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Core Concepts
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Agents
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Agents
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Available Agents
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Quick Start with Agents
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Choosing the Right Agent
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Agent Capabilities Matrix
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Common Agent Workflows
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Scenario 1: Full Web Application Pentest
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Scenario 2: IoT Device Security Assessment
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Scenario 3: Network Incident Response
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Basic Configuration
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Example: one_tool_agent.py
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Context
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Output types
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Handoffs
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Dynamic instructions
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Launch
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Performance Optimization
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Agent Best Practices
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1. Start with the Right Agent
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2. Switch Agents as Needed
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3. Monitor Resource Usage
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Handoffs
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Multi-Agent Systems
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Benchmarking
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User Interface
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Teams & Parallel Execution
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Sidebar Features
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Keyboard Shortcuts
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Commands Reference
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Interface
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OpenAI Chat Completions
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Handoff Filters
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FAQ
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Find Us
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Available Agents
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Quick Start with Agents
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<a href="#choosing-the-right-agent" class="md-nav__link">
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Choosing the Right Agent
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Agent Capabilities Matrix
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Common Agent Workflows
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<a href="#scenario-1-full-web-application-pentest" class="md-nav__link">
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Scenario 1: Full Web Application Pentest
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<a href="#scenario-2-iot-device-security-assessment" class="md-nav__link">
<span class="md-ellipsis">
Scenario 2: IoT Device Security Assessment
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<a href="#scenario-3-network-incident-response" class="md-nav__link">
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Scenario 3: Network Incident Response
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Basic Configuration
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Example: one_tool_agent.py
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<a href="#context" class="md-nav__link">
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Context
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<span class="md-ellipsis">
Output types
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<span class="md-ellipsis">
Handoffs
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<a href="#dynamic-instructions" class="md-nav__link">
<span class="md-ellipsis">
Dynamic instructions
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<span class="md-ellipsis">
Launch
</span>
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Performance Optimization
