This repository curates a collection of practical AI application examples, focusing on Retrieval-Augmented Generation (RAG), AI agents, and workflows. It targets developers and researchers looking to build and deploy LLM-powered applications, offering a practical guide with diverse frameworks and tools.

How It Works

The project showcases various AI agent frameworks like Google ADK, OpenAI Agents SDK, LangChain, LlamaIndex, Agno, and CrewAI. It integrates with tools and APIs such as Exa, Tavily, Firecrawl, BrightData, and multiple LLM models including Llama 3.1 and Qwen. This approach provides a broad overview of current AI development ecosystems and practical implementation patterns.

Quick Start & Requirements

• Install: Clone the repository (git clone https://github.com/Arindam200/awesome-ai-apps.git), navigate to a project directory, and install dependencies (pip install -r requirements.txt).
• Prerequisites: Python 3.10+, Git, pip.
• Setup: Each project has its own README.md with specific instructions.
• Resources: Links to demo videos and tutorials are available.

Highlighted Details

• Features a wide array of popular AI agent frameworks.
• Integrates with numerous data fetching and utility APIs.
• Showcases various state-of-the-art LLM models.
• Includes a playlist of demo videos and tutorials for practical learning.

Maintenance & Community

The repository welcomes community contributions for new examples and tutorials.

Licensing & Compatibility

Licensed under the MIT License, allowing for free use and modification in projects, including commercial applications.

Limitations & Caveats

Each project within the repository has its own specific setup and dependency requirements, necessitating careful review of individual README.md files before execution.