MCP ExplorerExplorer

Awesome Ai Apps

@Arindam200on 19 days ago
285 MIT
FreeCommunity
AI Systems
#agents#ai#llm#mcp
Collection of AI Applications

Overview

What is Awesome Ai Apps

Awesome AI Apps is a comprehensive collection of practical examples, tutorials, and recipes designed to help users build powerful LLM-powered applications using various frameworks and tools.

Use cases

Use cases include developing chatbots, creating AI systems for data analysis, building recommendation engines, and implementing natural language processing applications.

How to use

To use Awesome AI Apps, clone the repository from GitHub, navigate to the desired project directory, install the required dependencies using pip, and follow the project-specific instructions provided in each README.md file.

Key features

Key features include a wide range of AI application examples, detailed tutorials, community contributions, and support for various frameworks and tools for building LLM applications.

Where to use

Awesome AI Apps can be used in various fields such as software development, education, research, and any domain that requires the implementation of AI-driven solutions.

Content

Awesome AI Apps

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Powered by Nebius AI Studio - Your one-stop platform for building and deploying AI applications.

A comprehensive collection of practical examples, tutorials and recipes showcasing how to build powerful LLM-powered applications using various frameworks and tools.

From simple chatbots and MCP examples to advance AI Agents, this repository serves as your guide to building some cool AI applications.

🚀 Featured AI Agent Frameworks

🛠️ Featured Tools & APIs

🤖 Featured LLM Models

📺 Playlist of Demo Videos & Tutorials

Getting Started

Prerequisites

  • Python 3.10 or higher
  • Git
  • pip (Python package manager)

Installation Steps

  1. Clone the repository

    git clone https://github.com/Arindam200/awesome-ai-apps.git
    
  2. Navigate to the desired project directory

    cd awesome-ai-apps/starter_ai_agents/agno_starter
    
  3. Install the required dependencies

    pip install -r requirements.txt
    
  4. Follow project-specific instructions

    • Each project has its own README.md with detailed setup and usage instructions
    • Make sure to read the project-specific documentation before running the application

🤝 Contributing

We welcome contributions from the community! Whether you’re a beginner or an expert, your examples and tutorials can help others learn and grow. Here’s how you can contribute:

  1. Submit a Pull Request with your LLM application example
  2. Add detailed documentation and setup instructions
  3. Include requirements.txt or environment.yml
  4. Share your experience and best practices

📜 License

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

Thank You for the Support! 🙏

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