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Agent Best Practices
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<a href="#1-start-with-the-right-agent" class="md-nav__link">
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1. Start with the Right Agent
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<a href="#2-switch-agents-as-needed" class="md-nav__link">
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2. Switch Agents as Needed
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3. Monitor Resource Usage
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4. Save Successful Sessions
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<article class="md-content__inner md-typeset">
<h1 id="agents">Agents</h1>
<p>Agents are the core of CAI. An agent uses Large Language Models (LLMs), configured with instructions and tools to perform specialized cybersecurity tasks. Each agent is defined in its own <code>.py</code> file in <code>src/cai/agents</code> and optimized for specific security domains.</p>
<h2 id="available-agents">Available Agents</h2>
<p>CAI provides a comprehensive suite of specialized agents for different cybersecurity scenarios:</p>
<table>
<thead>
<tr>
<th>Agent</th>
<th>Description</th>
<th>Primary Use Case</th>
<th>Key Tools</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>redteam_agent</strong></td>
<td>Offensive security specialist for penetration testing</td>
<td>Active exploitation, vulnerability discovery</td>
<td>nmap, metasploit, burp</td>
</tr>
<tr>
<td><strong>blueteam_agent</strong></td>
<td>Defensive security expert for threat mitigation</td>
<td>Security hardening, incident response</td>
<td>wireshark, suricata, osquery</td>
</tr>
<tr>
<td><strong>bug_bounter_agent</strong></td>
<td>Bug bounty hunter optimized for vulnerability research</td>
<td>Web app security, API testing</td>
<td>ffuf, sqlmap, nuclei</td>
</tr>
<tr>
<td><strong>one_tool_agent</strong></td>
<td>Minimalist agent focused on single-tool execution</td>
<td>Quick scans, specific tool operations</td>
<td>Generic Linux commands</td>
</tr>
<tr>
<td><strong>dfir_agent</strong></td>
<td>Digital Forensics and Incident Response expert</td>
<td>Log analysis, forensic investigation</td>
<td>volatility, autopsy, log2timeline</td>
</tr>
<tr>
<td><strong>reverse_engineering_agent</strong></td>
<td>Binary analysis and reverse engineering</td>
<td>Malware analysis, firmware reversing</td>
<td>ghidra, radare2, ida</td>
</tr>
<tr>
<td><strong>memory_analysis_agent</strong></td>
<td>Memory dump analysis specialist</td>
<td>RAM forensics, process analysis</td>
<td>volatility, rekall</td>
</tr>
<tr>
<td><strong>network_traffic_analyzer</strong></td>
<td>Network packet analysis expert</td>
<td>PCAP analysis, traffic inspection</td>
<td>wireshark, tcpdump, tshark</td>
</tr>
<tr>
<td><strong>android_sast_agent</strong></td>
<td>Android Static Application Security Testing</td>
<td>APK analysis, Android vulnerability scanning</td>
<td>jadx, apktool, mobsf</td>
</tr>
<tr>
<td><strong>wifi_security_tester</strong></td>
<td>Wireless network security assessment</td>
<td>WiFi penetration testing, WPA cracking</td>
<td>aircrack-ng, reaver, wifite</td>
</tr>
<tr>
<td><strong>replay_attack_agent</strong></td>
<td>Replay attack execution specialist</td>
<td>Protocol replay, authentication bypass</td>
<td>custom scripts, burp</td>
</tr>
<tr>
<td><strong>subghz_sdr_agent</strong></td>
<td>Sub-GHz SDR signal analysis expert</td>
<td>RF analysis, IoT protocol testing</td>
<td>hackrf, gqrx, urh</td>
</tr>
</tbody>
</table>
<h3 id="quick-start-with-agents">Quick Start with Agents</h3>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-0-1"><span class="c1"># Launch CAI with a specific agent</span>
</span><span id="__span-0-2"><span class="nv">CAI_AGENT_TYPE</span><span class="o">=</span>redteam_agent<span class="w"> </span>cai
</span><span id="__span-0-3">
</span><span id="__span-0-4"><span class="c1"># Launch with custom model</span>
</span><span id="__span-0-5"><span class="nv">CAI_AGENT_TYPE</span><span class="o">=</span>bug_bounter_agent<span class="w"> </span><span class="nv">CAI_MODEL</span><span class="o">=</span>alias0<span class="w"> </span>cai
</span><span id="__span-0-6">
</span><span id="__span-0-7"><span class="c1"># Or switch agents during a session</span>
</span><span id="__span-0-8">CAI&gt;/agent<span class="w"> </span>redteam_agent
</span><span id="__span-0-9">
