                print(f"    Output Tokens: {stats.get('total_output_tokens', 0):,}")
-        
+
        # Display daily usage for the last 7 days
        daily_usage = usage_data.get("daily_usage", {})
        if daily_usage:
@ -55,8 +58,10 @@ def display_usage_stats():
                print(f"\n  {day}:")
                print(f"    Cost: ${stats.get('total_cost', 0):.4f}")
                print(f"    Requests: {stats.get('total_requests', 0)}")
-                print(f"    Tokens: {stats.get('total_input_tokens', 0) + stats.get('total_output_tokens', 0):,}")
+                print(
-        
+                    f"    Tokens: {stats.get('total_input_tokens', 0) + stats.get('total_output_tokens', 0):,}"
                )
        # Display recent sessions
        sessions = usage_data.get("sessions", [])
        if sessions:
@ -68,13 +73,13 @@ def display_usage_stats():
                print(f"    Cost: ${session.get('total_cost', 0):.4f}")
                print(f"    Requests: {session.get('total_requests', 0)}")
                print(f"    Models: {', '.join(session.get('models_used', []))}")
-                if session.get('end_time'):
+                if session.get("end_time"):
                    print(f"    End: {session.get('end_time')}")
                else:
                    print("    Status: Active")
-        
+
-        print("\n" + "="*35 + "\n")
+        print("\n" + "=" * 35 + "\n")
-        
+
    except json.JSONDecodeError:
        print("Error: Unable to read usage data. File may be corrupted.")
    except Exception as e:
@ -84,19 +89,20 @@ def display_usage_stats():
 def reset_usage_stats():
    """Reset usage statistics (with confirmation)"""
    usage_file = Path.home() / ".cai" / "usage.json"
-    
+
    if not usage_file.exists():
        print("No usage data to reset.")
        return
-    
+
    response = input("Are you sure you want to reset all usage statistics? (yes/no): ")
-    if response.lower() == 'yes':
+    if response.lower() == "yes":
        # Create backup
-        backup_file = usage_file.with_suffix('.json.backup')
+        backup_file = usage_file.with_suffix(".json.backup")
        import shutil
        shutil.copy2(usage_file, backup_file)
        print(f"Backup created at: {backup_file}")
-        
+
        # Reset the file
        usage_file.unlink()
        print("Usage statistics have been reset.")
@ -107,19 +113,20 @@ def reset_usage_stats():
 def export_usage_report(output_file="cai_usage_report.json"):
    """Export usage statistics to a file"""
    usage_file = Path.home() / ".cai" / "usage.json"
-    
+
    if not usage_file.exists():
        print("No usage data to export.")
        return
-    
+
    import shutil
    shutil.copy2(usage_file, output_file)
    print(f"Usage report exported to: {output_file}")
 if __name__ == "__main__":
    import sys
-    
+
    if len(sys.argv) > 1:
        command = sys.argv[1]
        if command == "reset":
@ -129,4 +136,4 @@ if __name__ == "__main__":
        else:
            print("Usage: python usage_tracking_example.py [reset|export [filename]]")
    else:
-        display_usage_stats()
+        display_usage_stats()
--- a/examples/cai/agent_patterns/LLM_as_judge.py
+++ b/examples/cai/agent_patterns/LLM_as_judge.py
@ -13,37 +13,50 @@ import os
 import json
 from dataclasses import dataclass
 from typing import Literal
-from cai.sdk.agents import Agent, ItemHelpers, Runner, TResponseInputItem, OpenAIChatCompletionsModel
+from cai.sdk.agents import (
    Agent,
    ItemHelpers,
    Runner,
    TResponseInputItem,
    OpenAIChatCompletionsModel,
 )
 from openai import AsyncOpenAI
 from cai.util import get_ollama_api_base
 # Enable debug mode
-#os.environ['CAI_DEBUG'] = '2'
+# os.environ['CAI_DEBUG'] = '2'
-#os.environ['LITELLM_VERBOSE'] = 'True'
+# os.environ['LITELLM_VERBOSE'] = 'True'
 # Force Ollama mode if qwen model is used
-if os.getenv('CAI_MODEL', "qwen2.5:14b").startswith("qwen"): 
+if os.getenv("CAI_MODEL", "qwen2.5:14b").startswith("qwen"):
-    os.environ['OLLAMA'] = 'true'
+    os.environ["OLLAMA"] = "true"
 # Modify OpenAIChatCompletionsModel._fetch_response_litellm_ollama to debug output
 import cai.sdk.agents.models.openai_chatcompletions
 original_fetch_response_litellm_ollama = cai.sdk.agents.models.openai_chatcompletions.OpenAIChatCompletionsModel._fetch_response_litellm_ollama
-async def debug_fetch_response_litellm_ollama(self, kwargs, model_settings, tool_choice, stream, parallel_tool_calls):
+
 async def debug_fetch_response_litellm_ollama(
    self, kwargs, model_settings, tool_choice, stream, parallel_tool_calls
 ):
    print("\n[DEBUG] Ollama request parameters:")
    print(f"Base URL: {get_ollama_api_base().rstrip('/v1')}")
    print(f"Model name: {kwargs.get('model')}")
-    print(f"Messages: {json.dumps(kwargs.get('messages'))[:200]}...") # Truncated to avoid huge output
+    print(
-    
+        f"Messages: {json.dumps(kwargs.get('messages'))[:200]}..."
    )  # Truncated to avoid huge output
    # Check if the model exists in Ollama
    import requests
    try:
        response = requests.get(f"{get_ollama_api_base().rstrip('/v1')}/api/tags")
        models = response.json().get("models", [])
        model_names = [model.get("name") for model in models]
        print(f"Available Ollama models: {model_names}")
-        
+
-        model_name = kwargs.get('model')
+        model_name = kwargs.get("model")
        if model_name in model_names:
            print(f"✅ Model '{model_name}' is available in Ollama")
        else:
@ -51,16 +64,19 @@ async def debug_fetch_response_litellm_ollama(self, kwargs, model_settings, tool
            similar_models = [name for name in model_names if model_name.split(":")[0] in name]
            if similar_models:
                print(f"Similar models available: {similar_models}")
-                
+
                # Try with first similar model
                if similar_models:
                    print(f"⚠️ Trying with similar model: {similar_models[0]}")
                    kwargs["model"] = similar_models[0]
    except Exception as e:
        print(f"Error checking Ollama models: {e}")
-    
+
    # Call the original function
-    return await original_fetch_response_litellm_ollama(self, kwargs, model_settings, tool_choice, stream, parallel_tool_calls)
+    return await original_fetch_response_litellm_ollama(
        self, kwargs, model_settings, tool_choice, stream, parallel_tool_calls
    )
 # Patch the function
 cai.sdk.agents.models.openai_chatcompletions.OpenAIChatCompletionsModel._fetch_response_litellm_ollama = debug_fetch_response_litellm_ollama
@ -75,10 +91,10 @@ ctf_task_planner = Agent(
        "Use any feedback to improve your planning."
    ),
    model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
    ),
-    tools=[] 
+    tools=[],
 )
@ -99,10 +115,10 @@ ctf_plan_evaluator = Agent[None](
        "Provide actionable feedback. Never approve on the first try."
    ),
    model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
    ),
-    tools=[],  
+    tools=[],
    output_type=EvaluationFeedback,
 )
@ -112,7 +128,7 @@ async def main() -> None:
    input_items: list[TResponseInputItem] = [{"content": challenge_desc, "role": "user"}]
    latest_plan: str | None = None
-    
+
    while True:
        try:
            print("\n[INFO] Running CTF Task Planner...")
@ -135,6 +151,7 @@ async def main() -> None:
        except Exception as e:
            print(f"Error: {e}")
            import traceback
            traceback.print_exc()
            break
@ -145,4 +162,4 @@ async def main() -> None:
 if __name__ == "__main__":
-    asyncio.run(main())
+    asyncio.run(main())
--- a/examples/cai/agent_patterns/agent_as_tool.py
+++ b/examples/cai/agent_patterns/agent_as_tool.py
@ -2,9 +2,10 @@
 Agents as Tools Example.
 This module demonstrates how to embed one agent as a tool within another agent's workflow.
-An agent-as-tool operates independently on its provided input —without access to prior conversation history or "taking over" 
+An agent-as-tool operates independently on its provided input —without access to prior conversation history or "taking over"
 the conversation — completes its specific task, and returns the result to the calling (parent) agent.
