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Honcho is an open source memory library with a managed service for building stateful agents. Use it with any model, framework, or architecture. It enables agents to build and maintain state about any entity--users, agents, groups, ideas, and more. And because it's a continual learning system, it understands entities that change over time. 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.

TL;DR - Getting Started

With Honcho you can easily setup your application's workflow, save your interaction history, and leverage the reasoning it does to inform the behavior of your agents

Typescript examples are available in our docs.

1. Install the SDK

# Python
pip install honcho-ai
uv add honcho-ai
poetry add honcho-ai

2. Setup your Workspace, Peers, Session, and send Messages

from honcho import Honcho

# 1. Initialize your Honcho client
honcho = Honcho(workspace_id="my-app-testing")

# 2. Initialize peers
alice = honcho.peer("alice")
tutor = honcho.peer("tutor")

# 3. Create a session and add messages

session = honcho.session("session-1")
# Adding messages from a peer will automatically add them to the session
session.add_messages(
    [
        alice.message("Hey there — can you help me with my math homework?"),
        tutor.message("Absolutely. Send me your first problem!"),
    ]
)

3. Leverage reasoning from Honcho to inform your agent's behavior

### 1. Use the chat endpoint to ask questions about your users in natural language
response = alice.chat("What learning styles does the user respond to best?")

### 2. Use session context to continue a conversation with an LLM
context = session.context(summary=True, tokens=10_000)

# Convert to a format to send to OpenAI and get the next message
openai_messages = context.to_openai(assistant=tutor)

from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
  model="gpt-4",
  messages=openai_messages
)

### 3. Search for similar messages
results = alice.search("Math Homework")

### 4. Get a session-scoped representation of a peer
alice_representation = session.representation(alice)

This is a simple example of how you can use Honcho to build a chatbot and leverage insights to personalize the agent's behavior.

Sign up at app.honcho.dev to get started with a managed version of Honcho.

Learn more ways to use Honcho on our developer docs.

Read about the design philosophy and history of the project on our blog.

Project Structure

• Usage
• Local Development
  • Prerequisites and Dependencies
  • Setup
  • Docker
  • Deploy on Fly
• Configuration
  • Using config.toml
  • Using Environment Variables
  • Configuration Priority
  • Example
• Architecture
  • Storage
  • Reasoning
  • Retrieving Data & Insights
• Contributing
• License

The Honcho project is split between several repositories with this one hosting the core service logic. This is implemented as a FastAPI server/API to store data about an application's state.

There are also client SDKs implemented in the sdks/ directory with support for Python and TypeScript.

• Python
• TypeScript

Examples on how to use the SDK are located within each SDK folder and in the SDK Reference

There are also documented examples of how to use the core SDKs in the API Reference section of the documentation.

Usage

Sign up for an account at https://app.honcho.dev and get started with $100 free credits. When you sign up you'll be prompted to join an organization which will have a dedicated instance of Honcho.

Provision API keys and change your base url to point to https://api.honcho.dev

Additionally, Honcho can be self-hosted for testing and evaluation purposes. See the Local Development section below for details on how to set up a local version of Honcho.

Local Development

Below is a guide on setting up a local environment for running the Honcho Server.

This guide was made using a M3 Macbook Pro. For any compatibility issues on different platforms, please raise an Issue.

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>

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.

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 representation, summaries, peer cards, and manages dreaming tasks. You can increase the number of deriver's to improve runtime efficiency.

Pre-commit Hooks

Honcho uses pre-commit hooks to ensure code quality and consistency across the project. These hooks automatically run checks on your code before each commit, including linting, formatting, type checking, and security scans.

Installation

To set up pre-commit hooks in your development environment:

1. Install pre-commit using uv

uv add --dev pre-commit

2. Install the pre-commit hooks

uv run pre-commit install \
    --hook-type pre-commit \
    --hook-type commit-msg \
    --hook-type pre-push

This will install hooks for pre-commit, commit-msg, and pre-push stages.

