honcho-ai 1.0.0

Official DX Optimized Python SDK for Honcho

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-sdk

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?")
])

# Query conversation context
response = alice.chat("What did Bob say to me?")
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")
    
    peer = client.peer("user")
    response = await peer.chat("Hello!")
    print(response)

Metadata Management

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

# Query using metadata context
response = user.chat("What's the weather like where I am?")

# 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_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

• Documentation
• GitHub Issues
• Discord Community