honcho

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Honcho is memory infrastructure for building stateful agents that understand changing people, agents, groups, projects, and ideas over time.

Store messages and events, let Honcho reason in the background, then query peer representations, session context, search results, or natural-language insights from any model or framework. Use it managed at api.honcho.dev or self-host the FastAPI server yourself.

Using Honcho as your memory system will earn your agents higher retention, more trust, and help you build data moats to out-compete incumbents.

Honcho has defined the Pareto Frontier of Agent Memory. Watch the video, check out our evals page, and read the blog post for more detail.

Contents

• Start Here
• Why Honcho
• The Honcho Loop
• Quickstart
• What Honcho Gives You
• Integrations
• Core Concepts
• Benchmarks & Evals
• Self-hosting
• Configuration
• Architecture
• SDKs
• Learn More
• Contributing
• License

The Honcho project is split between several repositories, with this one hosting the core service logic — implemented as a FastAPI server. Client SDKs for Python and TypeScript live in the sdks/ directory.

Start Here

I want to...	Path	Get started
Give my coding agent persistent memory	Claude Code, OpenCode, OpenClaw, Hermes, or any MCP client	Integrations
Add memory to my product	Python or TypeScript SDK	Quickstart
Self-host Honcho	Docker / local development	Self-hosting

Why Honcho

Capability	What it means
Reasoning-first memory	Extracts conclusions from conversations and events, not just matching chunks.
Peer-centric model	Tracks users, agents, groups, projects, and ideas as entities that change over time.
Multi-peer perspective	Models what one peer knows about another when configured.
Managed or self-hosted	Use api.honcho.dev or run the FastAPI server yourself.
Agent-tool integrations	MCP, Claude Code, OpenCode, OpenClaw, Hermes, Cursor-compatible clients.

The Honcho Loop

1. Store conversations, events, documents, or tool traces as messages on a session.
2. Reason — Honcho processes the queue in the background and updates peer representations.
3. Query — ask Honcho for context, search results, peer representations, or a natural-language answer.
4. Inject — drop the result into any LLM call or agent framework.

Concretely: workspaces hold peers, peers participate in sessions, messages live on sessions, and Honcho builds a per-peer representation that you query through the Chat Endpoint or directly.

Quickstart

Get an API key at app.honcho.dev — when you sign up you'll be prompted