 """
 from __future__ import annotations
 import os
 import json
@ -13,9 +14,18 @@ import asyncio
 from openai import AsyncOpenAI
 from cai.sdk.agents import function_tool
 from cai.tools.common import run_command
-from cai.sdk.agents import Agent, OpenAIChatCompletionsModel, HandoffInputData, Runner, function_tool, handoff, trace, ItemHelpers, MessageOutputItem
+from cai.sdk.agents import (
    Agent,
    OpenAIChatCompletionsModel,
    HandoffInputData,
    Runner,
    function_tool,
    handoff,
    trace,
    ItemHelpers,
    MessageOutputItem,
 )
 from cai.sdk.agents.extensions import handoff_filters
@function_tool
@ -28,21 +38,23 @@ flag_discriminator = Agent(
    description="Agent focused on extracting the flag from the output",
    instructions="You are an agent tailored to extract the flag from a given output.",
    model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
-    ), 
+    ),
-    handoff_description = "Agent focused on extracting the flag from the output" 
+    handoff_description="Agent focused on extracting the flag from the output",
 )
 # We'll use this variable to check if the flag_discriminator agent is used
 flag_discriminator_used = False
 # Wrap the as_tool method to set the flag when used
 def flag_discriminator_as_tool(*args, **kwargs):
    global flag_discriminator_used
    flag_discriminator_used = True
    return flag_discriminator._as_tool(*args, **kwargs)
 # Patch the as_tool method
 flag_discriminator._as_tool = flag_discriminator.as_tool
 flag_discriminator.as_tool = flag_discriminator_as_tool
@ -52,26 +64,28 @@ ctf_agent = Agent(
    description="Agent focused on conquering security challenges",
    instructions="You are a Cybersecurity expert Leader facing a CTF",
    tools=[
-        execute_cli_command, 
+        execute_cli_command,
        flag_discriminator.as_tool(
-            tool_name="find_flag",
+            tool_name="find_flag", tool_description="Find flag in output text"
-            tool_description ="Find flag in output text" 
+        ),
        )
    ],
    model=OpenAIChatCompletionsModel(
-        model= os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
-    )
+    ),
 )
 # Main function to execute the workflow
 async def main():
    result = await Runner.run(
-            ctf_agent,
+        ctf_agent,
-            input= [
+        input=[
-                {"content": "Here is some output from a task. Find the flag: nhwitm flag{1234} mlsk. And returns only the flag", "role": "user"}
+            {
-            ],
+                "content": "Here is some output from a task. Find the flag: nhwitm flag{1234} mlsk. And returns only the flag",
                "role": "user",
            }
        ],
    )
    for item in result.new_items:
@ -86,5 +100,6 @@ async def main():
    else:
        print("Flag discriminator agent was NOT used.")
 if __name__ == "__main__":
-    asyncio.run(main())
+    asyncio.run(main())
--- a/examples/cai/agent_patterns/deterministic.py
+++ b/examples/cai/agent_patterns/deterministic.py
@ -12,20 +12,22 @@ from cai.sdk.agents import Agent, Runner, OpenAIChatCompletionsModel, function_t
 from cai.tools.common import run_command
 from cai.sdk.agents.extensions import handoff_filters
 # Function tool to execute CLI command
@function_tool
 def execute_cli_command(command: str) -> str:
    return run_command(command)
 # Define Flag Discriminator Agent (handles extracting flags from CTF output)
 flag_discriminator = Agent(
    name="Flag discriminator",
    description="Agent focused on extracting the flag from the output",
    instructions="You are an agent tailored to extract the flag from a given output.",
    model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
-    )
+    ),
 )
 # Define CTF Agent (performs the actual challenge)
@ -37,24 +39,27 @@ ctf_agent = Agent(
        execute_cli_command,
    ],
    model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
-    )
+    ),
 )
 # Main handler function for CTF process
 async def main():
    # Step 1: Execute command in CTF environment
    ctf_result = await Runner.run(
        ctf_agent, "List directories with a simple ls command, the flag is in flag.txt"
    )
-        # Step 1: Execute command in CTF environment
+    # Step 2: Pass result to flag discriminator
-        ctf_result = await Runner.run(ctf_agent, "List directories with a simple ls command, the flag is in flag.txt")
+    flag_discriminator_result = await Runner.run(
        flag_discriminator,
        ctf_result.final_output,
    )
-        # Step 2: Pass result to flag discriminator
+    print(f"Flag found: {flag_discriminator_result.final_output}")
        flag_discriminator_result = await Runner.run(
            flag_discriminator,
            ctf_result.final_output,
        )
        print(f"Flag found: {flag_discriminator_result.final_output}")
 if __name__ == "__main__":
    asyncio.run(main())
--- a/examples/cai/agent_patterns/guardrails.py
+++ b/examples/cai/agent_patterns/guardrails.py
@ -11,6 +11,7 @@ We'll set up a guardrail that detects if the user is asking for unethical or una
 cybersecurity help, like hacking, exploiting, or bypassing security measures.
 """
 from __future__ import annotations
 import os
 import asyncio
@ -29,17 +30,20 @@ from cai.sdk.agents import (
 )
 from cai.tools.common import run_command
 # Example tool: run a shell command (for demonstration)
@function_tool
 def execute_cli_command(command: str) -> str:
    """Execute a shell command and return its output."""
    return run_command(command)
 ### 1. Agent-based guardrail triggered by unethical cybersecurity task request
 class CybersecurityCheckOutput(BaseModel):
    reasoning: str
    is_unethical_cybersecurity_request: bool
 cybersecurity_guardrail_agent = Agent(
    name="Cybersecurity Guardrail Check",
    instructions="""Check if the user is asking for unauthorized or unethical cybersecurity help (e.g., hacking, bypassing security, exploiting systems).
@ -51,13 +55,14 @@ You MUST respond using ONLY the following JSON format:
 }
 Do not include any other text, explanations, or conversation outside of this JSON structure.""",
-        output_type=CybersecurityCheckOutput,
+    output_type=CybersecurityCheckOutput,
-        model=OpenAIChatCompletionsModel(
+    model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
-    )
+    ),
 )
@input_guardrail
 async def cybersecurity_guardrail(
    context: RunContextWrapper[None], agent: Agent, input: str | list[TResponseInputItem]
@ -74,6 +79,7 @@ async def cybersecurity_guardrail(
 ### 2. Main run loop using the cybersecurity guardrail
 async def main():
    agent = Agent(
        name="Tech Support Agent",
@ -81,28 +87,28 @@ async def main():
        input_guardrails=[cybersecurity_guardrail],
        tools=[execute_cli_command],  # Add a tool for demonstration
        model=OpenAIChatCompletionsModel(
-            model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
+            model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
            openai_client=AsyncOpenAI(),
-        )
+        ),
    )
    input_data: list[TResponseInputItem] = []
    input_data.append(
-            {
+        {
-                "role": "user",
+            "role": "user",
-                "content": "Do a nmap to my router",
+            "content": "Do a nmap to my router",
-            }
+        }
-        )
+    )
    try:
-            result = await Runner.run(agent, input_data)
+        result = await Runner.run(agent, input_data)
-            print("Agent output:", result.final_output)
+        print("Agent output:", result.final_output)
-            input_data = result.to_input_list()
+        input_data = result.to_input_list()
    except InputGuardrailTripwireTriggered:
-            message = "Sorry, I can't assist with that cybersecurity request."
+        message = "Sorry, I can't assist with that cybersecurity request."
-            print(message)
+        print(message)
 if __name__ == "__main__":
    asyncio.run(main())
--- a/examples/cai/agent_patterns/handoffs.py
+++ b/examples/cai/agent_patterns/handoffs.py
@ -1,6 +1,7 @@
 """
 In many situations, you have specialized sub-agents that handle specific tasks. You can use handoffs to route the task to the right agent.
 """
 from __future__ import annotations
 import os
 import json
@ -9,9 +10,16 @@ import asyncio
 from openai import AsyncOpenAI
 from cai.sdk.agents import function_tool
 from cai.tools.common import run_command
-from cai.sdk.agents import Agent, OpenAIChatCompletionsModel, HandoffInputData, Runner, function_tool, handoff, trace
+from cai.sdk.agents import (
    Agent,
    OpenAIChatCompletionsModel,
    HandoffInputData,
    Runner,
    function_tool,
    handoff,
    trace,
 )
 from cai.sdk.agents.extensions import handoff_filters
@function_tool
@ -24,10 +32,10 @@ flag_discriminator = Agent(
    description="Agent focused on extracting the flag from the output",
    instructions="You are an agent tailored to extract the flag from a given output.",
    model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
-    ) 
+    ),
-    #handoff_description = "Agent focused on extracting the flag from the output" 
+    # handoff_description = "Agent focused on extracting the flag from the output"
 )
 ctf_agent = Agent(
@ -38,15 +46,15 @@ ctf_agent = Agent(
        execute_cli_command,
    ],
    model=OpenAIChatCompletionsModel(
-        model= os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
-    ), 
+    ),
-    handoffs = [flag_discriminator]
+    handoffs=[flag_discriminator],
 )
 # Complex way to do a handoff
-async def invoke_flag_discriminator(context: RunContextWrapper[Any], args: str="") -> Agent:
+async def invoke_flag_discriminator(context: RunContextWrapper[Any], args: str = "") -> Agent:
    """
    This function is called when we need to hand off the task to the flag_discriminator.