What the hooks do

The pre-commit configuration includes:

• Code Quality: Python linting and formatting (ruff), TypeScript linting (biome)
• Type Checking: Static type analysis with basedpyright
• Security: Vulnerability scanning with bandit
• Documentation: Markdown linting and license header checks
• Testing: Automated test runs for Python and TypeScript code
• File Hygiene: Trailing whitespace, line endings, file size checks
• Commit Standards: Conventional commit message validation

Manual execution

You can run the hooks manually on all files without making a commit:

uv run pre-commit run --all-files

Or run specific hooks:

uv run pre-commit run ruff --all-files
uv run pre-commit run basedpyright --all-files

Docker

As mentioned earlier a docker-compose template is included for running Honcho. As an alternative to running Honcho locally it can also be run with the compose template.

The docker-compose template is set to use an environment file called .env. You can also copy the .env.template and fill with the appropriate values.

Copy the template and update the appropriate environment variables before launching the service:

cd honcho
cp .env.template .env
# update the file with openai key and other wanted environment variables
cp docker-compose.yml.example docker-compose.yml
docker compose up

Deploy on Fly

The API can also be deployed on fly.io. Follow the Fly.io Docs to setup your environment and the flyctl.

A sample fly.toml is included for convenience.

Note: The fly.toml does not include launching a Postgres database. This must be configured separately

Once flyctl is set up use the following commands to launch the application:

cd honcho
flyctl launch --no-deploy # Follow the prompts and edit as you see fit
cat .env | flyctl secrets import # Load in your secrets
flyctl deploy # Deploy with appropriate environment variables

Configuration

Honcho uses a flexible configuration system that supports both TOML files and environment variables. Configuration values are loaded in the following priority order (highest to lowest):

1. Environment variables
2. .env file (for local development)
3. config.toml file
4. Default values

Using config.toml

Copy the example configuration file to get started:

cp config.toml.example config.toml

Then modify the values as needed. The TOML file is organized into sections:

• [app] - Application-level settings (log level, session limits, embedding settings, namespace)
• [db] - Database connection and pool settings
• [auth] - Authentication configuration
• [cache] - Redis cache configuration
• [llm] - LLM provider API keys and general settings
• [deriver] - Background worker settings and representation configuration
• [peer_card] - Peer card generation settings
• [dialectic] - Dialectic API configuration with per-level reasoning settings
• [summary] - Session summarization settings
• [dream] - Dream processing configuration (including specialist models and surprisal settings)
• [webhook] - Webhook configuration
• [metrics] - Prometheus pull-based metrics
• [telemetry] - CloudEvents telemetry for analytics
• [vector_store] - Vector store configuration (pgvector, turbopuffer, or lancedb)
• [sentry] - Error tracking and monitoring settings

Using Environment Variables

All configuration values can be overridden using environment variables. The environment variable names follow this pattern:

• {SECTION}_{KEY} for top-level section settings
• Use __ inside {KEY} for nested settings
• Just {KEY} for app-level settings

Examples:

• DB_CONNECTION_URI - Database connection string
• AUTH_JWT_SECRET - JWT secret key
• DERIVER_MODEL_CONFIG__TRANSPORT - Transport for the background deriver
• SUMMARY_MODEL_CONFIG__MODEL - Summary model override
• DIALECTIC_LEVELS__low__MODEL_CONFIG__MODEL - Model for low reasoning level
• LOG_LEVEL - Application log level
• METRICS_ENABLED - Enable Prometheus metrics
• TELEMETRY_ENABLED - Enable CloudEvents telemetry

Configuration Priority

When a configuration value is set in multiple places, Honcho uses this priority:

1. Environment variables - Always take precedence
2. .env file - Loaded for local development
3. config.toml - Base configuration
4. Default values - Built-in defaults

This allows you to:

• Use config.toml for base configuration
• Override specific values with environment variables in production
• Use .env files for local development without modifying config.toml

Example

If you have this in config.toml:

[db]
CONNECTION_URI = "postgresql+psycopg://localhost/honcho_dev"
POOL_SIZE = 10

You can override just the connection URI in production:

export DB_CONNECTION_URI="postgresql+psycopg://prod-server/honcho_prod"

The application will use the production connection URI while keeping the pool size from config.toml.

Architecture

The functionality of Honcho can be split into two different services: Storage and Insights.

