Honcho Python SDK

The official Python library for the Honcho conversational memory platform. Honcho provides tools for managing peers, sessions, and conversation context across multi-party interactions, enabling advanced conversational AI applications with persistent memory and theory-of-mind capabilities.

Installation

pip install honcho-ai

Quick Start

from honcho import Honcho

# Initialize client
client = Honcho(api_key="your-api-key")

# Create peers (participants in conversations)
alice = client.peer("alice")
bob = client.peer("bob")

# Create a session for group conversations
session = client.session("conversation-1")

# Add messages to the session
session.add_messages([
    alice.message("Hello, Bob!"),
    bob.message("Hi Alice, how are you?")
])

# Wait for deriver to process all messages (only necessary if very recent messages are critical to query)
client.poll_deriver_status()

# Query conversation context
response = alice.chat("What did Bob say to the user?")
print(response)

Core Concepts

Peers

Peers represent participants in conversations.

# Create peers
assistant = client.peer("assistant")
user = client.peer("user-123")

# Chat with global context
response = user.chat("What did I talk about yesterday?")

# Chat with perspective of another peer
response = user.chat("Does the assistant know my preferences?", target=assistant)

Sessions

Sessions group related conversations and messages:

# Create a session
session = client.session("project-discussion")

# Add peers to session
session.add_peers([alice, bob])

# Add messages
session.add_messages([
    alice.message("Let's discuss the project timeline"),
    bob.message("I think we need two more weeks")
])

# Get conversation context
context = session.get_context()

Messages and Context

Retrieve and use conversation history:

# Get messages from a session
messages = session.get_messages()

# Convert to OpenAI format for further prompting
openai_messages = context.to_openai(assistant="assistant")

# Convert to Anthropic format for further prompting
anthropic_messages = context.to_anthropic(assistant="assistant")

Async Support

from honcho import AsyncHoncho

async def main():
    client = AsyncHoncho(api_key="your-api-key")

Metadata Management

# Set peer metadata
user.set_metadata({"location": "San Francisco", "preferences": {"theme": "dark"}})

# Session metadata
session.set_metadata({"topic": "project-planning", "priority": "high"})

Multi-Perspective Queries

# Alice's view of what Bob knows
response = alice.chat("Does Bob remember our discussion about the budget?", target=bob)

# Session-specific perspective
response = alice.chat("What does Bob think about this project?",
                     target=bob,
                     session_id=session.id)

Configuration

Environment Variables

export HONCHO_API_KEY="your-api-key"
export HONCHO_BASE_URL="https://api.honcho.dev"  # Optional
export HONCHO_WORKSPACE_ID="your-workspace"  # Optional

Client Options

client = Honcho(
    api_key="your-api-key",
    environment="production",  # or "local", "demo"
    workspace_id="custom-workspace",
    base_url="https://api.honcho.dev"
)

Examples

Check out the examples/ directory for complete usage examples:

example.py - Comprehensive feature demonstration
chat.py - Basic multi-peer chat
async_example.py - Async/await usage
search.py - Context search and retrieval

License

Apache 2.0 - see LICENSE for details.

Support

Metadata

Release files for honcho-ai 1.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for honcho-ai 1.2.0
File	Size	Uploaded
honcho_ai-1.2.0.tar.gz	23.3 kB	Jul 17, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for honcho-ai 1.2.0
File	Interpreter	ABI	Platform	Reset
honcho_ai-1.2.0-py3-none-any.whl 29.5 kB Jul 17, 2025	Python 3	none	any	Details

Total release size: 52.8 kB

Release files / honcho_ai-1.2.0.tar.gz

Download URL	honcho_ai-1.2.0.tar.gz
Size	23.3 kB
Tags	Source
SHA-256 checksum How to use checksums	`fafdf5b7d2727cae77ee9f9d813fbc4a38a79c52f11d5568cbfc2ae6fdee0a54`
BLAKE2b-256 checksum How to use checksums	`d3652e588da897498925c42a529bfd7fd6a8c43009ca495d74034232ec4a0641`
Upload date	Jul 17, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`uv/0.7.19`

Release files / honcho_ai-1.2.0-py3-none-any.whl

Download URL	honcho_ai-1.2.0-py3-none-any.whl
Size	29.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`1f011bfa5da8c84791e59583d113d86f0f5c0ca085e4006e4e90a66a295add23`
BLAKE2b-256 checksum How to use checksums	`aebd8428e665f68b8ce3c0cf151b1b72c44f3b8d442bc584efbe34076ca9e775`
Upload date	Jul 17, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`uv/0.7.19`