</span><span id="__span-0-10"><span class="c1"># List all available agents with descriptions</span>
</span><span id="__span-0-11">CAI&gt;/agent<span class="w"> </span>list
</span><span id="__span-0-12">
</span><span id="__span-0-13"><span class="c1"># Get detailed info about a specific agent</span>
</span><span id="__span-0-14">CAI&gt;/agent<span class="w"> </span>info<span class="w"> </span>redteam_agent
</span></code></pre></div>
<h3 id="choosing-the-right-agent">Choosing the Right Agent</h3>
<ul>
<li><strong>For general pentesting</strong>: Start with <code>redteam_agent</code></li>
<li><strong>For web applications</strong>: Use <code>bug_bounter_agent</code></li>
<li><strong>For forensics</strong>: Use <code>dfir_agent</code> or <code>memory_analysis_agent</code></li>
<li><strong>For IoT/embedded</strong>: Try <code>subghz_sdr_agent</code> or <code>reverse_engineering_agent</code></li>
<li><strong>For network security</strong>: Use <code>network_traffic_analyzer</code> or <code>blueteam_agent</code></li>
<li><strong>For mobile apps</strong>: Use <code>android_sast_agent</code></li>
<li><strong>For wireless networks</strong>: Use <code>wifi_security_tester</code></li>
</ul>
<hr />
<h2 id="agent-capabilities-matrix">Agent Capabilities Matrix</h2>
<table>
<thead>
<tr>
<th>Capability</th>
<th>redteam</th>
<th>blueteam</th>
<th>bug_bounty</th>
<th>dfir</th>
<th>reverse_eng</th>
<th>network</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Web App Testing</strong></td>
<td>⭐⭐⭐</td>
<td>⭐</td>
<td>⭐⭐⭐⭐⭐</td>
<td>⭐⭐</td>
<td>⭐</td>
<td>⭐⭐</td>
</tr>
<tr>
<td><strong>Network Analysis</strong></td>
<td>⭐⭐⭐⭐</td>
<td>⭐⭐⭐⭐⭐</td>
<td>⭐⭐</td>
<td>⭐⭐⭐</td>
<td>⭐</td>
<td>⭐⭐⭐⭐⭐</td>
</tr>
<tr>
<td><strong>Binary Analysis</strong></td>
<td>⭐⭐</td>
<td>⭐</td>
<td>⭐</td>
<td>⭐⭐</td>
<td>⭐⭐⭐⭐⭐</td>
<td>⭐</td>
</tr>
<tr>
<td><strong>Forensics</strong></td>
<td>⭐⭐</td>
<td>⭐⭐⭐</td>
<td>⭐</td>
<td>⭐⭐⭐⭐⭐</td>
<td>⭐⭐⭐</td>
<td>⭐⭐</td>
</tr>
<tr>
<td><strong>IoT/Embedded</strong></td>
<td>⭐⭐⭐</td>
<td>⭐⭐</td>
<td>⭐⭐</td>
<td>⭐⭐</td>
<td>⭐⭐⭐⭐</td>
<td>⭐⭐⭐</td>
</tr>
<tr>
<td><strong>API Testing</strong></td>
<td>⭐⭐⭐</td>
<td>⭐⭐</td>
<td>⭐⭐⭐⭐⭐</td>
<td>⭐⭐</td>
<td>⭐</td>
<td>⭐⭐</td>
</tr>
<tr>
<td><strong>Exploit Development</strong></td>
<td>⭐⭐⭐⭐⭐</td>
<td>⭐</td>
<td>⭐⭐⭐</td>
<td>⭐</td>
<td>⭐⭐⭐⭐</td>
<td>⭐</td>
</tr>
</tbody>
</table>
<p><strong>Legend</strong>: ⭐ Limited | ⭐⭐⭐ Moderate | ⭐⭐⭐⭐⭐ Excellent</p>
<hr />
<h2 id="common-agent-workflows">Common Agent Workflows</h2>
<h3 id="scenario-1-full-web-application-pentest">Scenario 1: Full Web Application Pentest</h3>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-1-1"><span class="c1"># 1. Start with reconnaissance</span>
</span><span id="__span-1-2">CAI&gt;/agent<span class="w"> </span>bug_bounter_agent
</span><span id="__span-1-3">CAI&gt;<span class="w"> </span>Scan<span class="w"> </span>https://target.com<span class="w"> </span><span class="k">for</span><span class="w"> </span>vulnerabilities
</span><span id="__span-1-4">
</span><span id="__span-1-5"><span class="c1"># 2. Switch to exploitation</span>
</span><span id="__span-1-6">CAI&gt;/agent<span class="w"> </span>redteam_agent<span class="w"> </span>
</span><span id="__span-1-7">CAI&gt;<span class="w"> </span>Exploit<span class="w"> </span>the<span class="w"> </span>SQL<span class="w"> </span>injection<span class="w"> </span>found<span class="w"> </span>at<span class="w"> </span>/login
</span><span id="__span-1-8">
</span><span id="__span-1-9"><span class="c1"># 3. Post-exploitation analysis</span>
</span><span id="__span-1-10">CAI&gt;/agent<span class="w"> </span>dfir_agent
</span><span id="__span-1-11">CAI&gt;<span class="w"> </span>Analyze<span class="w"> </span>the<span class="w"> </span>logs<span class="w"> </span>to<span class="w"> </span>understand<span class="w"> </span>the<span class="w"> </span>attack<span class="w"> </span>surface
</span></code></pre></div>
<h3 id="scenario-2-iot-device-security-assessment">Scenario 2: IoT Device Security Assessment</h3>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-2-1"><span class="c1"># 1. RF signal analysis</span>
</span><span id="__span-2-2">CAI&gt;/agent<span class="w"> </span>subghz_sdr_agent
</span><span id="__span-2-3">CAI&gt;<span class="w"> </span>Analyze<span class="w"> </span>the<span class="w"> </span>433MHz<span class="w"> </span>signals<span class="w"> </span>from<span class="w"> </span>the<span class="w"> </span>device
</span><span id="__span-2-4">
</span><span id="__span-2-5"><span class="c1"># 2. Firmware analysis</span>
