What is Awesome Agent Quickstart

awesome-agent-quickstart is a repository designed to simplify the development of AI agents by providing ready-to-run templates for various agent frameworks, enabling users to quickly set up and run their first agent in just a few minutes.

Use cases

Use cases include building conversational agents, implementing role-playing scenarios, integrating with web tools, and experimenting with different AI frameworks to enhance agent capabilities.

How to use

To use awesome-agent-quickstart, clone the repository, set up a virtual environment with Python 3.13, and follow the step-by-step guides provided in each framework’s directory to run examples and create your own agents.

Key features

Key features include zero configuration needed for setup, runnable examples for learning, model-agnostic support for any large language model (LLM), and centralized configuration for shared settings across frameworks.

Where to use

awesome-agent-quickstart can be used in various fields such as AI development, chatbot creation, automated customer support, and any application that requires intelligent agent behavior.

awesome-agent-quickstart

Your fast lane to AI Agent development!

This repository helps you bypass setup complexities and dive straight into latest AI Agent frameworks. Go from zero to running your first agent in minutes, whether you’re interested in LangGraph, AutoGen, Smolagents, or other popular frameworks.

🎯 Features

• ⚡ Zero configuration needed - get started in minutes
• 🎓 Learn by example - all examples are runnable
• 🔄 Model-agnostic - support any LLM
• 🛠️ Centralized configuration - shared settings across framework

🚀 Supported Frameworks

Ready-to-run templates for:

Flow control

• LangChain
• LangGraph (Python&JS)
• Smolagents

Role-playing

• LangGraph-Swarm
• OpenAI Swarm
• AutoGen
• CrewAI
• OpenAI Agents SDK

Tools

• LangChain MCP Adapters
• Browser-use

📁 Project Structure

Each directory is a self-contained example of an agent framework. For example:

awesome-agent-quickstart/
├── langgraph/                # Framework name
│   ├── config.py             # Common configurations (model params, API settings&checking)
│   ├── helloworld.py         # Basic example: Simple conversational agent
│   ├── requirements.txt      # Dependency management
│   └── .env.example          # Environment variables template
│   └── READIT.md             # The framework's original README
│   └── README.md             # A step by step guide to use the framework

🐍 Setup

Since some frameworks reply on the latest Python features, we recommend create a virtual environment:

conda create -n agents-quickstart python=3.13
conda activate agents-quickstart

🤝 Contributing

Contributions for more agent framework examples are welcome! Please ensure:

1. Create examples under the respective framework directory
2. Use common configurations from config.py
3. Provide clear documentation and comments
4. Include requirements.txt and .env.example
5. Follow project code style and best practices

📝 Development Guidelines

1. Code Structure

   • Follow modular design
   • Separate configuration from logic
   • Include proper error handling
   • Keep dependency files up-to-date

2. Documentation

   • Add docstrings for main functions and classes
   • Include usage examples
   • Explain key concepts and design decisions

3. Security

   • Use environment variables for sensitive data
   • Include rate limiting considerations
   • Add proper input validation

See Contributing Guidelines for more details.

📃 License

MIT License - see LICENSE

🌟 Community

• ⭐ Star us on GitHub
• 🐛 Report issues
• 📧 Contact: [email protected]

🙏 Acknowledgments

Made with ❤️ by the AI community, for the AI community.

• Thanks to all contributors!
• Thanks to the framework development teams!
• Thanks to the LLM community!