honcho

---

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, run a local stack with honcho start, 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
• CLI
• 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. The honcho-cli package lives here too.

Start Here

I want to...	Path	Get started
Give my coding agent persistent memory	Claude Code, OpenCode, Kilo Code, OpenClaw, Hermes, or any MCP client	Integrations
Add memory to my product	Python or TypeScript SDK	Quickstart
Run Honcho locally	Install CLI, then honcho start --setup	CLI
Inspect a deployment	honcho workspace inspect, honcho doctor	CLI
Self-host from source	Docker Compose or 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, honcho start locally, or run the FastAPI server yourself.
Agent-tool integrations	MCP, Claude Code, OpenCode, Kilo Code, 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 to join an organization, which gets its own dedicated Honcho instance. Or install the CLI and run honcho start --setup, then point the SDK at http://localhost:8000.

Python

pip install honcho-ai
# or: uv add honcho-ai
# or: poetry add honcho-ai

import os
from honcho import Honcho

# Managed service uses api.honcho.dev by default. For self-hosted, pass
# base_url="http://localhost:8000" or set HONCHO_URL.
honcho = Honcho(
    workspace_id="my-app-testing",
    api_key=os.environ["HONCHO_API_KEY"],
)

# 1. Store: peers and messages on a session
alice = honcho.peer("alice")
tutor = honcho.peer("tutor")
session = honcho.session("session-1")
session.add_messages([
    alice.message("Hey there — can you help me with my math homework?"),
    tutor.message("Absolutely. Send me your first problem!"),
])

# 2. Reason: happens asynchronously in the background.

# 3. Query: ask Honcho what it knows, or pull prompt-ready context.
answer = alice.chat("What learning styles does the user respond to best?")
context = session.context(summary=True, tokens=10_000)

# 4. Inject: hand the context to your model of choice.
from openai import OpenAI
client = OpenAI()
completion = client.chat.completions.create(
    model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
    messages=context.to_openai(assistant=tutor),
)

TypeScript

npm install @honcho-ai/sdk
# or: bun add @honcho-ai/sdk

import { Honcho } from "@honcho-ai/sdk";
import OpenAI from "openai";

const honcho = new Honcho({
  workspaceId: "my-app-testing",
  apiKey: process.env.HONCHO_API_KEY,
});

const alice = await honcho.peer("alice");
const tutor = await honcho.peer("tutor");
const session = await honcho.session("session-1");
await session.addMessages([
  alice.message("Hey there — can you help me with my math homework?"),
  tutor.message("Absolutely. Send me your first problem!"),
]);

const answer = await alice.chat(
  "What learning styles does the user respond to best?",
);
const context = await session.context({ summary: true, tokens: 10_000 });

const openai = new OpenAI();
const completion = await openai.chat.completions.create({
  model: process.env.OPENAI_MODEL ?? "gpt-4o-mini",
  messages: context.toOpenAI({ assistant: tutor }),
});

Note: background reasoning is asynchronous. Newly-added messages may take a moment to be reflected in chat/representation responses; for low-latency reads, use the representation endpoint.

What Honcho Gives You

Need	API
Save interaction history	session.add_messages(...)
Ask what Honcho knows about a peer	peer.chat(...)
Ask across the whole workspace	honcho.chat(...) / honcho.chat_stream(...)
Get prompt-ready context	session.context(...).to_openai(...) / .to_anthropic(...)
Hybrid search (BM25 + vector)	peer.search(...), session.search(...), honcho.search(...)
Low-latency static representations	peer.representation(...), session.representation(...)
Import documents	session.upload_file(...)
Inspect background processing	honcho.queue_status(...)

See the full SDK Reference and API Reference.

Integrations

Honcho ships a first-party memory plugin for every major coding agent. They all read the same ~/.honcho/config.json, so one key configures all of them — and pointing two at the same workspace gives them one shared memory.

Agent	Install	Source
Claude Code	/plugin marketplace add plastic-labs/claude-honcho	claude-honcho
Codex	npm install -g @honcho-ai/codex-honcho	codex-honcho
Cursor	curl -fsSL .../cursor-honcho/main/install.sh | bash	cursor-honcho
DeepSeek Harness	dsh plugin --profile <name> add @honcho-ai/dsh-honcho	dsh-honcho
OpenCode	opencode plugin "@honcho-ai/opencode-honcho" --global	opencode-honcho
Kilo Code	kilo plugin @honcho-ai/kilo-honcho --global	kilo-honcho
OpenClaw	openclaw plugins install @honcho-ai/openclaw-honcho	openclaw-honcho
Hermes	hermes memory setup	built in upstream
Any MCP client	claude mcp add honcho --transport http ...	MCP guide

Get a key at app.honcho.dev, then honcho init (or uv tool install honcho-cli && honcho init) writes it to ~/.honcho/config.json once for every integration.

