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AI Apps

Source: https://github.com/Arindam200/awesome-ai-apps at 276e635f3f2b
License: MIT
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This repository is a comprehensive collection of 132 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.


A huge thank you to our sponsors for their generous support!

Bright Data - Web Data Platform
Web Data Platform
Visit Bright Data website
Nebius Token Factory
AI Inference Provider
Visit Nebius Token Factory
ScrapeGraphAI - Web Scraping Library
AI Web Scraping framework
View ScrapeGraphAI on GitHub
Memori - SQL Native Memory for AI
SQL Native Memory for AI
Visit Memorilabs website
CopilotKit - Agentic Application Platform
Agentic Application Platform
Visit CopilotKit website
ScaleKit - Auth Stack for AI
Auth Stack for AI
Visit ScaleKit website
Okahu - AI Platform
AI Observability Platform
Visit Okahu website
AgentField - Kubernetes for AI Agents
Kubernetes for AI Agents
Visit AgentField website
Byteful
Byteful
Visit Byteful website

Interested in sponsoring this project? Feel free to reach out!
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Quick-start agents for learning and extending different AI frameworks. 21 projects

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

Real-time voice assistants and streaming speech pipelines — including LiveKit, Pipecat, Gradium, and VoxCode (Deepgram + Cursor SDK). 9 projects

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

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

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

Complex multi-agent pipelines for production-ready end-to-end workflows. 35 projects

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


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  • 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)
  1. Clone the repository

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

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

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

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

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

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.

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

This project is actively maintained by:

Arindam Majumder     Shivay Lamba     Astrodevil

Arindam Majumder  ·  Shivay Lamba  ·  Amitesh Anand

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

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Multi-agent execution runs research, build, and review in parallel.
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