Hanzo Python SDK

The official Python SDK for the Hanzo AI platform, providing unified access to 100+ LLM providers through a single OpenAI-compatible API interface.

🚀 Features

Unified API: Single interface for 100+ LLM providers (OpenAI, Anthropic, Google, Meta, etc.)
OpenAI Compatible: Drop-in replacement for OpenAI SDK
Enterprise Features: Cost tracking, rate limiting, observability
Local AI Support: Run models locally with node infrastructure
Model Context Protocol (MCP): Advanced tool use and context management
Agent Framework: Build and orchestrate AI agents
Memory Management: Persistent memory and RAG capabilities
Network Orchestration: Distributed AI compute capabilities

📦 Installation

Basic Installation

pip install hanzoai

Full Installation (All Features)

pip install "hanzoai[all]"

Development Installation

git clone https://github.com/hanzoai/python-sdk.git
cd python-sdk
make setup

🎯 Quick Start

Basic Usage

from hanzoai import Hanzo

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

# Chat completion
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

Using Different Providers

# Use Claude
response = client.chat.completions.create(
    model="claude-3-opus-20240229",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Use local models
response = client.chat.completions.create(
    model="llama2:7b",
    messages=[{"role": "user", "content": "Hello!"}]
)

🏗️ Architecture

Package Structure

python-sdk/
├── pkg/
│   ├── hanzo/          # CLI and orchestration tools
│   ├── hanzo-mcp/      # Model Context Protocol implementation
│   ├── hanzo-agents/   # Agent framework
│   ├── hanzo-network/  # Distributed network capabilities
│   ├── hanzo-memory/   # Memory and RAG
│   ├── hanzo-aci/      # AI code intelligence
│   ├── hanzo-repl/     # Interactive REPL
│   └── hanzoai/        # Core SDK

Core Components

1. Hanzo CLI (`hanzo`)

Command-line interface for AI operations:

# Chat with AI
hanzo chat

# Start local node
hanzo node start

# Manage router
hanzo router start

# Interactive REPL
hanzo repl

2. Model Context Protocol (`hanzo-mcp`)

Advanced tool use and context management:

from hanzo_mcp import create_mcp_server

server = create_mcp_server()
server.register_tool(my_tool)
server.start()

3. Agent Framework (`hanzo-agents`)

Build and orchestrate AI agents:

from hanzo_agents import Agent, Swarm

agent = Agent(
    name="researcher",
    model="gpt-4",
    instructions="You are a research assistant"
)

swarm = Swarm([agent])
result = await swarm.run("Research quantum computing")

4. Network Orchestration (`hanzo-network`)

Distributed AI compute:

from hanzo_network import LocalComputeNode, DistributedNetwork

node = LocalComputeNode(node_id="node-001")
network = DistributedNetwork()
network.register_node(node)

5. Memory Management (`hanzo-memory`)

Persistent memory and RAG:

from hanzo_memory import MemoryService

memory = MemoryService()
await memory.store("key", "value")
result = await memory.retrieve("key")

🛠️ Development

Setup Development Environment

# Install Python 3.10+
make install-python

# Setup virtual environment
make setup

# Install development dependencies
make dev

Running Tests

# Run all tests
make test

# Run specific package tests
make test-hanzo
make test-mcp
make test-agents

# Run with coverage
make test-coverage

Code Quality

# Format code
make format

# Run linting
make lint

# Type checking
make type-check

Building Packages

# Build all packages
make build

# Build specific package
cd pkg/hanzo && uv build

📚 Documentation

Package Documentation

API Reference

See the API documentation for detailed API reference.

🔧 Configuration

Environment Variables

# API Configuration
HANZO_API_KEY=your-api-key
HANZO_BASE_URL=https://api.hanzo.ai

# Router Configuration
HANZO_ROUTER_URL=http://localhost:4000/v1

# Node Configuration
HANZO_NODE_URL=http://localhost:8000/v1

# Logging
HANZO_LOG_LEVEL=INFO

Configuration File

Create ~/.hanzo/config.yaml:

api:
  key: your-api-key
  base_url: https://api.hanzo.ai

router:
  url: http://localhost:4000/v1
  
node:
  url: http://localhost:8000/v1
  workers: 4
  
logging:
  level: INFO

🚢 Deployment

Docker

# Build image
docker build -t hanzo-sdk .

# Run container
docker run -p 8000:8000 hanzo-sdk

Docker Compose

# Start all services
docker-compose up

# Start specific service
docker-compose up router

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

Fork the repository
Create feature branch (git checkout -b feature/amazing-feature)
Make changes and test
Commit changes (git commit -m 'Add amazing feature')
Push to branch (git push origin feature/amazing-feature)
Open Pull Request

Code Standards

Follow PEP 8
Use type hints
Write tests for new features
Update documentation
Run make lint before committing

📊 Performance

Benchmarks

Operation	Latency	Throughput
Chat Completion	50ms	20 req/s
Embedding	10ms	100 req/s
Local Inference	200ms	5 req/s

Optimization Tips

Use streaming for long responses
Enable caching for repeated queries
Use batch operations when possible
Configure appropriate timeouts

🔒 Security

API keys are encrypted at rest
All communications use TLS 1.3+
Regular security audits
SOC 2 Type II certified

Report security issues to security@hanzo.ai

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

OpenAI for the API specification
Anthropic for Claude integration
The open-source community

📞 Support

Documentation: https://docs.hanzo.ai
Discord: https://discord.gg/hanzo
Email: support@hanzo.ai
GitHub Issues: https://github.com/hanzoai/python-sdk/issues

🗺️ Roadmap

Built with ❤️ by the Hanzo team

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