    """
@ -56,19 +64,20 @@ async def invoke_flag_discriminator(context: RunContextWrapper[Any], args: str="
    else:
        print("Input provided, processing...")
    print(f"Passing args to flag_discriminator: {args}")
-    
+
    # Return the agent (flag_discriminator) that will handle extracting the flag
    return flag_discriminator
 # input_filter: can be used for additional data filtering during the handoff
 flag_discriminator_complex_handoff = handoff(
-    agent=flag_discriminator,
+    agent=flag_discriminator, input_filter=invoke_flag_discriminator
    input_filter = invoke_flag_discriminator
 )
 ctf_agent.handoffs.append(flag_discriminator_complex_handoff)
 # Main function to execute the workflow
 async def main():
    # Trace the entire run as a single workflow
@ -79,14 +88,18 @@ async def main():
        # Step 2: Ask an additional question for calling the Flag Discriminator agent
        result = await Runner.run(
            ctf_agent,
-            input=result.to_input_list() + [
+            input=result.to_input_list()
-                {"content": "Here is some output from a task. The first file is the name of the flag", "role": "user"}
+            + [
                {
                    "content": "Here is some output from a task. The first file is the name of the flag",
                    "role": "user",
                }
            ],
        )
    for message in result.to_input_list():
        print(json.dumps(message, indent=2))
 if __name__ == "__main__":
-    asyncio.run(main())
+if __name__ == "__main__":
    asyncio.run(main())
--- a/examples/cai/agent_patterns/handoffs_and_tools.py
+++ b/examples/cai/agent_patterns/handoffs_and_tools.py
@ -1,4 +1,4 @@
-""" 
+"""
 This example demonstrates how to use handoffs and tools together
 """
@ -12,6 +12,7 @@ from cai.sdk.agents.extensions.handoff_prompt import RECOMMENDED_PROMPT_PREFIX
 import os
 import asyncio
@function_tool
 def execute_cli_command(command: str) -> str:
    """Execute a command-line command and return its output."""
@ -27,9 +28,9 @@ flag_discriminator = Agent(
    handoff_description="Specialized agent in determining whether the content corresponds to the flag of the CTF challenge",
    handoffs=[],
    model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "qwen2.5:72b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:72b"),
        openai_client=AsyncOpenAI(),
-    )
+    ),
 )
 # Create the Bash Agent (can hand off to Flag Discriminator)
@ -44,9 +45,9 @@ bash_agent = Agent(
    handoffs=[handoff(flag_discriminator)],
    handoff_description="Specialized agent in Bash commands and Linux operations",
    model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
-    )
+    ),
 )
 # Create the Crypto Agent
@ -59,9 +60,9 @@ crypto_agent = Agent(
    handoffs=[],
    handoff_description="Specialized agent in cryptography and codebreaking",
    model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
-    )
+    ),
 )
 # Create the Cybersecurity Lead Agent (can hand off to both Bash and Crypto)
@ -73,15 +74,12 @@ cybersecurity_lead = Agent(
    - Hand off to the Bash Agent when you need to execute Linux commands or navigate the file system.
    - Hand off to the Cryptography Agent when you encounter encrypted data or codes that need deciphering.""",
    tools=[execute_cli_command],
-    handoffs=[
+    handoffs=[handoff(bash_agent), handoff(crypto_agent)],
        handoff(bash_agent),
        handoff(crypto_agent)
    ],
    handoff_description="Lead agent in cybersecurity operations",
    model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
-    )
+    ),
 )
@ -93,5 +91,6 @@ async def main():
    print(result.final_output)
 if __name__ == "__main__":
-    asyncio.run(main())
+    asyncio.run(main())
--- a/examples/cai/agent_patterns/paralelization.py
+++ b/examples/cai/agent_patterns/paralelization.py
@ -1,7 +1,7 @@
 """
 Parallelization Pattern:
-This pattern runs multiple agents in parallel to perform a task, generating different responses. 
+This pattern runs multiple agents in parallel to perform a task, generating different responses.
-Afterward, a separate agent is used to evaluate and pick the best result. 
+Afterward, a separate agent is used to evaluate and pick the best result.
 """
 import asyncio
@ -10,14 +10,24 @@ import asyncio
 from openai import AsyncOpenAI
 from cai.sdk.agents import function_tool
 from cai.tools.common import run_command
-from cai.sdk.agents import Agent, OpenAIChatCompletionsModel, HandoffInputData, Runner, function_tool, handoff, trace, ItemHelpers
+from cai.sdk.agents import (
    Agent,
    OpenAIChatCompletionsModel,
    HandoffInputData,
    Runner,
    function_tool,
    handoff,
    trace,
    ItemHelpers,
 )
 from cai.sdk.agents.extensions import handoff_filters
@function_tool
 def execute_cli_command(command: str) -> str:
    return run_command(command)
 # Create the CTF agent
 ctf_agent = Agent(
    name="CTF agent",
@ -27,9 +37,9 @@ ctf_agent = Agent(
        execute_cli_command,
    ],
    model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
-    )
+    ),
 )
 # An agent to pick the best solution after multiple attempts
@ -38,50 +48,52 @@ best_solution_picker = Agent(
    description="Agent focused on  picking the best security solutio",
    instructions="You pick the best security solution from the given attempts.",
    model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
-    )
+    ),
 )
 async def main():
    # Define your CTF challenge
    challenge = input("Enter the CTF challenge you're facing:\n\n")
    # Ensure the entire workflow is a single trace
    res_1, res_2, res_3 = await asyncio.gather(
-            Runner.run(
+        Runner.run(
-                ctf_agent,
+            ctf_agent,
-                challenge,
+            challenge,
-            ),
+        ),
-            Runner.run(
+        Runner.run(
-                ctf_agent,
+            ctf_agent,
-                challenge,
+            challenge,
-            ),
+        ),
-            Runner.run(
+        Runner.run(
-                ctf_agent,
+            ctf_agent,
-                challenge,
+            challenge,
-            ),
+        ),
-        )
+    )
-        # Gather the results from the CTF attempts
+    # Gather the results from the CTF attempts
    outputs = [
-            ItemHelpers.text_message_outputs(res_1.new_items),
+        ItemHelpers.text_message_outputs(res_1.new_items),
-            ItemHelpers.text_message_outputs(res_2.new_items),
+        ItemHelpers.text_message_outputs(res_2.new_items),
-            ItemHelpers.text_message_outputs(res_3.new_items),
+        ItemHelpers.text_message_outputs(res_3.new_items),
-        ]
+    ]
-        # Show all the results
+    # Show all the results
    results = "\n\n".join(outputs)
    print(f"\n\nCTF Results:\n\n{results}")
-        # Run the best solution picker agent
+    # Run the best solution picker agent
    best_solution = await Runner.run(
-            best_solution_picker,
+        best_solution_picker,
-            f"Input: {challenge}\n\nResults:\n{results}",
+        f"Input: {challenge}\n\nResults:\n{results}",
    )
    print("\n\n-----")
    print(f"Best solution: {best_solution.final_output}")
 if __name__ == "__main__":
-    asyncio.run(main())
+    asyncio.run(main())
--- a/examples/cai/basic_usage.py
+++ b/examples/cai/basic_usage.py
@ -1,8 +1,9 @@
 """
-A common tactic is to break down a task into a series of smaller steps. 
+A common tactic is to break down a task into a series of smaller steps.
 Each task can be performed by an agent, and the output of one agent is used as input to the next
 try stream and normal
 """
 import os
 import sys
 import time
@ -10,7 +11,7 @@ import asyncio
 from cai.sdk.agents import Runner, Agent, OpenAIChatCompletionsModel, set_tracing_disabled
 from openai import AsyncOpenAI
 from cai.sdk.agents import function_tool
-from cai.tools.common import run_command 
+from cai.tools.common import run_command
@function_tool
@ -26,15 +27,18 @@ ctf_agent = Agent(
        execute_cli_command,
    ],
    model=OpenAIChatCompletionsModel(
-        model= os.getenv('CAI_MODEL', "qwen2.5:14b"),
+        model=os.getenv("CAI_MODEL", "qwen2.5:14b"),
        openai_client=AsyncOpenAI(),
-    )
+    ),
 )
 async def main():
    result = await Runner.run(ctf_agent, "List the files in the current directory?")
    print("\nAgent response:")
    print(result.final_output)
 async def main_streamed():
    print("\nAgent response (streaming):")
    result = Runner.run_streamed(ctf_agent, "List the files in the current directory?")
@ -48,18 +52,19 @@ async def main_streamed():
        event_count += 1
        # Add a small delay to allow the streaming panel to update properly
        await asyncio.sleep(0.01)
-        
+
        # # Print a progress indicator
        # if event_count % 10 == 0:
        #     elapsed = time.time() - start_time
        #     sys.stdout.write(f"\rProcessed {event_count} events in {elapsed:.1f} seconds...")
        #     sys.stdout.flush()
-    
+
    # Clear the progress line
    sys.stdout.write("\r" + " " * 60 + "\r")
    sys.stdout.flush()
 if __name__ == "__main__":
    set_tracing_disabled(True)
-    asyncio.run(main()) 
+    asyncio.run(main())
-    asyncio.run(main_streamed()) 
+    asyncio.run(main_streamed())
--- a/examples/cai/simple_one_tool_test.py
+++ b/examples/cai/simple_one_tool_test.py
@ -19,7 +19,7 @@ from cai.sdk.agents.models._openai_shared import set_use_responses_by_default
 # Load environment variables
 load_dotenv()
-#set_tracing_disabled(True) #disable tracing or OPENAI_AGENTS_DISABLE_TRACING=1
+# set_tracing_disabled(True) #disable tracing or OPENAI_AGENTS_DISABLE_TRACING=1
 # NOTE: This is needed when using LiteLLM Proxy Server
 #
@ -29,23 +29,29 @@ load_dotenv()
 # )
 # set_default_openai_client(external_client)
 async def main():
    # Apply litellm patch to fix the __annotations__ error
    patch_applied = fix_litellm_transcription_annotations()
    if not patch_applied:
-        print(color("Something went wrong patching LiteLLM fix_litellm_transcription_annotations", color="red"))
+        print(
-    
+            color(
                "Something went wrong patching LiteLLM fix_litellm_transcription_annotations",
                color="red",
            )
        )
    # Force the use of OpenAIChatCompletionsModel instead of OpenAIResponsesModel
    set_use_responses_by_default(False)
-        
+
    # Get the one_tool agent
    agent = get_agent_by_name("one_tool_agent")
-    
+
    print(f"Using model: {os.getenv('CAI_MODEL', 'default')}")
-    
+
    # Run the agent with a simple test message
    result = await Runner.run(agent, "Hello! Can you list the files in the current directory?")
