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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.

• ⚡ 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

Ready-to-run templates for:

• LangChain
• LangGraph (Python&JS)
• Smolagents

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

• LangChain MCP Adapters
• Browser-use

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

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

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

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

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

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

SeeContributing Guidelines for more details.

MIT License - seeLICENSE

• ⭐ Star us on GitHub
• 🐛Report issues
• 📧 Contact:ababai.ai@outlook.com

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!