Peer Paradigm

Honcho uses an entity-centric model where both users and agents are represented as "peers". This unified approach 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

Key Features

• Rich Reasoning System: Multiple implementation methods that extract conclusions from interactions and build comprehensive representations of peers
• Chat API: Provides 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

Storage

Honcho contains several different primitives used for storing application and peer data. This data is used for managing conversations, modeling peer identity, building RAG applications, and more.

The philosophy behind Honcho is to provide a platform that is peer-centric and easily scalable from a single user to a million.

Below is a mapping of the different primitives and their relationships.

Workspaces
├── Peers ←──────────────────┐
│   ├── Sessions             │
│   └── Collections          │
│       └── Documents        │
│                            │
│                            │
└── Sessions ←───────────────┤ (many-to-many)
    ├── Peers ───────────────┘
    └── Messages (session-level)

Relationship Details:

• A Workspace contains multiple Peers
• Peers and Sessions have a many-to-many relationship (peers can participate in multiple sessions, sessions can have multiple peers)
• Messages can exist at two levels:
  • Session-level: Communication between peers within a session
• Collections belong to specific Peers
• Documents are stored within Collections

Users familiar with APIs such as the OpenAI Assistants API will be familiar with much of the mapping here.

Workspaces

This is the top level construct of Honcho. Developers can register different Workspaces for different assistants, agents, AI enabled features, etc. It is a way to isolate data between use cases and provide multi-tenant capabilities.

Peers

Within a Workspace everything revolves around a Peer. The Peer object represents any participant in the system - whether human users or AI agents. This unified model enables complex multi-participant interactions.

Sessions

The Session object represents a set of interactions between Peers within a Workspace. Other applications may refer to this as a thread or conversation. Sessions can involve multiple peers with configurable observation settings.

Messages

The Message represents an atomic data unit that can exist at two levels:

• Session-level Messages: Communication between peers within a session context

All messages are labeled by their source peer and can be processed asynchronously to update their representations. This flexible design allows for both conversational interactions and broader data ingestion for personality modeling.

Collections

At a high level a Collection is a named group of Documents. Developers familiar with RAG based applications will be familiar with these. Collections store vector embedded data that developers and agents can retrieve against using functions like cosine similarity.

Collections are also used internally by Honcho while creating representations of peers.

Documents

As stated before a Document is vector embedded data stored in a Collection.

Reasoning

The reasoning functionality of Honcho is built on top of the Storage service. As Messages and Sessions are created for Peers, Honcho will asynchronously reason about peer psychology to derive facts about them and store them in reserved Collections.

A high level summary of the pipeline is as follows:

1. Messages are created via the API
2. Derivation Tasks are enqueued for background processing including:
   • representation: To update representations of Peers
   • summary: To create summaries of Sessions
3. Session-based queue processing ensures proper ordering
4. Results are stored internally

Retrieving Data & Insights

Honcho exposes several different ways to retrieve data from the system to best serve the needs of any given application.

Get Context

In long-running conversations with an LLM, the context window can fill up quickly. To address this, Honcho provides a context endpoint that returns a combination of messages, conclusions, summaries from a session up to a provided token limit.

Use this to keep sessions going indefinitely. If you'd like to see this in action, try out Honcho Chat.

Search

There are several search endpoints that let developers query messages at the Workspace, Session, or Peer level using a hybrid search strategy.

Requests can include advanced filters to further refine the results.

Chat API

The flagship interface for using these insights is through the Chat Endpoint.

This is a regular API endpoint (/peers/{peer_id}/chat) that takes natural language requests to get data about the Peer. This robust design lets us use this single endpoint for all cases where extra personalization or information about the Peer is necessary.

A developer's application can treat Honcho as an oracle to the Peer and consult it when necessary. Some examples of how to leverage the Dialectic API include:

• Asking Honcho for a generic or specific insight about the Peer
• Asking Honcho to hydrate a prompt with data about the Peers behavior
• Asking Honcho for a 2nd opinion or approach about how to respond to the Peer
• Getting personalized responses that incorporate long-term facts and context

Representations

For low-latency use cases, Honcho provides access to a representation endpoint that returns a static document with insights about a Peer in the context of a particular session.

Use this to quickly add context to a prompt without having to wait for an LLM response.

Contributing

We welcome contributions to Honcho! Please read our Contributing Guide for details on our development process, coding conventions, and how to submit pull requests.

License

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