</span><span id="__span-2-6">CAI&gt;/agent<span class="w"> </span>reverse_engineering_agent
</span><span id="__span-2-7">CAI&gt;<span class="w"> </span>Extract<span class="w"> </span>and<span class="w"> </span>analyze<span class="w"> </span>the<span class="w"> </span>firmware<span class="w"> </span>from<span class="w"> </span>dump.bin
</span><span id="__span-2-8">
</span><span id="__span-2-9"><span class="c1"># 3. Memory analysis if device captured</span>
</span><span id="__span-2-10">CAI&gt;/agent<span class="w"> </span>memory_analysis_agent
</span><span id="__span-2-11">CAI&gt;<span class="w"> </span>Analyze<span class="w"> </span>the<span class="w"> </span>memory<span class="w"> </span>dump<span class="w"> </span><span class="k">for</span><span class="w"> </span>secrets
</span></code></pre></div>
<h3 id="scenario-3-network-incident-response">Scenario 3: Network Incident Response</h3>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-3-1"><span class="c1"># 1. Network traffic analysis</span>
</span><span id="__span-3-2">CAI&gt;/agent<span class="w"> </span>network_traffic_analyzer
</span><span id="__span-3-3">CAI&gt;<span class="w"> </span>Analyze<span class="w"> </span>capture.pcap<span class="w"> </span><span class="k">for</span><span class="w"> </span>suspicious<span class="w"> </span>activity
</span><span id="__span-3-4">
</span><span id="__span-3-5"><span class="c1"># 2. Forensic investigation</span>
</span><span id="__span-3-6">CAI&gt;/agent<span class="w"> </span>dfir_agent
</span><span id="__span-3-7">CAI&gt;<span class="w"> </span>Investigate<span class="w"> </span>the<span class="w"> </span>compromised<span class="w"> </span>host<span class="w"> </span>logs
</span><span id="__span-3-8">
</span><span id="__span-3-9"><span class="c1"># 3. Defensive recommendations</span>
</span><span id="__span-3-10">CAI&gt;/agent<span class="w"> </span>blueteam_agent
</span><span id="__span-3-11">CAI&gt;<span class="w"> </span>Provide<span class="w"> </span>mitigation<span class="w"> </span>strategies<span class="w"> </span>based<span class="w"> </span>on<span class="w"> </span>findings
</span></code></pre></div>
<hr />
<h2 id="basic-configuration">Basic Configuration</h2>
<p>Key agent properties include:</p>
<ul>
<li><code>name</code>: Name of the agent (e.g., the name of <code>one_tool_agent</code> is 'CTF Agent')</li>
<li><code>instructions</code>: The system prompt that defines agent behavior</li>
<li><code>model</code>: Which LLM to use, with optional <code>model_settings</code> to configure parameters like temperature, top_p, etc.</li>
<li><code>tools</code>: Tools that the agent can use to achieve its tasks</li>
<li><code>handoffs</code>: Allows an agent to delegate tasks to another agent</li>
</ul>
<h2 id="example-one_tool_agentpy">Example: <code>one_tool_agent.py</code></h2>
<div class="language-python highlight"><pre><span></span><code><span id="__span-4-1"><span class="kn">from</span><span class="w"> </span><span class="nn">cai.sdk.agents</span><span class="w"> </span><span class="kn">import</span> <span class="n">Agent</span><span class="p">,</span> <span class="n">OpenAIChatCompletionsModel</span>
</span><span id="__span-4-2"><span class="kn">from</span><span class="w"> </span><span class="nn">cai.tools.reconnaissance.generic_linux_command</span><span class="w"> </span><span class="kn">import</span> <span class="n">generic_linux_command</span>
</span><span id="__span-4-3"><span class="kn">from</span><span class="w"> </span><span class="nn">openai</span><span class="w"> </span><span class="kn">import</span> <span class="n">AsyncOpenAI</span>
</span><span id="__span-4-4">
</span><span id="__span-4-5"><span class="n">one_tool_agent</span> <span class="o">=</span> <span class="n">Agent</span><span class="p">(</span>
</span><span id="__span-4-6"> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;CTF agent&quot;</span><span class="p">,</span>
</span><span id="__span-4-7"> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Agent focused on conquering security challenges using generic linux commands&quot;</span><span class="p">,</span>
</span><span id="__span-4-8"> <span class="n">instructions</span><span class="o">=</span><span class="s2">&quot;You are a Cybersecurity expert Leader facing a CTF challenge.&quot;</span><span class="p">,</span>
</span><span id="__span-4-9"> <span class="n">tools</span><span class="o">=</span><span class="p">[</span>
</span><span id="__span-4-10"> <span class="n">generic_linux_command</span><span class="p">,</span>