-    
+
    # Print the result
    print("\nAgent response:")
    print("-" * 40)
@ -53,5 +59,6 @@ async def main():
    print("-" * 40)
    print("\nTest completed successfully!")
 if __name__ == "__main__":
-    asyncio.run(main()) 
+    asyncio.run(main())
--- a/examples/cai/simple_one_tool_test_streamed.py
+++ b/examples/cai/simple_one_tool_test_streamed.py
@ -31,25 +31,31 @@ load_dotenv()
 # )
 # set_default_openai_client(external_client)
 async def main():
    # Apply litellm patch to fix the __annotations__ error
    patch_applied = fix_litellm_transcription_annotations()
    if not patch_applied:
-        print(color("Something went wrong patching LiteLLM fix_litellm_transcription_annotations", color="red"))
+        print(
-        
+            color(
                "Something went wrong patching LiteLLM fix_litellm_transcription_annotations",
                color="red",
            )
        )
    # Force the use of OpenAIChatCompletionsModel instead of OpenAIResponsesModel
    set_use_responses_by_default(False)
-        
+
    # Get the one_tool agent
    agent = get_agent_by_name("one_tool_agent")
    print(f"Using model: {os.getenv('CAI_MODEL', 'default')}")
-    
+
    # Stream indicator
    print("\nAgent response (streaming):")
    print("-" * 40)
    print("Agent: ", end="", flush=True)
-    
+
    # Run the agent with a simple test message in streaming mode
    result = Runner.run_streamed(agent, "Hello! Can you list the files in the current directory?")
@ -62,20 +68,20 @@ async def main():
        event_count += 1
        # Add a small delay to allow the streaming panel to update properly
        await asyncio.sleep(0.01)
-        
+
        # # Print a progress indicator
        # if event_count % 10 == 0:
        #     elapsed = time.time() - start_time
        #     sys.stdout.write(f"\rProcessed {event_count} events in {elapsed:.1f} seconds...")
        #     sys.stdout.flush()
-    
+
    # Clear the progress line
    sys.stdout.write("\r" + " " * 60 + "\r")
    sys.stdout.flush()
-    
+
    print("\n" + "-" * 40)
    print("\nTest completed successfully!")
 if __name__ == "__main__":
-    asyncio.run(main()) 
+    asyncio.run(main())
--- a/examples/cai_api_cli.py
+++ b/examples/cai_api_cli.py
@ -0,0 +1,87 @@
 """Simple CLI to interact with the CAI API backend."""
 from __future__ import annotations
 import json
 import os
 import sys
 from dataclasses import dataclass
 from typing import Any, Dict
 import requests
 API_KEY_HEADER = os.getenv("CAI_API_KEY_HEADER", "X-CAI-API-Key")
@dataclass
 class ClientConfig:
    base_url: str
    api_key: str
 def _prompt(label: str, default: str | None = None) -> str:
    suffix = f" [{default}]" if default else ""
    value = input(f"{label}{suffix}: ").strip()
    if not value and default is not None:
        return default
    return value
 def configure_client() -> ClientConfig:
    host = _prompt("Host", os.getenv("CAI_API_HOST", "127.0.0.1"))
    port = _prompt("Port", os.getenv("CAI_API_PORT", "8000"))
    api_key = _prompt("API key (use ALIAS_API_KEY)", os.getenv("ALIAS_API_KEY") or os.getenv("CAI_API_KEY", ""))
    base_url = f"http://{host}:{port}/api/v1"
    return ClientConfig(base_url=base_url, api_key=api_key)
 def request(method: str, path: str, config: ClientConfig, payload: Dict[str, Any] | None = None) -> Dict[str, Any]:
    url = f"{config.base_url}{path}"
    headers = {"Content-Type": "application/json"}
    if config.api_key:
        headers[API_KEY_HEADER] = config.api_key
    response = requests.request(method, url, json=payload, headers=headers, timeout=30)
    if response.status_code >= 400:
        raise RuntimeError(f"{response.status_code}: {response.text}")
    if response.text:
        return response.json()
    return {}
 def main() -> None:
    print("=== CAI API Demo CLI ===")
    config = configure_client()
    session_payload = {
        "agent": _prompt("Agent", os.getenv("CAI_AGENT_TYPE", "redteam_agent")),
        "model": _prompt("Model", os.getenv("CAI_MODEL", "alias1")),
        "stateful": True,
    }
    session = request("POST", "/sessions", config, session_payload)
    session_id = session["id"]
    print(f"Session created: {session_id}")
    try:
        while True:
            user_input = input("prompt> ").strip()
            if user_input in {"/exit", "quit", "q"}:
                break
            if not user_input:
                continue
            payload = {"input": user_input}
            result = request("POST", f"/sessions/{session_id}/messages", config, payload)
            text_output = result["result"].get("text_output")
            print("--- Response ---")
            print(text_output or json.dumps(result["result"], indent=2))
    finally:
        try:
            request("DELETE", f"/sessions/{session_id}", config)
        except Exception:  # pragma: no cover - cleanup best effort
            pass
 if __name__ == "__main__":
    try:
        main()
    except KeyboardInterrupt:
        sys.exit(1)
--- a/examples/cai_api_tester.py
+++ b/examples/cai_api_tester.py
@ -0,0 +1,178 @@
 """Interactive client for the CAI API (defaults to 127.0.0.1:8080).
 How to use:
  1) Start the backend:
     ALIAS_API_KEY=your_key cai --api --api-host 0.0.0.0 --api-port 8080
  2) Run this client:
     python examples/cai_api_tester.py
 Requires: requests
 """
 from __future__ import annotations
 import json
 import os
 import sys
 from dataclasses import dataclass
 from typing import Any, Dict, List
 import requests
 API_KEY_HEADER = os.getenv("CAI_API_KEY_HEADER", "X-CAI-API-Key")
@dataclass
 class ClientConfig:
    host: str = os.getenv("CAI_API_HOST", "127.0.0.1")
    port: int = int(os.getenv("CAI_API_PORT", "8080"))
    api_key: str = os.getenv("ALIAS_API_KEY", "") or os.getenv("CAI_API_KEY", "")
    @property
    def base_url(self) -> str:
        return f"http://{self.host}:{self.port}/api/v1"
 def _prompt(label: str, default: str | None = None) -> str:
    suffix = f" [{default}]" if default else ""
    value = input(f"{label}{suffix}: ").strip()
    return default if (not value and default is not None) else value
 def _print_json(data: Any) -> None:
    print(json.dumps(data, indent=2, ensure_ascii=False))
 def _request(method: str, path: str, cfg: ClientConfig, payload: Dict[str, Any] | None = None):
    url = f"{cfg.base_url}{path}"
    headers = {"Content-Type": "application/json"}
    if cfg.api_key:
        headers[API_KEY_HEADER] = cfg.api_key
    r = requests.request(method, url, json=payload, headers=headers, timeout=60)
    if r.status_code >= 400:
        raise RuntimeError(f"{r.status_code} {r.reason}: {r.text}")
    return r.json() if r.text else None
 def main():
    print("=== CAI API Tester (default 8080) ===")
    cfg = ClientConfig()
    # Quick configuration at startup
    cfg.host = _prompt("Host", cfg.host) or cfg.host
    cfg.port = int(_prompt("Port", str(cfg.port)) or cfg.port)
    cfg.api_key = _prompt("API Key (ALIAS_API_KEY)", cfg.api_key)
    state: dict = {"last_session": None}
    def action_health():
        _print_json(_request("GET", "/health", cfg))
    def action_list_commands():
        _print_json(_request("GET", "/commands", cfg))
    def action_run_command():
        name = _prompt("Command (e.g. memory)")
        args_str = _prompt("Args separated by spaces (optional)")
        args = args_str.split() if args_str else []
        _print_json(
            _request(
                "POST",
                f"/commands/{name}",
                cfg,
                {"args": args, "auto_correct": True},
            )
        )
    def action_create_session():
        agent = _prompt("Agent", os.getenv("CAI_AGENT_TYPE", "redteam_agent"))
        model = _prompt("Model", os.getenv("CAI_MODEL", "alias1"))
        stateful = (_prompt("Stateful [true/false]", "true").lower() != "false")
        resp = _request("POST", "/sessions", cfg, {"agent": agent, "model": model, "stateful": stateful})
        state["last_session"] = resp["id"]
        print(f"Created session: {state['last_session']}")
        _print_json(resp)
    def action_list_sessions():
        _print_json(_request("GET", "/sessions", cfg))
    def action_session_detail():
        _print_json(_request("GET", f"/sessions/{_ensure_session(state['last_session'])}", cfg))
    def action_send_message():
        prompt = _prompt("Prompt")
        _print_json(
            _request(
                "POST",
                f"/sessions/{_ensure_session(state['last_session'])}/messages",
                cfg,
                {"input": prompt},
            )
        )
    def action_history():
        _print_json(_request("GET", f"/sessions/{_ensure_session(state['last_session'])}/history", cfg))
    def action_reset():
        _print_json(_request("POST", f"/sessions/{_ensure_session(state['last_session'])}/reset", cfg))
    def action_delete():
        _request("DELETE", f"/sessions/{_ensure_session(state['last_session'])}", cfg)
        print("Session deleted")
        if state["last_session"]:
            state["last_session"] = None
    menu = {
        "1": ("Healthcheck", action_health),
        "1a": ("List agents", lambda: _print_json(_request("GET", "/agents", cfg))),
        "1b": ("List models", lambda: _print_json(_request("GET", "/models", cfg))),
        "2": ("List commands", action_list_commands),
        "3": ("Run command", action_run_command),
        "4": ("Create session", action_create_session),
        "5": ("List sessions", action_list_sessions),
        "6": ("Session detail", action_session_detail),
        "7": ("Send message", action_send_message),
        "8": ("History", action_history),
        "9": ("Reset session", action_reset),
        "10": ("Delete session", action_delete),
        "0": ("Exit", None),
    }
    while True:
        try:
            print("\nOptions:")
            for k in sorted(menu.keys(), key=lambda x: int(x) if x.isdigit() else 99):
                print(f"  {k}) {menu[k][0]}")
            choice = input("> ").strip()
            if choice == "0":
                break
            action = menu.get(choice)
            if not action:
                print("Invalid option")
                continue
            result = action[1]()
            # If an action returned data (and it wasn't printed already), pretty-print it
            if result is not None and not isinstance(result, bool):
                _print_json(result)
        except KeyboardInterrupt:
            print("\nExiting...")
