Honcho Integration Guide

What is Honcho

Honcho is an open source memory library for building stateful agents. It works with any model, framework, or architecture. You send Honcho the messages from your conversations, and custom reasoning models process them in the background — extracting premises, drawing conclusions, and building rich representations of each participant over time. Your agent can then query those representations on-demand ("What does this user care about?", "How technical is this person?") and get grounded, reasoned answers.

The key mental model: Peers are any participant — human or AI. Both are represented the same way. observe_me is a peer-level flag (PeerConfig) controlling whether Honcho forms a representation of that peer; typically you want Honcho to model your users (observe_me=True) but not anything with deterministic behavior (observe_me=False). observe_others is a separate per-peer SessionPeerConfig setting that controls whether that peer forms representations of the other participants in a session. Sessions scope conversations between peers. Messages are the raw data you feed in — Honcho reasons about them asynchronously and stores the results as the peer's representation. No messages means no reasoning means no memory.

Your agent accesses this memory through peer.chat(query) (ask a natural language question, get a reasoned answer — a few seconds of live reasoning) or session.context() (near-instant read of formatted history + representation). Prefer context() for per-turn grounding; use chat() when you need a reasoned answer.

Reference map

Follow the workflow below. Read a reference file only when you reach the step that needs it: