LibreAI / Agents IA / honcho
// agents · plastic-labs/honcho

honcho

honcho est un projet de agents IA open source, créé par plastic-labs (plastic-labs/honcho sur GitHub). Il compte 7 536 étoiles sur GitHub et 928 forks, il est publié sous la licence AGPL-3.0 et a été mis à jour pour la dernière fois sur GitHub le 8 octobre 2026.

Honcho — Plastic Labs' memory library for stateful agents that learn about their users over time.

★ 7,5 k étoiles⑂ 928 forksAGPL-3.0mis à jour oct. 2026
▦ visuel — le README de ce projet ne contient aucune capture intégrable,
voici donc un emplacement réservé. Le guide ci-dessous est complet malgré tout.
// détails du projet

Détails du projet

Étoiles GitHub
7 536
Forks
928
Licence
AGPL-3.0
Dernière mise à jour sur GitHub
8 octobre 2026
Catégorie
Agents IA
Type de fiche
Guide d'installation complet
Site officiel
Official site
// fonctionnement

Comment ça marche

  1. Clarify your goal

    Decide managed versus self-hosted, and model your world as peers and sessions — typically your users and your agent as peers exchanging messages.

  2. Get the project

    Clone plastic-labs/honcho from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.

  3. Install and configure

    Install the SDK for your language; for self-hosting, clone the repo and start it with Docker Compose, then point the SDK at your local base URL.

  4. Run your first task

    Store a few messages on a session, let background reasoning run, then query Honcho for what it knows about that peer and inject the context into a prompt.

// fonctionnalités

Pourquoi on l'adore

The Honcho loop

Store messages, let asynchronous background reasoning build understanding, query it, and inject prompt-ready context into any model.

Peers and sessions model

Memory is organised around peers (users, agents) and their sessions, rather than a flat chat log.

Managed or self-hosted

Use the managed api.honcho.dev service, or self-host from source with Docker Compose or a local dev setup.

Python and TypeScript SDKs

pip install honcho-ai, or npm install @honcho-ai/sdk, with equivalent workflows in both languages.

Agent-tool integrations

Documented integrations add Honcho memory to Claude Code, Codex and Cursor via dedicated packages and install scripts.

Built for stateful agents

Aims at continual, cross-session memory and context engineering rather than one-conversation chat history.

Advertisement
// lancez-le

Lancez-le vous-même

  1. Clone the repository

    Get the source from the official GitHub repository for honcho.

    git clone https://github.com/plastic-labs/honcho.git
    cd honcho
  2. Install the SDK

    Install the Python SDK as the README quickstart shows (TypeScript: npm install @honcho-ai/sdk).

    pip install honcho-ai
  3. Or self-host from source

    Clone the repo and start the full service locally with Docker Compose, then set HONCHO_URL or base_url to your instance.

    git clone https://github.com/plastic-labs/honcho.git
    cd honcho
    docker compose up  # see README self-hosting section
// cas d'usage

Ce que vous pouvez créer

Advertisement
// faq

Questions fréquentes

Who maintains honcho?

plastic-labs (Plastic Labs), through the plastic-labs/honcho repository, with docs at honcho.dev/docs.

Is honcho free to use?

Yes — the project is open source under the AGPL-3.0 license as recorded for this listing. See the license terms for any conditions on redistribution.

Does honcho need a paid API key?

The managed service uses api.honcho.dev (account/API key). Self-hosting is free under AGPL-3.0 and runs on your own infrastructure; reasoning uses models you configure there.

How does Honcho compare to storing chat history in my own database?

A database stores raw history; you still build recall, summarisation and context assembly. Honcho adds the reasoning layer — it processes messages in the background so queries return understanding and prompt-ready context, not just logs.

How do I get started with honcho?

Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — store a few messages on a session, let background reasoning run, then query Honcho for what it knows about that peer and inject the context into a prompt.

// continuez l'exploration

Outils similaires

Agents IA

hindsight

Agent memory that learns — so your agents get smarter over time.

★ 47,0 k étoilesMIT
Voir le guide →
Agents IA

letta

Stateful AI agents with memory that learns and improves.

★ 25,1 k étoilesApache-2.0
Voir le guide →
Agents IA

mex

Team memory for engineers and their AI agents.

★ 1,8 k étoilesMIT
Voir le guide →
Agents IA

langmem

LangMem: long-term memory primitives for LangGraph agents that learn and adapt from conversations.

★ 1,7 k étoilesMIT
Voir le guide →
Agents IA

graphiti

Real-time knowledge graphs that give agents a memory with structure.

★ 31,5 k étoilesApache-2.0
Voir le guide →
Agents IA

Agent_Memory_Techniques

30 runnable notebooks teaching everything about agent memory.

★ 1,1 k étoilesApache-2.0
Voir le guide →
// alternatives

Alternatives en Agents IA

Les autres projets de la catégorie Agents IA avec le plus d'étoiles du catalogue LibreAI — la sélection réaliste d'alternatives si honcho ne vous convient pas.

// plus en agents

Plus en Agents IA

Projets de la catégorie Agents IA proches dans le catalogue, les plus proches en étoiles de honcho — continuez à explorer de part et d'autre.

Obtenir honcho

Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.