</span><span id="__span-4-11"> <span class="p">],</span>
</span><span id="__span-4-12"> <span class="n">model</span><span class="o">=</span><span class="n">OpenAIChatCompletionsModel</span><span class="p">(</span>
</span><span id="__span-4-13"> <span class="n">model</span><span class="o">=</span><span class="s2">&quot;qwen2.5:14b&quot;</span><span class="p">,</span>
</span><span id="__span-4-14"> <span class="n">openai_client</span><span class="o">=</span><span class="n">AsyncOpenAI</span><span class="p">(),</span>
</span><span id="__span-4-15"> <span class="p">)</span>
</span><span id="__span-4-16"><span class="p">)</span>
</span></code></pre></div>
<h2 id="context">Context</h2>
<p>There are two main context types. See <a href="../context/">context</a> for details.</p>
<p>Agents are generic on their <code>context</code> type. Context is a dependency-injection tool: it's an object you create and pass to <code>Runner.run()</code>, 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.</p>
<div class="language-python highlight"><pre><span></span><code><span id="__span-5-1"><span class="nd">@dataclass</span>
</span><span id="__span-5-2"><span class="k">class</span><span class="w"> </span><span class="nc">SecurityContext</span><span class="p">:</span>
</span><span id="__span-5-3"> <span class="n">target_system</span><span class="p">:</span> <span class="nb">str</span>
</span><span id="__span-5-4"> <span class="n">is_compromised</span><span class="p">:</span> <span class="nb">bool</span>
</span><span id="__span-5-5">
</span><span id="__span-5-6"> <span class="k">async</span> <span class="k">def</span><span class="w"> </span><span class="nf">get_exploits</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="n">Exploits</span><span class="p">]:</span>
</span><span id="__span-5-7"> <span class="k">return</span> <span class="o">...</span>
</span><span id="__span-5-8">
</span><span id="__span-5-9"><span class="n">agent</span> <span class="o">=</span> <span class="n">Agent</span><span class="p">[</span><span class="n">SecurityContext</span><span class="p">](</span>
</span><span id="__span-5-10"> <span class="o">...</span><span class="p">,</span>
</span><span id="__span-5-11"><span class="p">)</span>
</span></code></pre></div>
<h2 id="output-types">Output types</h2>
<p>By default, agents produce plain text (i.e. <code>str</code>) outputs. If you want the agent to produce a particular type of output, you can use the <code>output_type</code> parameter. A common choice is to use <a href="https://docs.pydantic.dev/">Pydantic</a> objects, but we support any type that can be wrapped in a Pydantic <a href="https://docs.pydantic.dev/latest/api/type_adapter/">TypeAdapter</a> - dataclasses, lists, TypedDict, etc.</p>
<div class="language-python highlight"><pre><span></span><code><span id="__span-6-1"><span class="kn">from</span><span class="w"> </span><span class="nn">pydantic</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseModel</span>
</span><span id="__span-6-2"><span class="kn">from</span><span class="w"> </span><span class="nn">cai.sdk.agents</span><span class="w"> </span><span class="kn">import</span> <span class="n">Agent</span>
</span><span id="__span-6-3">
</span><span id="__span-6-4"><span class="k">class</span><span class="w"> </span><span class="nc">SecurityVulnerability</span><span class="p">(</span><span class="n">BaseModel</span><span class="p">):</span>
</span><span id="__span-6-5"> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span>
</span><span id="__span-6-6"> <span class="n">severity</span><span class="p">:</span> <span class="nb">str</span>
</span><span id="__span-6-7"> <span class="n">affected_files</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
</span><span id="__span-6-8"> <span class="n">description</span><span class="p">:</span> <span class="nb">str</span>
</span><span id="__span-6-9">
</span><span id="__span-6-10"><span class="n">agent</span> <span class="o">=</span> <span class="n">Agent</span><span class="p">(</span>
</span><span id="__span-6-11"> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;Vulnerability scanner&quot;</span><span class="p">,</span>
</span><span id="__span-6-12"> <span class="n">instructions</span><span class="o">=</span><span class="s2">&quot;Analyze system output and identify security vulnerabilities&quot;</span><span class="p">,</span>
</span><span id="__span-6-13"> <span class="n">output_type</span><span class="o">=</span><span class="n">SecurityVulnerability</span><span class="p">,</span>
</span><span id="__span-6-14"><span class="p">)</span>
</span></code></pre></div>
<div class="admonition note">
<p class="admonition-title">Note</p>
</div>
<p>When you pass an <code>output_type</code>, that tells the model to use structured outputs instead of regular plain text responses.</p>
<h2 id="handoffs">Handoffs</h2>
<p>Handoffs are sub-agents that the agent can delegate to. You provide a list of handoffs, and the agent can choose to delegate to them if relevant. This is a powerful pattern that allows orchestrating modular, specialized agents that excel at a single task. Read more in the <a href="../handoffs/">handoffs</a> documentation.</p>
<div class="language-python highlight"><pre><span></span><code><span id="__span-7-1"><span class="kn">from</span><span class="w"> </span><span class="nn">cai.sdk.agents</span><span class="w"> </span><span class="kn">import</span> <span class="n">Agent</span>
</span><span id="__span-7-2">
</span><span id="__span-7-3"><span class="n">crypto_agent</span> <span class="o">=</span> <span class="n">Agent</span><span class="p">(</span>
</span><span id="__span-7-4"> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;Cryptography agent&quot;</span><span class="p">,</span>
</span><span id="__span-7-5"> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Agent specialized in solving cryptographic challenges and decoding encrypted messages&quot;</span><span class="p">,</span>
</span><span id="__span-7-6"> <span class="n">instructions</span><span class="o">=</span><span class="s2">&quot;Analyze encrypted data and apply cryptographic techniques to decode it.&quot;</span><span class="p">,</span>
</span><span id="__span-7-7"> <span class="n">tools</span><span class="o">=</span><span class="p">[</span>
</span><span id="__span-7-8"> <span class="n">execute_cli_command</span><span class="p">,</span>
</span><span id="__span-7-9"> <span class="p">],</span>
</span><span id="__span-7-10"> <span class="n">handoff_description</span><span class="o">=</span><span class="s2">&quot;Specialized agent in Cryptography and code breaking&quot;</span><span class="p">,</span>
</span><span id="__span-7-11"> <span class="n">model</span><span class="o">=</span><span class="n">OpenAIChatCompletionsModel</span><span class="p">(</span>
</span><span id="__span-7-12"> <span class="n">model</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">&#39;CAI_MODEL&#39;</span><span class="p">,</span> <span class="s2">&quot;qwen2.5:14b&quot;</span><span class="p">),</span>
</span><span id="__span-7-13"> <span class="n">openai_client</span><span class="o">=</span><span class="n">AsyncOpenAI</span><span class="p">(),</span>
</span><span id="__span-7-14"> <span class="p">)</span>
</span><span id="__span-7-15"><span class="p">)</span>
</span><span id="__span-7-16"><span class="n">network_agent</span> <span class="o">=</span> <span class="n">Agent</span><span class="p">(</span>
</span><span id="__span-7-17"> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;Network Agent&quot;</span><span class="p">,</span>
</span><span id="__span-7-18"> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Agent specialized in network analysis, packet inspection, and network security assessments&quot;</span><span class="p">,</span>
</span><span id="__span-7-19"> <span class="n">instructions</span><span class="o">=</span><span class="s2">&quot;Analyze network traffic, identify suspicious patterns, and help with network-related CTF challenges&quot;</span><span class="p">,</span>
</span><span id="__span-7-20"> <span class="n">handoff_description</span><span class="o">=</span><span class="s2">&quot;Specialized agent in network security, traffic analysis, and protocol understanding&quot;</span><span class="p">,</span>
</span><span id="__span-7-21"> <span class="n">model</span><span class="o">=</span><span class="n">OpenAIChatCompletionsModel</span><span class="p">(</span>
</span><span id="__span-7-22"> <span class="n">model</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">&#39;CAI_MODEL&#39;</span><span class="p">,</span> <span class="s2">&quot;qwen2.5:72b&quot;</span><span class="p">),</span>
</span><span id="__span-7-23"> <span class="n">openai_client</span><span class="o">=</span><span class="n">AsyncOpenAI</span><span class="p">(),</span>
</span><span id="__span-7-24"> <span class="p">)</span>
</span><span id="__span-7-25"><span class="p">)</span>
