From c675cb669e7a56d92aacea65de646f657ad4ae94 Mon Sep 17 00:00:00 2001 From: ajspig Date: Wed, 15 Jul 2026 17:53:24 -0400 Subject: [PATCH] fix: language about observe_others --- skills/honcho-integration/SKILL.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skills/honcho-integration/SKILL.md b/skills/honcho-integration/SKILL.md index 40725d4a..6b6706a2 100644 --- a/skills/honcho-integration/SKILL.md +++ b/skills/honcho-integration/SKILL.md @@ -10,7 +10,7 @@ allowed-tools: Read, Glob, Grep, Bash(uv:*), Bash(bun:*), Bash(npm:*), Edit, Wri 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. Observation settings (`observe_me`, `observe_others`) control which peers Honcho reasons about. Typically you want Honcho to model your users (`observe_me=True`) but not anything with deterministic behavior (`observe_me=False`). **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. +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.