mirror of
https://github.com/hanzoai/python-sdk.git
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5d1cf72fcba5bf7d65fb22f2d6de180038a3f7d0
Mirror the v1.8.3 extension bridge fix on the python side. The MCP 'browser' tool now exposes: - tab_id — accepts the targetId returned by 'tabs' (e.g. "tab-1888868904") or a numeric tab id. Forwards as tabId so every action operates on the specified tab instead of the OS-active tab. Critical when the user has many windows open and the wrong one is in focus. - client_id — pin to a specific connected extension instance (returned in 'status'). Use with 'list_browsers' to address one Chrome instance vs another Firefox instance bridged to the same hanzo bridge. - target_browser — 'chrome' | 'firefox' per call. Per-call override of the global backend so a single MCP session can address Chrome and Firefox at different moments. Also expose two action names that the bridge already supports: select_tab — bring a specific tab to focus (Target.activateTarget) list_browsers — enumerate every connected provider with details The "tab-NNN" prefix from getTargets is automatically stripped and the numeric id forwarded; the bridge's parseTabId() handles both formats. Tested against extension v1.8.3 which lands the matching bridge changes.
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
- Hanzo CLI Documentation
- MCP Documentation
- Agents Documentation
- Network Documentation
- Memory 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 lintbefore 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 [email protected]
📄 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: [email protected]
- GitHub Issues: https://github.com/hanzoai/python-sdk/issues
🗺️ Roadmap
- Multi-modal support (images, audio, video)
- Enhanced caching strategies
- WebSocket streaming
- Browser SDK
- Mobile SDKs (iOS, Android)
Built with ❤️ by the Hanzo team
Languages
Python
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