</span><span id="__span-7-26"><span class="n">lead_agent</span> <span class="o">=</span> <span class="n">Agent</span><span class="p">(</span>
</span><span id="__span-7-27"> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;Cybersecurity Lead Agent&quot;</span><span class="p">,</span>
</span><span id="__span-7-28"> <span class="n">instructions</span><span class="o">=</span><span class="p">(</span>
</span><span id="__span-7-29"> <span class="s2">&quot;You are a lead cybersecurity expert coordinating security operations.&quot;</span>
</span><span id="__span-7-30"> <span class="s2">&quot;If the user needs network analysis or traffic inspection, handoff to the network agent.&quot;</span>
</span><span id="__span-7-31"> <span class="s2">&quot;If the user needs cryptographic solutions or code breaking, handoff to the crypto agent.&quot;</span>
</span><span id="__span-7-32"> <span class="p">),</span>
</span><span id="__span-7-33"> <span class="n">handoffs</span><span class="o">=</span><span class="p">[</span><span class="n">network_agent</span><span class="p">,</span> <span class="n">crypto_agent</span><span class="p">],</span>
</span><span id="__span-7-34"> <span class="n">model</span><span class="o">=</span><span class="s2">&quot;qwen2.5:72b&quot;</span>
</span><span id="__span-7-35"><span class="p">)</span>
</span></code></pre></div>
<h2 id="dynamic-instructions">Dynamic instructions</h2>
<p>In most cases, you can provide instructions when you create the agent. However, you can also provide dynamic instructions via a function. The function will receive the agent and context, and must return the prompt. Both regular and <code>async</code> functions are accepted.</p>
<div class="language-python highlight"><pre><span></span><code><span id="__span-8-1"><span class="k">def</span><span class="w"> </span><span class="nf">dynamic_instructions</span><span class="p">(</span>
</span><span id="__span-8-2"> <span class="n">context</span><span class="p">:</span> <span class="n">RunContextWrapper</span><span class="p">[</span><span class="n">UserContext</span><span class="p">],</span> <span class="n">agent</span><span class="p">:</span> <span class="n">Agent</span><span class="p">[</span><span class="n">UserContext</span><span class="p">]</span>
</span><span id="__span-8-3"><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
</span><span id="__span-8-4"> <span class="n">security_level</span> <span class="o">=</span> <span class="s2">&quot;high&quot;</span> <span class="k">if</span> <span class="n">context</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">is_admin</span> <span class="k">else</span> <span class="s2">&quot;standard&quot;</span>
</span><span id="__span-8-5"> <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;You are assisting </span><span class="si">{</span><span class="n">context</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2"> with cybersecurity operations. Their security clearance level is </span><span class="si">{</span><span class="n">security_level</span><span class="si">}</span><span class="s2">. Tailor your security recommendations appropriately and prioritize addressing their immediate security concerns.&quot;</span>
</span><span id="__span-8-6">
</span><span id="__span-8-7">
</span><span id="__span-8-8"><span class="n">agent</span> <span class="o">=</span> <span class="n">Agent</span><span class="p">[</span><span class="n">UserContext</span><span class="p">](</span>
</span><span id="__span-8-9"> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;Cybersecurity Triage Agent&quot;</span><span class="p">,</span>
</span><span id="__span-8-10"> <span class="n">instructions</span><span class="o">=</span><span class="n">dynamic_instructions</span><span class="p">,</span>
</span><span id="__span-8-11"><span class="p">)</span>
</span></code></pre></div>
<h3 id="launch">Launch</h3>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-9-1">cai
</span></code></pre></div>
<h3 id="performance-optimization">Performance Optimization</h3>
<p><strong>1. Use streaming for better responsiveness:</strong>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-10-1"><span class="nv">CAI_STREAM</span><span class="o">=</span><span class="nb">true</span><span class="w"> </span>cai
</span></code></pre></div>
<strong>2. Enable tracing for debugging:</strong>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-11-1"><span class="nv">CAI_TRACING</span><span class="o">=</span><span class="nb">true</span><span class="w"> </span>cai
</span></code></pre></div></p>
<hr />