            break
        except Exception as e:
            print(f"Error: {e}")
 def _ensure_session(current: str | None) -> str:
    if current:
        return current
    sid = input("Session ID: ").strip()
    if not sid:
        raise RuntimeError("A session ID is required. Create a session first.")
    return sid
 if __name__ == "__main__":
    try:
        main()
    except KeyboardInterrupt:
        sys.exit(1)
--- a/examples/datasets/pentest-R1/findjsonl.sh
+++ b/examples/datasets/pentest-R1/findjsonl.sh
@ -0,0 +1,65 @@
 #!/usr/bin/env bash
 # Lists all .jsonl files as: DATE SIZE PATH
 # Uses creation date if available, else modification date.
 # Usage:
 #   ./list_jsonl_paths.sh --path /your/input/dir
 # or
 #   ./list_jsonl_paths.sh               # will use the default INPUT_DIR
 set -e
 # ============================================================
 # DEFAULT VARIABLES
 # ============================================================
 INPUT_DIR="/caiextensions/caiextensions-memory"
 OUTPUT_FILE="logs.txt"
 # ============================================================
 # Parse arguments
 # ============================================================
 while [[ "$#" -gt 0 ]]; do
  case "$1" in
    --path)
      INPUT_DIR="$2"
      shift 2
      ;;
    *)
      echo "❌ Unknown option: $1"
      echo "Usage: $0 [--path <input_directory>]"
      exit 1
      ;;
  esac
 done
 # ============================================================
 # Ensure output directory exists
 # ============================================================
 mkdir -p "$(dirname "$OUTPUT_FILE")"
 # ============================================================
 # Build the list
 # ============================================================
 # We emit: <date> <size> <path>
 # date: creation (%w) if present, else modification (%y)
 : > "$OUTPUT_FILE"  # truncate
 echo "📂 Input directory: $INPUT_DIR"
 echo "📝 Output file:     $OUTPUT_FILE"
 echo
 find "$INPUT_DIR" -type f -name "*.jsonl" -print0 |
 while IFS= read -r -d '' file; do
  # Get birth (%w), mod (%y), size (%s), path (%n)
  IFS='|' read -r birth mod size path < <(stat -c '%w|%y|%s|%n' "$file")
  date_field="$birth"
  # If birth time unavailable ('-' or empty), use modification time
  if [[ -z "$date_field" || "$date_field" == "-" ]]; then
    date_field="$mod"
  fi
  printf '%s %s %s\n' "$date_field" "$size" "$path" >> "$OUTPUT_FILE"
 done
 # Sort (optional): by date then size
 sort -o "$OUTPUT_FILE" "$OUTPUT_FILE"
 echo "✅ Wrote $(wc -l < "$OUTPUT_FILE" | tr -d ' ') entries to $OUTPUT_FILE"
--- a/examples/datasets/pentest-R1/load_to_pentestr1.py
+++ b/examples/datasets/pentest-R1/load_to_pentestr1.py
@ -0,0 +1,70 @@
 import os
 import json
 import re
 import numpy as np
 from datasets import Dataset
 def load_step_data(step_folder):
    all_samples = []
    for filename in os.listdir(step_folder):
        if filename.endswith(".json"):
            filepath = os.path.join(step_folder, filename)
            with open(filepath, "r") as f:
                data = json.load(f)
                initial_prompt = data.get("initial_prompt", "")
                SYSTEM_PROMPT = data.get("system_prompt", "")  # MODIFIED
                for step in data.get("steps", []):
                    if step["step_number"] > 1:
                        user_content = f"Initial Description of Penetration Testing Task: {initial_prompt}"
                        for prev_step in data.get("steps", [])[
                            : step["step_number"] - 1
                        ]:
                            user_content += f"\n===Step {prev_step['step_number']}==="
                            user_content += f"\nThought: {prev_step['thought']}"
                            user_content += f"\nCommand: {prev_step['command']}"
                            user_content += f"\nObservation: {prev_step['result']}"
                        user_content += "\nWhat are the next steps and actions?"
                    else:
                        user_content = f"Initial Description of Penetration Testing Task: {initial_prompt}\nWhat are the next instruction and commands?"
                    content = f"""You will guide the penetration testing process step by step. At each stage, you will receive the context from the previous steps. Based on that information, you must determine the next logical action to advance the assessment. Continue this process until the penetration test is fully completed.
 ### Response Format (Always follow this format exactly):
 === Step i ===
 Thought: [Explain your reasoning and goals here.]
 Command: [Write the full command you would run.]
 ### Previous Steps Context
 {user_content}"""
                    assistant_content = f"===Step {step['step_number']}===\nThought: {step['thought']}\nCommand: {step['command']}"
                    sample = {
                        "prompt": [
                            {"role": "system", "content": SYSTEM_PROMPT},
                            {"role": "user", "content": content},
                        ],
                        "answer": assistant_content,
                    }
                    all_samples.append(sample)
    return Dataset.from_list(all_samples)
 def main():
    dataset = load_step_data("data/walkthroughs")
    print(dataset)
    print(dataset.features)
    print(dataset.column_names)
    answers = dataset["answer"]
    prompts = dataset["prompt"]
    print(
        answers[:2]
    )  # increasing the index is going to increase the the number of steps visualized
 main()
--- a/examples/datasets/pentest-R1/readme.MD
+++ b/examples/datasets/pentest-R1/readme.MD
@ -0,0 +1,35 @@
 # Convert CAI-logs to  walkthroughs to be loaded on Pentest-R1
 ## Overview
 This tool converts CAI logs with "history from jsonl format" into pentestR1 "walkthroughs" files. Then is possible to load the CAI walkthroughs  on pentest-R1 and convert the files to the final text string for embeddings. The "history from jsonl format" of CAI were generated using the function in dataset.py
 ```python
 messages = cai.sdk.agents.run_to_json.load_history_from_jsonl(jsonl_path, system_prompt=True)
 ```
 ---
 ## Quick Start
 These commands use the paths in dev container where the main repository of cai is in /workspace folder
 1. Find the path logs of caiextensions-memory. A logs.txt file will be generated in the  current folder.
    ```sh
    bash findjsonl.sh --path /path/to/caiextensions/caiextensions-memory
    ```
 2. Convert CAI-messages to walkthroughs with datasets.py. a folder called data with a subfolder called walkthroughs will be created.
    ```sh
    python3 /workspace/datasets/dataset.py --logs-path logs.txt --num-logs 5 --walkthroughs
    ```
    *Note:  --num-logs optional parameter
 4.  Run the load function of pentest r1 to visualize the CAI walkthrough in pentest-r1 step format:
    ```sh
    python load_to_pentestr1.py
    ```
--- a/examples/mcp/filesystem_example/README.md
+++ b/examples/mcp/filesystem_example/README.md
@ -21,6 +21,6 @@ It's only given access to the `sample_files` directory adjacent to the example,
 Under the hood:
 1. The server is spun up in a subprocess, and exposes a bunch of tools like `list_directory()`, `read_file()`, etc.
-2. We add the server instance to the Agent via `mcp_agents`.
+2. We add the server instance to the Agent via `mcp_servers=[...]`.
