dce96889bc
* chore: 3.0 honcho and 2.0 sdks changelog fix: use PeerContextResponse in peer.ts * chore: move docs to /v3/, build SDKs * chore: code review * feat: [WIP] migrate away from stainless in typescript sdk * chore: move api from /v2/ to /v3/ * feat: no-stainless typescript with real tests * feat: migrate python sdk off of stainless * feat: clean typescript sdk * chore: add tests for ts http client * fix: rewrite entire python sdk in new format, update typescript sdk to use `configuration` not `config` for consistency with API * fix: clean up SDKs, synchronize * chore: update sdk examples * chore: update OpenAPI documentation and SDK examples to reflect changes * fix: better test * fix: install deps in test runner, improve robustness of streaming in sdk, coderabbit nits * fix: standardize around camelCase in TS SDK * refactor: update configuration handling in SDKs to use typed models for workspace, session, and peer configurations * docs: clarify queue status usage and remove polling methods from SDKs add claude skills for migrations * chore: fix links in docs * feat: add deriver flush mode to bypass batch token threshold - Introduced `is_deriver_flush_enabled` function to check if flush mode is active. - Updated `QueueManager` to conditionally apply batch token thresholds based on flush mode. - Enhanced `UnifiedTestExecutor` to enable flush mode via Redis. - Added `flush` parameter to test cases to facilitate testing of flush mode behavior. - Updated various test cases to utilize the new flush functionality. * feat: implement schedule_dream functionality in SDKs, use in unified test runner - Added `schedule_dream` method to both Python and TypeScript SDKs for scheduling dream tasks. - Updated HTTP routes to include endpoint for scheduling dreams. - Enhanced test runner to utilize the new `schedule_dream` method for scheduling actions. - Updated TypeScript client to support the new scheduling functionality with appropriate parameters. * feat: update single deriver task to support multiple observers - Changed the `observer` parameter to `observers` as a list in multiple functions across the deriver module. - Updated the processing logic to handle multiple observers for representation tasks. - Adjusted related payload and queue management functions to accommodate the new observers structure. - Modified tests to reflect changes in the representation task handling and ensure proper functionality. * refactor: update enqueue tests to support deduplication of queue items with multiple observers - Modified tests in `test_enqueue.py` to reflect changes in the queue item structure, where each message now results in a single queue item containing a list of observers. - Updated assertions to validate that the `observers` field correctly includes all relevant peers, ensuring proper functionality of the deduplication logic. - Removed redundant payload matching logic to streamline test cases and improve clarity. * fix: add backwards compatibility for representation work unit keys and payload observers * feat: update dialectic configuration and introduce cost calculator - Adjusted LLM and dialectic settings in `.env.template`, `config.toml.example`, and `src/config.py` to reduce maximum tool output characters and session history tokens for cost efficiency. - Implemented a new `dialectic_cost_calculator.py` script to estimate costs based on reasoning levels and model pricing. - Enhanced `DialecticAgent` to utilize minimal tools and adjusted output token settings based on reasoning level to optimize performance and reduce costs. * feat: add reasoning level to chat input in unified test runner - Enhanced the `UnifiedTestExecutor` to include a `reasoning_level` parameter in the chat method call. - Updated the `QueryAction` model to support the new `reasoning_level` attribute, allowing for more nuanced chat interactions. * feat: run deriver once for multiple observers (#335) * feat: update single deriver task to support multiple observers - Changed the `observer` parameter to `observers` as a list in multiple functions across the deriver module. - Updated the processing logic to handle multiple observers for representation tasks. - Adjusted related payload and queue management functions to accommodate the new observers structure. - Modified tests to reflect changes in the representation task handling and ensure proper functionality. * refactor: update enqueue tests to support deduplication of queue items with multiple observers - Modified tests in `test_enqueue.py` to reflect changes in the queue item structure, where each message now results in a single queue item containing a list of observers. - Updated assertions to validate that the `observers` field correctly includes all relevant peers, ensuring proper functionality of the deduplication logic. - Removed redundant payload matching logic to streamline test cases and improve clarity. * fix: add backwards compatibility for representation work unit keys and payload observers * feat: refactor benchmark runners to share common functionality - Introduced a new `runner_common.py` module containing shared utilities for benchmark test runners, including common argument parsing, client creation, and queue management. - Updated `BEAMRunner`, `LoCoMoRunner`, and `LongMemEvalRunner` to inherit from `RunnerMixin`, leveraging shared functionality for metrics collection and logging. - Added `reasoning_level` and `redis_url` parameters to runner constructors for enhanced configuration. - Streamlined argument parsing by utilizing `add_common_arguments` for shared command-line options across all runners. * fix: update last_user_message handling to use message content instead of ID * fix: standardize config vs configuration --------- Co-authored-by: Vineeth Voruganti <13438633+VVoruganti@users.noreply.github.com> |
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README.md
Honcho CrewAI Integration
Build CrewAI agents with persistent memory and reasoning capabilities powered by Honcho.
Installation
pip install honcho-crewai
Quick Start
from crewai import Agent, Task, Crew, Process
from crewai.memory.external.external_memory import ExternalMemory
from honcho_crewai import HonchoStorage
# Initialize Honcho storage
storage = HonchoStorage(user_id="user-123")
external_memory = ExternalMemory(storage=storage)
# Create agent with memory
agent = Agent(
role="AI Assistant",
goal="Help users with persistent memory",
backstory="You remember past conversations.",
)
# Create crew with external memory
crew = Crew(
agents=[agent],
tasks=[task],
external_memory=external_memory
)
Features
- Automatic Memory: CrewAI agents automatically store and retrieve conversation context
- Semantic Search: Find relevant past messages using vector similarity
- Logical Reasoning: Query what the system knows about users via the Dialectic API
- Multi-Agent Support: Give each agent distinct memory and identity
- Tools Integration:
HonchoGetContextTool,HonchoDialecticTool, andHonchoSearchToolfor explicit memory control
Documentation
For comprehensive guides, examples, and API reference, visit: https://docs.honcho.dev/v3/integrations/crewai
Examples
Check out complete examples in the GitHub repository.
License
AGPL-3.0-or-later
Support
- Report issues: GitHub Issues
- Documentation: docs.honcho.dev
- Website: honcho.dev