Claude Code

Two ways, depending on how deep you want to go:

Plugin (richer integration — recommended for Claude Code users):

/plugin marketplace add plastic-labs/claude-honcho
/plugin install honcho@honcho

Raw MCP (works in any MCP client — Cursor, Cline, Windsurf, etc.):

claude mcp add honcho \
  --transport http \
  --url "https://mcp.honcho.dev" \
  --header "Authorization: Bearer hch-your-key-here" \
  --header "X-Honcho-User-Name: YourName"

Details: Claude Code guide · MCP guide · repo.

Codex

npm install -g @honcho-ai/codex-honcho
codex-honcho install      # registers hooks + MCP + skill in ~/.codex

Restart Codex to load the hooks. Details: Codex guide · repo.

Cursor

curl -fsSL https://raw.githubusercontent.com/plastic-labs/cursor-honcho/main/install.sh | bash

Windows (PowerShell): irm https://raw.githubusercontent.com/plastic-labs/cursor-honcho/main/install.ps1 | iex. The installer wires global hooks and MCP config. Details: cursor-honcho.

DeepSeek Harness

dsh plugin --profile <name> add @honcho-ai/dsh-honcho

A native Cordis plugin. It injects memory into the system prompt and captures new information from the session event feed. The model gets three tools — honcho_search, honcho_chat, and honcho_remember — and you can run /honcho to check status. Details: DeepSeek Harness guide · repo.

OpenCode

opencode plugin "@honcho-ai/opencode-honcho" --global

Details: OpenCode guide · repo.

Kilo Code

kilo plugin @honcho-ai/kilo-honcho --global

One install covers the Kilo CLI, the VS Code extension, and the JetBrains plugin, since all three run the same Kilo server. Details: Kilo Code guide · source.

OpenClaw

openclaw plugins install @honcho-ai/openclaw-honcho
openclaw honcho setup
openclaw gateway --force

openclaw honcho setup prompts for your API key, writes the config, and optionally migrates legacy MEMORY.md / USER.md / IDENTITY.md files into Honcho (non-destructive — originals are never deleted). Details: OpenClaw guide · repo.

Hermes

hermes memory setup   # select "honcho", point at api.honcho.dev or your local server

Details: Hermes guide.

Add Honcho to your own codebase (agent skill)

For wiring the Honcho SDK into an existing application, install the integration skill — it explores your codebase, asks about integration preferences, generates the SDK setup, and verifies it works:

npx skills add plastic-labs/honcho

Then invoke /honcho-integration in Claude Code (or /honcho-dev:integrate via the plugin marketplace). The same command also installs the memory skills — honcho-memory (concepts: the recall/record loop, session and peer strategy, plus how to connect and drive an MCP-connected Honcho) and honcho-cli (inspecting a deployment, or running a local stack with honcho start). Details: agentic development guide.

Other MCP clients

The same claude mcp add form (or its client-specific equivalent) works in any MCP-compatible client. See MCP guide.

CLI

honcho-cli inspects a Honcho deployment from the terminal, or runs a personal local stack with Docker.

uv tool install honcho-cli
honcho init                 # Honcho API key or browser login + server URL
honcho start --setup basic  # local stack: LLM provider key + Docker
honcho doctor

honcho init authenticates the CLI against a Honcho server. honcho start --setup is a separate step: it writes the LLM provider key the local deriver needs and starts API + deriver + Postgres + Redis.

Full commands and local-stack details: CLI reference · honcho-cli/README.md. To develop the server from source, see Self-hosting.

Core Concepts

Honcho organises everything around peers — humans and AI agents alike are first-class entities. The peer model enables:

• Multi-participant sessions with mixed human and AI agents
• Configurable observation settings (which peers observe which others)
• Flexible identity management for all participants
• Support for complex multi-agent interactions

Peers exchange messages within sessions; Honcho reasons over those messages to build a representation of each peer that you can query.

• Workspace (formerly App): top-level container; isolates data between use cases.
• Peer (formerly User): any participant — human user or AI agent.
• Session: a conversation context; many-to-many with peers.
• Scope: a named grouping of sessions that bounds recall (chat, representation, search) to those members.
• Message: an atomic data unit (peer-to-peer communication or ingested document chunk).

What you query out of Honcho:

• Conclusions — what Honcho has extracted about a peer (deductive and inductive). Exposed via the conclusions API.
• Representations — static, low-latency snapshots of what Honcho knows about a peer (optionally session-scoped).
• Peer Cards — compact identity summaries.
• Session context / summaries — prompt-ready bundles for long-running conversations.

Internally, Honcho stores peer-related observations in collections of vector-embedded documents. Collections are keyed by (observer, observed) peer pairs — the same mechanism powers self-representation (observer == observed) and cross-peer modelling (peer X's understanding of peer Y). These primitives are not exposed directly; the Conclusions API is the public surface.

Benchmarks & Evals

Honcho's evals span LongMemEval, LoCoMo, and other long-conversation benchmarks. See the evals page, the research blog post, and the Pareto-frontier announcement video for methodology and reproducible results.

