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, Cursor, Grok Bot, 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, Cursor, Grok Bot, OpenCode, Kilo Code, OpenClaw, Hermes.

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 · Install Honcho from the Cursor Marketplace · integrations/cursor Grok Bot · Add to Grok Bot from the Honcho plugin page · integrations/cursor DeepSeek Harness · dsh plugin --profile 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. 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-honcho
codex-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-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.

Internal storage (Collections & Documents)

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