honcho-memory
Using Honcho as Memory
Honcho is a memory layer for agents. You feed it the messages from your conversations; in the background it reasons over them and builds a representation of each participant. At any point you can ask it natural-language questions about the user ("How technical are they?", "What are they trying to do?") and get grounded, reasoned answers.
This skill is for when Honcho is already connected to you and you want to use it. If you're instead adding Honcho to a codebase from scratch, use the honcho-integration skill.
What's durable vs. what to look up. The concepts and the recall/record loop below change rarely — rely on them. Specifics that change often — the exact set of integrations, tool names, install commands, headers, and defaults — are illustrative here; treat the linked docs (and your own live tool list) as authoritative and fetch them when the details matter.
The mental model
- Peer — any participant, human or AI. You and the user are both peers. Honcho builds a representation of peers it observes (typically the user, not you).
- Session — one conversation thread; messages live in sessions. Honcho reasons over the messages in a session together, so scope each session to one coherent context (a conversation, channel, task, or project) and keep that context's turns in the same session rather than fragmenting them across many thin ones. For low-volume or trickle inputs, append to one ongoing session rather than spinning up a new one each time. See design patterns and reasoning.
- Message — the raw turns you feed in. No messages → no reasoning → no memory.
- Conclusion — a fact Honcho derived (or you stored) about a peer. Conclusions power the representation.
- Representation / peer card — the synthesized understanding of a peer, queryable via
chat. A peer's representation accumulates across every session it appears in — that's the cross-conversation memory. Session-scoped data (recent messages, summaries) stays local to one session.
Reasoning happens asynchronously. After you record a turn, don't poll or wait — the representation updates in the background and is richer next time you ask.