<h2 id="agent-best-practices">Agent Best Practices</h2>
<h3 id="1-start-with-the-right-agent">1. Start with the Right Agent</h3>
<p>Don't use a specialized agent for general tasks. Match the agent to your objective:</p>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-12-1"><span class="c1"># ✅ Good: Using bug bounty agent for web testing</span>
</span><span id="__span-12-2"><span class="nv">CAI_AGENT_TYPE</span><span class="o">=</span>bug_bounter_agent<span class="w"> </span>cai
</span><span id="__span-12-3">CAI&gt;<span class="w"> </span>Test<span class="w"> </span>https://target.com<span class="w"> </span><span class="k">for</span><span class="w"> </span>vulnerabilities
</span><span id="__span-12-4">
</span><span id="__span-12-5"><span class="c1"># ❌ Bad: Using reverse engineering agent for web testing</span>
</span><span id="__span-12-6"><span class="nv">CAI_AGENT_TYPE</span><span class="o">=</span>reverse_engineering_agent<span class="w"> </span>cai
</span><span id="__span-12-7">CAI&gt;<span class="w"> </span>Test<span class="w"> </span>https://target.com<span class="w"> </span><span class="k">for</span><span class="w"> </span>vulnerabilities
</span></code></pre></div>
<h3 id="2-switch-agents-as-needed">2. Switch Agents as Needed</h3>
<p>Don't hesitate to switch agents mid-session:</p>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-13-1">CAI&gt;/agent<span class="w"> </span>bug_bounter_agent
</span><span id="__span-13-2">CAI&gt;<span class="w"> </span>Find<span class="w"> </span>vulnerabilities<span class="w"> </span><span class="k">in</span><span class="w"> </span>the<span class="w"> </span>web<span class="w"> </span>app
</span><span id="__span-13-3"><span class="c1"># ... agent finds SQL injection ...</span>
</span><span id="__span-13-4">
</span><span id="__span-13-5">CAI&gt;/agent<span class="w"> </span>redteam_agent
</span><span id="__span-13-6">CAI&gt;<span class="w"> </span>Exploit<span class="w"> </span>the<span class="w"> </span>SQL<span class="w"> </span>injection<span class="w"> </span>to<span class="w"> </span>gain<span class="w"> </span>access
</span><span id="__span-13-7"><span class="c1"># ... successful exploitation ...</span>
</span><span id="__span-13-8">
</span><span id="__span-13-9">CAI&gt;/agent<span class="w"> </span>dfir_agent
</span><span id="__span-13-10">CAI&gt;<span class="w"> </span>Analyze<span class="w"> </span>what<span class="w"> </span>data<span class="w"> </span>was<span class="w"> </span>exposed<span class="w"> </span>during<span class="w"> </span>the<span class="w"> </span><span class="nb">test</span>
</span></code></pre></div>
<h3 id="3-monitor-resource-usage">3. Monitor Resource Usage</h3>
<p>Keep an eye on costs and performance:</p>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-14-1"><span class="c1"># During session, check costs</span>
</span><span id="__span-14-2">CAI&gt;/cost
</span><span id="__span-14-3">
</span><span id="__span-14-4"><span class="c1"># Set limits before starting</span>
</span><span id="__span-14-5"><span class="nv">CAI_PRICE_LIMIT</span><span class="o">=</span><span class="s2">&quot;5.00&quot;</span><span class="w"> </span><span class="nv">CAI_MAX_TURNS</span><span class="o">=</span><span class="m">50</span><span class="w"> </span>cai
</span></code></pre></div>
<h3 id="4-save-successful-sessions">4. Save Successful Sessions</h3>
<p>Use <code>/load</code> to reuse successful approaches:</p>
<div class="language-bash highlight"><pre><span></span><code><span id="__span-15-1"><span class="c1"># In future session</span>
</span><span id="__span-15-2">CAI&gt;/load<span class="w"> </span>logs/logname.jsonl
</span></code></pre></div>
<hr />
<h2 id="next-steps">Next Steps</h2>
<ul>
<li><strong>Running Agents</strong>: See <a href="../running_agents/">running_agents documentation</a> for execution details</li>
<li><strong>Understanding Results</strong>: See <a href="../results/">results documentation</a> for output interpretation</li>
<li><strong>Agent Tools</strong>: See <a href="../tools/">tools documentation</a> for available tools</li>
<li><strong>Handoffs</strong>: See <a href="../handoffs/">handoffs documentation</a> for agent coordination</li>
<li><strong>MCP Integration</strong>: See <a href="../mcp/">mcp documentation</a> for connecting external tools</li>
<li><strong>Multi-Agent Patterns</strong>: See <a href="../multi_agent/">multi_agent documentation</a> for orchestration patterns</li>
</ul>
</article>
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