-3. Each time the agent runs, we call out to the MCP server to fetch the list of tools via `server.list_tools()`.
+3. Each time the agent runs, we call out to the MCP server to fetch the list of tools via `server.list_tools()`. The result can be cached when configured.
-4. If the LLM chooses to use an MCP tool, we call the MCP server to run the tool via `server.run_tool()`.
+4. If the LLM chooses to use an MCP tool, the runtime calls the server via `server.call_tool()`.
--- a/examples/mcp/git_example/README.md
+++ b/examples/mcp/git_example/README.md
@ -10,7 +10,7 @@ uv run python examples/mcp/git_example/main.py
 ## Details
-The example uses the `MCPServerStdio` class from `agents.mcp`, with the command:
+The example uses `MCPServerStdio` from `agents.mcp` (see `main.py`; parallel types live under `cai.sdk.agents.mcp` in the framework), with the command:
 ```bash
 uvx mcp-server-git
@ -21,6 +21,6 @@ Prior to running the agent, the user is prompted to provide a local directory pa
 Under the hood:
 1. The server is spun up in a subprocess, and exposes a bunch of tools like `git_log()`
-2. We add the server instance to the Agent via `mcp_agents`.
+2. We add the server instance to the Agent via `mcp_servers=[...]`.
-3. Each time the agent runs, we call out to the MCP server to fetch the list of tools via `server.list_tools()`. The result is cached.
+3. Each time the agent runs, we call out to the MCP server to fetch the list of tools via `server.list_tools()`. The result can be cached when configured.
-4. If the LLM chooses to use an MCP tool, we call the MCP server to run the tool via `server.run_tool()`.
+4. If the LLM chooses to use an MCP tool, the runtime calls the server via `server.call_tool()`.
--- a/examples/mcp/sse_example/README.md
+++ b/examples/mcp/sse_example/README.md
@ -5,9 +5,9 @@ This example uses a local SSE server in [server.py](server.py).
 Run the example via:
 ```
-uv run python  python examples/mcp/sse_example/main.py
+uv run python examples/mcp/sse_example/main.py
 ```
 ## Details
-The example uses the `MCPServerSse` class from `agents.mcp`. The server runs in a sub-process at `https://localhost:8000/sse`.
+The example uses `MCPServerSse` from `agents.mcp` (see `main.py`; same MCP server types as in CAI’s `cai.sdk.agents.mcp`). The SSE URL must match your server (often `http://localhost:8000/sse`).
--- a/examples/model_providers/README.md
+++ b/examples/model_providers/README.md
@ -46,5 +46,5 @@ curl -s http://localhost:4000/v1/chat/completions -H "Content-Type: application/
 When using virtual keys:
 ```bash
-curl -s http://localhost:4000/v1/chat/completions -H "Content-Type: application/json" -H "Authorization: Bearer REDACTED_EXAMPLE_KEY" -d '{"model": "gpt-4o-mini", "messages": [{"role": "user", "content": "Say hi"}], "max_tokens": 10}' | jq
+curl -s http://localhost:4000/v1/chat/completions -H "Content-Type: application/json" -H "Authorization: Bearer sk-pNCn8ZA0SCtWMpkZNUWe5g" -d '{"model": "gpt-4o-mini", "messages": [{"role": "user", "content": "Say hi"}], "max_tokens": 10}' | jq
 ```
--- a/examples/model_providers/litellm.py
+++ b/examples/model_providers/litellm.py
@ -1,7 +1,7 @@
 import os
 from dotenv import load_dotenv
 from openai import AsyncOpenAI
-from cai.sdk.agents import OpenAIChatCompletionsModel,Agent,Runner
+from cai.sdk.agents import OpenAIChatCompletionsModel, Agent, Runner
 from cai.sdk.agents import set_default_openai_client, set_tracing_disabled
 from openai.types.responses import ResponseTextDeltaEvent
@ -9,26 +9,27 @@ from openai.types.responses import ResponseTextDeltaEvent
 load_dotenv()
 external_client = AsyncOpenAI(
-    base_url = os.getenv('LITELLM_BASE_URL', 'http://localhost:4000'),
+    base_url=os.getenv("LITELLM_BASE_URL", "http://localhost:4000"),
-    api_key=os.getenv('LITELLM_API_KEY', 'key'))
+    api_key=os.getenv("LITELLM_API_KEY", "key"),
 )
 set_default_openai_client(external_client)
 set_tracing_disabled(True)
 # llm_model=os.getenv('LLM_MODEL', 'gpt-4o')
 # llm_model=os.getenv('LLM_MODEL', 'claude-3-7')
-llm_model=os.getenv('LLM_MODEL', 'qwen2.5:14b')
+llm_model = os.getenv("LLM_MODEL", "qwen2.5:14b")
 # For Qwen models, we need to skip system instructions as they're not supported
 instructions = None if "qwen" in llm_model.lower() else "You are a helpful assistant"
 agent = Agent(
-    name="Assistant", 
+    name="Assistant",
    instructions=instructions,
    model=OpenAIChatCompletionsModel(
        model=llm_model,
        openai_client=external_client,
-    )
+    ),
 )
@ -51,4 +52,4 @@ async def stream_jokes():
 # asyncio.run(stream_jokes())
 # sync
-run_sync_haiku()
+run_sync_haiku()
--- a/mkdocs.yml
+++ b/mkdocs.yml
@ -23,72 +23,18 @@ theme:
 repo_url: https://github.com/aliasrobotics/cai
 repo_name: aliasrobotics/cai
 nav:
  # ========================================
  # GETTING STARTED (Simplificado)
  # ========================================
  - Getting Started:
      - Welcome: index.md
      - Installation: cai_installation.md
      - Quickstart: cai_quickstart.md
-      - Commands reference: cli/commands_reference.md
+      - Available Models: cai_list_of_models.md
      - Model Providers:
          - OpenRouter: providers/openrouter.md
          - Ollama: providers/ollama.md
          - Azure OpenAI: providers/azure.md
-  # ========================================
+  - '🚀 CAI PRO': cai_pro.md
  # CAI PRO (EXPANDIDO - COMERCIAL) ⭐
  # ========================================
  - 'Professional Edition':
      - '🤔 Why CAI PRO?': cai_pro.md
      - '⭐ Alias1 Model': cai_pro_alias1.md
      - '🔥 Exclusive Features': cai_pro_features.md
      - '⭐ API Server (CAI PRO)': api.md
      - '💰 Pricing & Plans': cai_pro_pricing.md
      - '🚀 Get Started with PRO': cai_pro_quickstart.md
      - '📞 Contact Sales': cai_pro_contact.md
  # ========================================
  # USER INTERFACES (NUEVO AGRUPAMIENTO)
  # ========================================
  - User Interfaces:
      # Mobile UI (MUI) - Subsección
      - Mobile UI (iOS):
          - Overview: mui/mui_index.md
          - Getting Started: mui/getting_started.md
          - User Interface: mui/user_interface.md
          - Gestures & Shortcuts: mui/gestures_shortcuts.md
          - Chat Features: mui/chat_features.md
      # Terminal UI (TUI) - Subsección
      - Terminal UI (TUI):
          - Overview: tui/tui_index.md
          - Getting Started: tui/getting_started.md
          - User Interface: tui/user_interface.md
          - Terminals Management: tui/terminals_management.md
          - Teams & Parallel Execution: tui/teams_and_parallel_execution.md
          - Sidebar Features: tui/sidebar_features.md
          - Keyboard Shortcuts: tui/keyboard_shortcuts.md
          - Commands Reference: tui/commands_reference.md
          - Advanced Features: tui/advanced_features.md
          - Troubleshooting: tui/troubleshooting.md
      # Command Line Interface (CLI) - Subsección
      - Command Line Interface (CLI):
          - Overview: cli/cli_index.md
          - Getting Started: cli/getting_started.md
          - Commands Reference: cli/commands_reference.md
          - Advanced Usage: cli/advanced_usage.md
  # ========================================
  # SUPPORTING OTHER CLIs
  # ========================================
  - Supporting Other CLIs:
      - Claude Code: other_cli/claude_code.md
      - Codex CLI: other_cli/codex.md
      - OpenCode: other_cli/opencode.md
  # ========================================
  # CORE CONCEPTS
  # ========================================
  - Core Concepts:
      - Architecture: cai_architecture.md
      - Agents: agents.md
@ -96,35 +42,6 @@ nav:
      - Handoffs: handoffs.md
      - Multi-Agent Systems: multi_agent.md
  # ========================================
  # MODELS & PROVIDERS (REORGANIZADO)
  # ========================================
  - Models & Providers:
      # TODO: Crear página destacada para Alias Models
      # - '⭐ Alias Models (CAI PRO)': cai_alias_models.md
      - Available Models: cai_list_of_models.md
      - Model Providers:
          - OpenRouter: providers/openrouter.md
          - Ollama: providers/ollama.md
          - Azure OpenAI: providers/azure.md
  # ========================================
  # GUIDES
  # ========================================
  - Guides:
      - Running Agents: running_agents.md
      - Continue Mode: continue_mode.md
      - Session Resume: session_resume.md
      - Working with Results: results.md
      - Streaming: streaming.md
      - Tracing & Debugging: tracing.md
      - Context Management: context.md
      - Guardrails & Security: guardrails.md
      - Environment Variables: environment_variables.md
