Provide persistent memory and context retrieval for LLM applications.
npx skills add https://github.com/zot24/skills --skill honcho-zot24
Please help me install this Agent Skill. Skill: honcho Source: https://github.com/zot24/skills/tree/main/skills/honcho/skills/honcho Command: npx skills add https://github.com/zot24/skills --skill honcho-zot24
Honcho provides a production-ready memory layer that captures and reasons about user and agent interactions so LLM-based systems can act statefully and consistently across sessions and channels.
Request: Get context for workspace my-app, session conv-1, peer_target user-123 to retrieve the peer representation and recent messages.
High-intent search queries and answers about installing and using this skill.
Persistent memory is added by integrating an SDK that captures user interactions to build peer representations, enabling stateful agents to recall context across sessions. Honcho provides this memory layer to isolate data per workspace while maintaining session history.
User modeling creates representations of user preferences and behaviors, while background memory consolidation—called dreaming—refines these peer cards and removes contradictions to keep long-term memory accurate without manual intervention.
Yes, Python and TypeScript SDKs are supported for integrating with multiple LLM providers. The system retrieves peer representations and recent messages to assemble token-budgeted context for injection into LLM prompts and tool use.
Session context is retrieved by specifying the workspace, session ID, and peer target. This returns the peer representation and recent messages, allowing stateful assistants to surface concise, personalized context for LLM prompts.
Token-budgeted context assembly is managed by retrieving automated summaries, deductive conclusions, and peer representations rather than raw histories. This extracts explicit premises to provide concise context that fits within LLM token limits.
Yes, asynchronous reasoning is supported for stateful agent workflows. The system extracts deductive and inductive conclusions from messages, generating summaries that enable agents to reason about user interactions without blocking main processes.
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