Self-hosting

Honcho is open source under AGPL-3.0. To run a personal instance, install the CLI (uv tool install honcho-cli) and then honcho start --setup. The paths below are for building from source, contributing, or deploying without the CLI.

Quick start (from source, Docker)

git clone https://github.com/plastic-labs/honcho.git
cd honcho
cp docker-compose.yml.example docker-compose.yml
cp .env.template .env       # fill in LLM_GEMINI_API_KEY / LLM_ANTHROPIC_API_KEY / LLM_OPENAI_API_KEY
docker compose up

Then point the SDKs at it:

honcho = Honcho(workspace_id="my-app-testing", base_url="http://localhost:8000")
# or: export HONCHO_URL=http://localhost:8000

Below is a guide on setting up a local environment for running the Honcho Server without Docker.

Prerequisites and Dependencies

Honcho is developed using python and uv.

The minimum python version is 3.10 The minimum uv version is 0.5.0

Setup

Once the dependencies are installed on the system run the following steps to get the local project setup.

1. Clone the repository

git clone https://github.com/plastic-labs/honcho.git

2. Enter the repository and install the python dependencies

We recommend using a virtual environment to isolate the dependencies for Honcho from other projects on the same system. uv will create a virtual environment when you sync your dependencies in the project.

cd honcho
uv sync

This will create a virtual environment and install the dependencies for Honcho. The default virtual environment will be located at honcho/.venv. Activate the virtual environment via:

source honcho/.venv/bin/activate

3. Set up a database

Honcho utilizes Postgres for its database with pgvector. An easy way to get started with a postgres database is to create a project with Supabase

Alternatively, a docker-compose template is available with a sample database configuration. To use Docker:

cp docker-compose.yml.example docker-compose.yml
docker compose up -d database

4. Edit the environment variables

Honcho uses a .env file for managing runtime environment variables. A .env.template file is included for convenience. Several of the configurations are not required and are only necessary for additional logging, monitoring, and security.

Below are the required configurations:

DB_CONNECTION_URI= # Connection uri for a postgres database (with postgresql+psycopg prefix)

# LLM Provider API Keys
LLM_GEMINI_API_KEY= # API Key for Google Gemini (used for deriver, summary, and dialectic minimal/low by default)
LLM_ANTHROPIC_API_KEY= # API Key for Anthropic (used for dialectic medium/high/max and dream by default)
LLM_OPENAI_API_KEY= # API Key for OpenAI (used for embeddings when EMBED_MESSAGES=true)

Note that the DB_CONNECTION_URI must have the prefix postgresql+psycopg to function properly. This is a requirement brought by sqlalchemy

The template has the additional functionality disabled by default. To ensure that they are disabled you can verify the following environment variables are set to false:

AUTH_USE_AUTH=false
SENTRY_ENABLED=false

If you set AUTH_USE_AUTH to true you will need to generate a JWT secret. You can do this with the following command:

python scripts/generate_jwt_secret.py

This will generate a JWT secret and print it to the console. You can then set the AUTH_JWT_SECRET environment variable. This is required for AUTH_USE_AUTH:

AUTH_JWT_SECRET=<generated_secret>

Once auth is enabled, use scripts/generate_jwt.py to mint tokens for local development and scripting:

# Admin token (full access, no expiry)
uv run python scripts/generate_jwt.py --admin

# Admin token expiring in 24 hours
uv run python scripts/generate_jwt.py --admin --expires 24h

# Workspace-scoped token
uv run python scripts/generate_jwt.py --workspace my-workspace --expires 30d

# Capture a token for use in curl/scripts
TOKEN=$(uv run python scripts/generate_jwt.py --admin --print-only)
curl -H "Authorization: Bearer $TOKEN" http://localhost:8000/v3/workspaces

Duration units: s (seconds), m (minutes), h (hours), d (days), w (weeks), y (years).

5. Run database migrations

With the database set up and environment variables configured, run the migrations to create the necessary tables:

uv run alembic upgrade head

This will create all tables for Honcho including workspaces, peers, sessions, messages, and the queue system.

Migrations hold a session-level advisory lock, so they need a session-mode connection (direct Postgres or a session pooler), not a transaction-mode pooler. A Supabase-style :6543 port is rewritten to :5432 automatically.

6. Launch Honcho

With everything set up, you can now launch a local instance of Honcho. In addition to the database, two components need to be running:

Start the API server:

uv run fastapi dev src/main.py

This is a development server that will reload whenever code is changed.

Start a background worker (deriver):

In a separate terminal, run:

uv run python -m src.deriver

The deriver generates representations, summaries, peer cards, and manages dreaming tasks. You can increase the number of derivers to improve runtime efficiency.

Contributors: see CONTRIBUTING.md for pre-commit setup. Deploying to Fly.io: see Self-hosting docs → Deploying on Fly.io.

Configuration