  # ========================================
  # BENCHMARKING (CREDIBILIDAD)
  # ========================================
  - Benchmarking:
      - Overview: benchmarking/overview.md
      - Running Benchmarks: benchmarking/running_benchmarks.md
@ -134,17 +51,43 @@ nav:
      - Knowledge Benchmarks: benchmarking/knowledge_benchmarks.md
      - Privacy Benchmarks: benchmarking/privacy_benchmarks.md
-  # ========================================
+  - Guides:
-  # RESEARCH (CREDIBILIDAD CIENTÍFICA)
+      - Running Agents: running_agents.md
-  # ========================================
+      - Continue Mode: continue_mode.md
-  - Research:
+      - Working with Results: results.md
-      - Overview: research.md
+      - Streaming: streaming.md
-      # TODO: Opcional - expandir research
+      - Tracing & Debugging: tracing.md
-      # - Publications: research_publications.md
+      - Context Management: context.md
      - Guardrails & Security: guardrails.md
      - Environment Variables: environment_variables.md
      - Packet Capture on WSL2: cai/getting-started/packet_capture_wsl.md
  - Troubleshooting:
      - Operator Feedback Reproduction: cai/troubleshooting/operator_feedback_reproduction.md
      - Platform Limitations: cai/troubleshooting/platform_limitations.md
  - Case Studies:
      - Operator Artifact Evidence: cai/case-studies/operator-artifact-evidence.md
  - Mobile UI (iOS):
      - Overview: mui/mui_index.md
      - Getting Started: mui/getting_started.md
      - User Interface: mui/user_interface.md
      - Gestures & Shortcuts: mui/gestures_shortcuts.md
      - Chat Features: mui/chat_features.md
  - Terminal UI (TUI) - Deprecated:
      - Overview: tui/tui_index.md
      - Getting Started: tui/getting_started.md
      - User Interface: tui/user_interface.md
      - Terminals Management: tui/terminals_management.md
      - Teams & Parallel Execution: tui/teams_and_parallel_execution.md
      - Sidebar Features: tui/sidebar_features.md
      - Keyboard Shortcuts: tui/keyboard_shortcuts.md
      - Commands Reference: tui/commands_reference.md
      - Advanced Features: tui/advanced_features.md
      - Troubleshooting: tui/troubleshooting.md
  # ========================================
  # API REFERENCE (TÉCNICO)
  # ========================================
  - API Reference:
      - Agents:
          - Agent: ref/agent.md
@ -170,15 +113,13 @@ nav:
          - Handoff Filters: ref/extensions/handoff_filters.md
          - Handoff Prompt: ref/extensions/handoff_prompt.md
  # ========================================
  # ADVANCED
  # ========================================
  - Advanced:
      # - Benchmarks: cai_benchmark.md
      - Development: cai_development.md
-  # ========================================
+  - Research:
-  # RESOURCES
+      - Overview: research.md
-  # ========================================
+
  - Resources:
      - FAQ: cai_faq.md
      - Find Us: cai_find_us.md
@ -190,8 +131,9 @@ plugins:
      handlers:
        python:
          paths: ["src"]
-          options:
+          selection:
            docstring_style: google
          options:
            # Shows links to other members in signatures
            signature_crossrefs: true
            # Orders members by source order, rather than alphabetical
--- a/pricing.json
+++ b/pricing.json
@ -1,52 +0,0 @@
 {
    "alias0": {
        "max_tokens": 128000,
        "max_input_tokens": 200000,
        "max_output_tokens": 128000,
        "input_cost_per_token": 0.000005,
        "output_cost_per_token": 0.000005,
        "cache_creation_input_token_cost": 0.000005,
        "cache_read_input_token_cost": 0.0000005,
        "search_context_cost_per_query": {
            "search_context_size_low": 1e-2,
            "search_context_size_medium": 1e-2,
            "search_context_size_high": 1e-2
        },
        "litellm_provider": "openai",
        "mode": "chat",
        "supports_function_calling": true,
        "supports_vision": true,
        "tool_use_system_prompt_tokens": 159,
        "supports_assistant_prefill": true,
        "supports_pdf_input": true,
        "supports_prompt_caching": true,
        "supports_response_schema": true,
        "deprecation_date": "2026-02-01",
        "supports_tool_choice": true
    },
    "alias0-fast": {
        "max_tokens": 64000,
        "max_input_tokens": 100000,
        "max_output_tokens": 64000,
        "input_cost_per_token": 0.000005,
        "output_cost_per_token": 0.000005,
        "cache_creation_input_token_cost": 0.000005,
        "cache_read_input_token_cost": 0.0000005,
        "search_context_cost_per_query": {
            "search_context_size_low": 1e-2,
            "search_context_size_medium": 1e-2,
            "search_context_size_high": 1e-2
        },
        "litellm_provider": "openai",
        "mode": "chat",
        "supports_function_calling": true,
        "supports_vision": true,
        "tool_use_system_prompt_tokens": 159,
        "supports_assistant_prefill": true,
        "supports_pdf_input": true,
        "supports_prompt_caching": true,
        "supports_response_schema": true,
        "deprecation_date": "2026-02-01",
        "supports_tool_choice": true
    }
 }
--- a/pricings/.gitkeep
+++ b/pricings/.gitkeep
@ -0,0 +1 @@
--- a/pricings/native_pricing.json
+++ b/pricings/native_pricing.json
--- a/pricings/pricing.json
+++ b/pricings/pricing.json
@ -0,0 +1,149 @@
 {
    "alias2": {
        "max_tokens": 128000,
        "max_input_tokens": 200000,
        "max_output_tokens": 128000,
        "input_cost_per_token": 0.000005,
        "output_cost_per_token": 0.000005,
        "cache_creation_input_token_cost": 0.000005,
        "cache_read_input_token_cost": 0.0000005,
        "search_context_cost_per_query": {
            "search_context_size_low": 1e-2,
            "search_context_size_medium": 1e-2,
            "search_context_size_high": 1e-2
        },
        "litellm_provider": "openai",
        "mode": "chat",
        "supports_function_calling": true,
        "supports_vision": true,
        "tool_use_system_prompt_tokens": 159,
        "supports_assistant_prefill": true,
        "supports_pdf_input": true,
        "supports_prompt_caching": true,
        "supports_response_schema": true,
        "deprecation_date": "2030-02-01",
        "supports_tool_choice": true
    },
    "alias1": {
        "max_tokens": 128000,
        "max_input_tokens": 200000,
        "max_output_tokens": 128000,
        "input_cost_per_token": 0.000005,
        "output_cost_per_token": 0.000005,
        "cache_creation_input_token_cost": 0.000005,
        "cache_read_input_token_cost": 0.0000005,
        "search_context_cost_per_query": {
            "search_context_size_low": 1e-2,
            "search_context_size_medium": 1e-2,
            "search_context_size_high": 1e-2
        },
        "litellm_provider": "openai",
        "mode": "chat",
        "supports_function_calling": true,
        "supports_vision": true,
        "tool_use_system_prompt_tokens": 159,
        "supports_assistant_prefill": true,
        "supports_pdf_input": true,
        "supports_prompt_caching": true,
        "supports_response_schema": true,
        "deprecation_date": "2030-02-01",
        "supports_tool_choice": true
    },
    "openai/Qwen/Qwen3-Coder-480B-A35B-Instruct": {
        "max_tokens": 128000,
        "max_input_tokens": 200000,
        "max_output_tokens": 128000,
        "input_cost_per_token": 0.000005,
        "output_cost_per_token": 0.000005,
        "cache_creation_input_token_cost": 0.000005,
        "cache_read_input_token_cost": 0.0000005,
        "search_context_cost_per_query": {
            "search_context_size_low": 1e-2,
            "search_context_size_medium": 1e-2,
            "search_context_size_high": 1e-2
        },
        "litellm_provider": "openai",
        "mode": "chat",
        "supports_function_calling": true,
        "supports_vision": true,
        "tool_use_system_prompt_tokens": 159,
        "supports_assistant_prefill": true,
        "supports_pdf_input": true,
        "supports_prompt_caching": true,
        "supports_response_schema": true,
        "deprecation_date": "2026-02-01",
        "supports_tool_choice": true
    },
    "openrouter/z-ai/glm-4.5": {
        "max_tokens": 128000,
        "max_input_tokens": 200000,
        "max_output_tokens": 128000,
        "input_cost_per_token": 0.0000005,
        "output_cost_per_token": 0.000002,
        "cache_creation_input_token_cost": 0.0000005,
        "cache_read_input_token_cost": 0.00000005,
        "search_context_cost_per_query": {
            "search_context_size_low": 1e-2,
            "search_context_size_medium": 1e-2,
            "search_context_size_high": 1e-2
        },
        "litellm_provider": "openrouter",
        "mode": "chat",
        "supports_function_calling": true,
        "supports_vision": true,
        "tool_use_system_prompt_tokens": 159,
        "supports_assistant_prefill": true,
        "supports_pdf_input": true,
        "supports_prompt_caching": true,
        "supports_response_schema": true,
        "supports_tool_choice": true
    },
    "alias3": {
        "max_tokens": 128000,
        "max_input_tokens": 200000,
        "max_output_tokens": 128000,
        "input_cost_per_token": 0.000001,
        "output_cost_per_token": 0.000002,
        "cache_creation_input_token_cost": 0.000001,
        "cache_read_input_token_cost": 0.0000001,
        "search_context_cost_per_query": {
            "search_context_size_low": 1e-2,
            "search_context_size_medium": 1e-2,
            "search_context_size_high": 1e-2
        },
        "litellm_provider": "openai",
        "mode": "chat",
        "supports_function_calling": true,
        "supports_vision": true,
        "tool_use_system_prompt_tokens": 159,
        "supports_assistant_prefill": true,
        "supports_pdf_input": false,
        "supports_prompt_caching": true,
        "supports_response_schema": true,
        "supports_tool_choice": true
    },
    "alias2-mini": {
        "max_tokens": 128000,
        "max_input_tokens": 200000,
        "max_output_tokens": 128000,
        "input_cost_per_token": 0.000001,
        "output_cost_per_token": 0.000002,
        "cache_creation_input_token_cost": 0.000001,
        "cache_read_input_token_cost": 0.0000001,
        "search_context_cost_per_query": {
            "search_context_size_low": 1e-2,
            "search_context_size_medium": 1e-2,
            "search_context_size_high": 1e-2
        },
        "litellm_provider": "openai",
        "mode": "chat",
        "supports_function_calling": true,
        "supports_vision": true,
        "tool_use_system_prompt_tokens": 159,
        "supports_assistant_prefill": true,
        "supports_pdf_input": false,
        "supports_prompt_caching": true,
        "supports_response_schema": true,
        "supports_tool_choice": true
    }
 }
--- a/pyproject.toml
+++ b/pyproject.toml
@ -1,61 +1,87 @@
 [project]
 name = "cai-framework"
-version = "0.5.10"
+version = "1.1.5"
 description = "Cybersecurity AI Framework"
 readme = "README.md"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
 license = {text = "Dual-licensed MIT and Proprietary"}
 authors = [{ name = "Alias Robotics", email = "research@aliasrobotics.com" }]
 dependencies = [
-    # logging and visualization
+    # --- Core (always needed) ---
-    "folium>=0.15.0, <1",
+    "openai>=1.75.0",
-    "matplotlib>=3.0, <4",
+    "httpx>=0.27",                     # Direct HTTP for alias models [N]
    "numpy>=1.21, <3",
    "pandas>=1.3, <3",
    # core cai
    "openai==1.75.0",
    "pydantic>=2.10, <3",
    "griffe>=1.5.6, <2",
    "typing-extensions>=4.12.2, <5",
-    "requests>=2.0, <3",
+    "rich",
    "types-requests>=2.0, <3",
    "openinference-instrumentation-openai>=0.1.22",
    "wasabi>=1.1.3",
    "rich>=13.9.4",
    "prompt_toolkit>=3.0.39",
-    "dotenv>=0.9.9",
+    "questionary>=2.0.1",
-    "litellm[proxy]>=1.63.7",
+    "python-dotenv>=0.19.0",
-    "mako>=1.3.8",
+    "pytz>=2024.1",                    # DataRecorder timestamps (run_to_jsonl); not always transitive
-    "mcp; python_version >= '3.10'",
+    "PyYAML",
-    "mkdocs>=1.6.0",
+    "wasabi>=1.1.3",
-    "mkdocs-material>=9.6.0",
+    "tiktoken>=0.5",                   # Token counting
-    "paramiko>=3.5.1",
+    "mako>=1.3.8",                     # Prompt templates
-    "dnspython",
+    "griffe>=1.5.6, <2",              # Function schema generation
-    "flask",
+    # --- LLM routing (still needed for non-alias models + proxy) ---
-    "networkx",
+    "litellm[proxy]>=1.80.5",
-    "PyPDF2",
+    # --- TUI ---
    "textual>=0.86.0",
    # --- Networking & Tools ---
    "requests>=2.0, <3",
    "paramiko>=3.5.1",                 # SSH
    "docker",                          # Container management
    "cryptography>=42, <44",
    # --- Web fetch tool (fetch_url) ---
    "trafilatura>=2.0",                # HTML -> Markdown main-content extraction
    "curl-cffi>=0.7",                  # Anti-bot fallback (Chrome TLS fingerprint)
    "pypdf>=4.0",                      # PDF text extraction (BSD-licensed successor of PyPDF2)
    # --- API server ---
    "uvicorn<=1",
    "fastapi>=0.115.5, <1",
    # --- Misc (used in specific modules) ---
    "mcp",                             # Model Context Protocol
    "nest_asyncio",                    # Async compat in sync contexts
 ]
 classifiers = [
    "Typing :: Typed",
    "Intended Audience :: Developers",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.9",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Intended Audience :: Developers",
    "Operating System :: OS Independent",
    "Topic :: Software Development :: Libraries :: Python Modules",
    "License :: OSI Approved :: MIT License",
 ]
 [project.urls]
-Homepage = "https://github.com/openai/openai-agents-python"
+Homepage = "https://github.com/aliasrobotics/cai"
-Repository = "https://github.com/openai/openai-agents-python"
+Repository = "https://github.com/aliasrobotics/cai"
 Documentation = "https://aliasrobotics.github.io/cai/"
 Issues = "https://github.com/aliasrobotics/cai/issues"
 [project.optional-dependencies]
-voice = ["numpy>=2.2.0, <3; python_version>='3.10'", "websockets>=15.0, <16"]
+voice = ["numpy>=2.2.0", "websockets>=15.0"]
-viz = ["graphviz>=0.17"]
+viz = ["graphviz>=0.17", "matplotlib>=3.0,<4", "networkx", "folium>=0.15.0,<1"]
 data = ["numpy>=1.21,<3", "pandas>=1.3,<3", "scipy"]
 # CTR graph canvas in the TUI needs networkx; full CTR solver needs numpy/scipy (see data/viz).
 tui = ["networkx", "numpy>=1.21,<3", "scipy"]
 docs = ["mkdocs>=1.6.0", "mkdocs-material>=9.6.0"]
 mail = ["dnspython"]
 pdf = ["PyPDF2"]
 web = ["flask"]
 telemetry = ["openinference-instrumentation-openai>=0.1.22"]
 test = [
    "pytest",
    "pytest-asyncio",
    "pytest-mock>=3.14.0",
    "pytest-timeout",
    "inline-snapshot>=0.20.7",
    "graphviz>=0.17",
 ]
 all = [
    "cai-framework[viz,data,tui,docs,mail,pdf,web,telemetry,test]",
 ]
 [dependency-groups]
 dev = [
@ -76,17 +102,12 @@ dev = [
    "sounddevice",
    "textual",
    "websockets",
-    "litellm",
+    "litellm>=1.63.7",
    "prisma",
    "graphviz",
    "nest_asyncio"
 ]
 [tool.uv.workspace]
 members = ["agents"]
 [tool.uv.sources]
 agents = { workspace = true }
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"
@ -107,14 +128,20 @@ exclude = [
    "*.egg-info/",
    "workspaces/",
    "benchmarks/",
    "src/cai/caibench/",
    "logs/",
    "site/",
    ".cai/",
    "nohup.out",
    "ci/",
    "tools/cut_the_rope/",
    "private/",
    "data/",
    "tools/license_check.py",
    "tools/create_root_user.sh",
    "media/",
-    "docs/media/"
+    "docs/media/",
    "docs"
 ]
 [tool.hatch.build.targets.wheel]
@ -140,8 +167,17 @@ exclude = [
    "nohup.out",
    "ci/",
    "tools/cut_the_rope/",
    "private/",
    "data/",
    "tools/license_check.py",
    "tools/create_root_user.sh",
    "media/",
-    "docs/media/"
+    "docs/media/",
    "docs"    
 ]
 # Explicitly include pricing data files
 artifacts = [
    "src/cai/pricings/*.json"
 ]
 [tool.hatch.build.targets.wheel.sources]
@ -171,8 +207,13 @@ exclude = [
    "nohup.out",
    "ci/",
    "tools/cut_the_rope/",
    "private/",
    "data/",
    "tools/license_check.py",
    "tools/create_root_user.sh",
    "media/",
-    "docs/media/"
+    "docs/media/",
    "docs"    
 ]
 [tool.ruff]
@ -230,6 +271,7 @@ filterwarnings = [
 ]
 markers = [
    "allow_call_model_methods: mark test as allowing calls to real model implementations",
    "slow: heavier imports (e.g. full agent graph); exclude with pytest -m \"not slow\" in fast slices",
 ]
 [tool.inline-snapshot]
@ -240,3 +282,5 @@ cai = "cai.cli:main"
 cai-replay = "tools.replay:main"
 cai-asciinema = "tools.asciinema:main"
 cai-gif = "tools.gif:main"
 cai-bench = "cai.caibench.cli:main"
 cai-ctr = "tools.ctr_experiment:main"
--- a/release_to_pypi_public.sh
+++ b/release_to_pypi_public.sh
@ -49,4 +49,4 @@ echo ""
 echo "To test in a clean environment:"
 echo "python3 -m venv test_env"
 echo "source test_env/bin/activate"
-echo "pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple cai-framework"
+echo "pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple cai-framework"
--- a/Show More
+++ b/Show More