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, 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

Why Honcho

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 osfrom 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 sessionalice = 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 OpenAIclient = 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

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.

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. Cursor and Grok Bot are the exception: the marketplace plugin signs in with OAuth, and honcho init is optional there (in Cursor it turns on automatic capture).

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-honchocodex-honcho install      # registers hooks + MCP + skill in ~/.codex

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

Cursor and Grok Bot

One Cursor Marketplace listing serves both. In Cursor, open Customize, install Honcho, authenticate the honcho MCP server, then run /honcho-setup once. In Grok Bot, add Honcho under Connect apps (or Add to Grok Bot from the plugin page) and ask a Bot to set up Honcho. The plugin connects to the hosted MCP server over OAuth and ships the skills that drive the memory loop, so nothing runs locally. Details: Cursor guide · Grok Bot guide · source.

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-honchoopenclaw honcho setupopenclaw 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-clihoncho init                 # Honcho API key or browser login + server URLhoncho start --setup basic  # local stack: LLM provider key + Dockerhoncho 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.

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.gitcd honchocp docker-compose.yml.example docker-compose.ymlcp .env.template .env       # set LLM_OPENAI_API_KEY (see "LLM provider" below for OpenRouter etc.)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

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

# Postgres and pgvector majors must match the distro's packaged version.# Ubuntu 24.04:sudo apt install postgresql-16 postgresql-16-pgvector# Debian 12:sudo apt install postgresql-15 postgresql-15-pgvector# Other versions: add the PostgreSQL APT repo (apt.postgresql.org) first.
sudo -u postgres psql -c "CREATE ROLE honcho LOGIN PASSWORD 'honcho';"sudo -u postgres createdb -O honcho honchosudo -u postgres psql -d honcho -c "CREATE EXTENSION IF NOT EXISTS vector;"sudo -u postgres psql -d honcho -c "CREATE EXTENSION IF NOT EXISTS pg_trgm;"  # optional, speeds up message search

brew install postgresql@17 pgvectorbrew services start postgresql@17
psql -d postgres -c "CREATE ROLE honcho LOGIN PASSWORD 'honcho';"createdb -O honcho honchopsql -d honcho -c "CREATE EXTENSION IF NOT EXISTS vector;"psql -d honcho -c "CREATE EXTENSION IF NOT EXISTS pg_trgm;"

cp .env.template .env

DB_CONNECTION_URI=postgresql+psycopg://honcho:honcho@localhost:5432/honchoLLM_OPENAI_API_KEY=sk-...

LLM_OPENAI_API_KEY=sk-or-v1-...DERIVER_MODEL_CONFIG__MODEL=google/gemini-2.5-flashDERIVER_MODEL_CONFIG__OVERRIDES__BASE_URL=https://openrouter.ai/api/v1# ...repeat for DIALECTIC_LEVELS__*, SUMMARY_*, DREAM_*

python scripts/generate_jwt_secret.py

AUTH_JWT_SECRET=<generated_secret>

# Admin token (full access, no expiry)uv run python scripts/generate_jwt.py --admin
# Admin token expiring in 24 hoursuv run python scripts/generate_jwt.py --admin --expires 24h
# Workspace-scoped tokenuv run python scripts/generate_jwt.py --workspace my-workspace --expires 30d
# Capture a token for use in curl/scriptsTOKEN=$(uv run python scripts/generate_jwt.py --admin --print-only)curl -H "Authorization: Bearer $TOKEN" http://localhost:8000/v3/workspaces

uv run alembic upgrade head

uv run fastapi dev src/main.py                  # local development, reloads on code changesuv run fastapi run --host 0.0.0.0 src/main.py   # on a server

uv run python -m src.deriver

Contributors: install the git hooks with uv run pre-commit install --hook-type pre-commit --hook-type commit-msg --hook-type pre-push. Tests only run at push time, so a clean commit is not a clean push. Deploying to Fly.io: see Self-hosting docs → Deploying on Fly.io.

Configuration

Honcho uses a flexible configuration system that supports both TOML files and environment variables. Configuration values are loaded in priority order: environment variables > .env file > config.toml > defaults.

Copy the example file to get started:

cp config.toml.example config.toml

The file is organized by subsystem — [app], [db], [auth], [cache], [llm], [deriver], [dialectic], [summary], [dream], [peer_card], [webhook], [metrics], [telemetry], [vector_store], and [sentry]. Any value can be overridden by an environment variable named {SECTION}_{KEY}, using __ for nesting (DIALECTIC_LEVELS__low__MODEL_CONFIG__MODEL), or just {KEY} for app-level settings.

See the configuration reference for every available option, and .env.template for an annotated list of environment variables.

Architecture

Honcho splits into two services: Storage (workspaces, peers, sessions, scopes, messages, internal collections) and Insights (reasoning, conclusions, representations, summaries, the chat endpoint). Storage is synchronous via the API; Insights is asynchronous via a background queue consumed by the deriver worker process.

Key features:

• Rich Reasoning System — multiple implementation methods that extract conclusions from interactions and build comprehensive representations of peers
• Chat Endpoint — reasoning-informed responses that integrate conclusions with current context
• Background Processing — asynchronous processing pipeline for expensive operations like representation updates and session summarization
• Multi-Provider Support — configurable LLM providers for different use cases

Workspaces├── Peers ←──────────────────┐│   ├── Sessions             ││   └── (internal collections, keyed by observer/observed peer pair)│                            │├── Scopes ←─────────────────┤ (many-to-many with sessions)│                            │└── Sessions ←───────────────┤ (many-to-many with peers)    ├── Peers ───────────────┘    └── Messages (session-level)

SDKs

• Python — honcho-ai on PyPI · source in sdks/python/
• TypeScript — @honcho-ai/sdk on npm · source in sdks/typescript/
• CLI — honcho-cli on PyPI · source in honcho-cli/ · CLI reference

SDKs are versioned independently of the server. Current SDK versions track each other; the server badge above reflects the deployed server version.

See the SDK Reference for full API surface, the API Reference for the raw HTTP API, and per-SDK example folders for runnable demos.

Learn More

• Developer documentation — full API surface, guides, integrations.
• CLI reference — local stack, inspect/debug commands, scripting.
• Plastic Labs blog — design philosophy and history of the project.

Contributing

Pull requests are limited to maintainers. If you find a bug or want a change, please open an issue instead. If you think your work is an exception, make the case in Discord, where maintainers are most active.

See CONTRIBUTING.md for the full process, an architecture walkthrough, and a map of where to change what. For vulnerabilities, see SECURITY.md — note that Honcho does not operate a bug bounty.

License

Honcho is licensed under the AGPL-3.0 License. Learn more at the License file.