/honcho-integration

name: honcho-integration
description: Integrate Honcho memory into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, and accessing Honcho's representation.
allowed-tools: Read, Glob, Grep, Bash(uv:*), Bash(bun:*), Bash(npm:*), Edit, Write, WebFetch, AskUserQuestion

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:

| When you're… | Read | | --- | --- | | Writing the client/peer/session setup (init, peers, sessions, add messages) | `references/core-patterns.md` | | Wiring how the AI reads context (tool call, pre-fetch, `context()`, streaming) | `references/agent-patterns.md` | | Integrating into a bot framework (nanobot, openclaw, picoclaw, …) | `references/bot-frameworks.md` + `references/bot-frameworks/<framework>/` |

Integration Workflow

Follow these phases in order:

Phase 1: Codebase Exploration

Before asking the user anything, explore the codebase to understand:

1. **Language & Framework**: Is this Python or TypeScript? What frameworks are used (FastAPI, Express, Next.js, etc.)? 2. **Existing AI/LLM code**: Search for existing LLM integrations (OpenAI, Anthropic, LangChain, etc.) 3. **Entity structure**: Identify users, agents, bots, or other entities that interact 4. **Session/conversation handling**: How does the app currently manage conversations? 5. **Message flow**: Where are messages sent/received? What's the request/response cycle?

Use Glob and Grep to find:

• `**/*.py` or `**/*.ts` files with "openai", "anthropic", "llm", "chat", "message"
• User/session models or types
• API routes handling chat or conversation endpoints

> **Bot framework detected?** If the codebase is built around an agent loop, tool registry, session manager, and message bus (e.g., nanobot, openclaw, picoclaw), read `references/bot-frameworks.md` for framework-specific integration guidance and check `references/bot-frameworks/<framework>/` for concrete reference implementations.

Phase 2: Interview (REQUIRED)

After exploring the codebase, use the **AskUserQuestion** tool to clarify integration requirements. Ask these questions (adapt based on what you learned in Phase 1):

Question Set 1 - Entities & Peers

Ask about which entities should be Honcho peers:

• header: "Peers"
• question: "Which entities should Honcho track and build representations for?"
• options based on what you found (e.g., "End users only", "Users + AI assistant", "Users + multiple AI agents", "All participants including third-party services")
• Include a follow-up if they have multiple AI agents: should any AI peers be observed?

Question Set 2 - Integration Pattern

Ask how they want to use Honcho context (see `references/agent-patterns.md` for the implementation of each):

• header: "Pattern"
• question: "How should your AI access Honcho's user context?"
• options:
• "Tool call (Recommended)" - "Agent queries Honcho on-demand via function calling"
• "Pre-fetch" - "Fetch user context before each LLM call with predefined queries"
• "context()" - "Include conversation history and representations in prompt"
• "Multiple patterns" - "Combine approaches for different use cases"

Question Set 3 - Session Structure

Ask about conversation structure:

• header: "Sessions"
• question: "How should conversations map to Honcho sessions?"
• options based on their app (e.g., "One session per chat thread", "One session per user", "Multiple users per session (group chat)", "Custom session logic")

Question Set 4 - Specific Queries (if using pre-fetch pattern)

If they chose pre-fetch, ask what context matters:

• header: "Context"
• question: "What user context should be fetched for the AI?"
• multiSelect: true
• options: "Communication style", "Expertise level", "Goals/priorities", "Preferences", "Recent activity summary", "Custom queries"

Phase 3: Implementation

Based on interview responses, implement the integration:

1. Install the SDK (see [Installation](#installation)) 2. Create Honcho client initialization — `references/core-patterns.md` §1 3. Set up peer creation for identified entities — `references/core-patterns.md` §2–3 4. Implement the chosen integration pattern(s) — `references/agent-patterns.md` 5. Add message storage after exchanges — `references/core-patter