Integrates Honcho’s open-source memory and social cognition layer into Python or TypeScript AI agent codebases.
This skill automates the complex process of adding long-term, reasoned memory to AI agents by integrating the Honcho SDK. It performs a deep dive into your existing codebase to identify LLM patterns and conversation structures, then guides you through a requirement gathering phase to define how peers and sessions should behave. By the end of the workflow, it generates production-ready code for peer configuration, observation settings, and session management, enabling your agents to understand user preferences, expertise, and goals through grounded, reasoned context rather than simple vector retrieval.
주요 기능
01Interactive configuration for Peer entities and social cognition settings
02Implementation of 'Dialectic Chat' tool-calling patterns for agents
03Native support for bot frameworks including nanobot, openclaw, and picoclaw
049 GitHub stars
05Automated codebase exploration for existing LLM and session patterns
06Support for both Python (sync/async via uv) and TypeScript (bun/npm) environments
사용 사례
01Implementing social cognition in multi-agent systems to track different participant roles
02Transforming stateless chatbots into stateful agents that remember user preferences
03Adding long-term memory to FastAPI or Express-based AI services with minimal manual coding
