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Awesome AI Apps Awesome

This repository is a comprehensive collection of 128 practical projects, tutorials, and recipes for building powerful LLM-powered applications, including text agents, voice assistants, RAG apps, and MCP-backed tools. These projects serve as a guide for developers working with various AI frameworks and stacks.

📋 Table of Contents


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🚀 Featured AI Apps

🧩 Starter Agents

Quick-start agents for learning and extending different AI frameworks. 20 projects

🪶 Simple Agents

Straightforward, practical use-cases for everyday AI applications. 18 projects

🎙️ Voice Agents

Real-time voice assistants and streaming speech pipelines. 8 projects

🗂️ MCP Agents

Examples using Model Context Protocol for external tool integration. 14 projects

🧠 Memory Agents

Agents with advanced memory capabilities for context retention and personalization. 13 projects

📚 RAG Applications

Retrieval-augmented generation examples for document understanding and knowledge bases. 18 projects

🔬 Advanced Agents

Complex multi-agent pipelines for production-ready end-to-end workflows. 31 projects

🧬 Fine-Tuning

End-to-end examples of fine-tuning open-source LLMs, from data prep to deployment. 6 projects

📺 Tutorials & Videos

🎓 Course Playlists

🔧 Framework Tutorials


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Getting Started

Prerequisites

  • Python 3.10+ (Python 3.11+ recommended for newer projects)
  • Git for cloning the repository
  • Package Manager: pip or uv (recommended for faster installs)
  • API Keys: Most projects require API keys (see individual project READMEs)

Quick Start

  1. Clone the repository

    git clone https://github.com/Arindam200/awesome-ai-apps.git
    cd awesome-ai-apps
    
  2. Choose a project and navigate to its directory

    cd starter_ai_agents/agno_starter  # Example: Start with Agno starter
    
  3. Set up environment variables

    cp .env.example .env  # Copy example environment file
    # Edit .env with your API keys
    
  4. Install dependencies

    # Using pip
    pip install -r requirements.txt
    
    # OR using uv (recommended - faster)
    uv sync
    # or
    uv pip install -e .
    
  5. Run the project

    python main.py
    # or for Streamlit apps
    streamlit run app.py
    

🤝 Contributing

We welcome contributions from the community! Here's how you can help:

  • 💡 Add new projects: Submit your own AI agent examples
  • 🔧 Fix issues: Contribute code improvements and bug fixes
  • 📝 Improve documentation: Help make projects more accessible
  • 🐛 Report bugs or suggest improvements via GitHub Issues

Before contributing:

  • Read our Contributing Guidelines for detailed information
  • Check existing issues to avoid duplicates
  • Follow the project structure and naming conventions
  • Ensure your project includes a comprehensive README.md

Important: This project follows a Contributor Code of Conduct. By participating, you agree to abide by its terms.

📜 License

This repository is licensed under the MIT License. Feel free to use and modify the examples for your projects.

👥 Core Maintainers

This project is actively maintained by:

For any questions, suggestions, or contributions, feel free to reach out to the maintainers.

Thank You for the Support! 🙏

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