This repository curates a diverse collection of practical and creative Large Language Model (LLM) applications, focusing on AI Agents and Retrieval Augmented Generation (RAG). It targets developers and researchers interested in exploring and building with various LLM providers (OpenAI, Anthropic, Gemini) and local open-source models, offering a valuable resource for learning and contribution.

How It Works

The collection showcases applications integrating LLMs with RAG for enhanced knowledge retrieval and AI Agents for task automation. Projects demonstrate diverse use cases, from data analysis and content generation to complex multi-agent systems and multimodal interactions, highlighting practical implementations of cutting-edge LLM capabilities.

Quick Start & Requirements

• Install by cloning the repository: git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
• Navigate to a specific project directory (e.g., cd awesome-llm-apps/starter_ai_agents/ai_travel_agent).
• Install dependencies: pip install -r requirements.txt
• Follow individual project READMEs for setup and execution.
• Prerequisites vary per project but generally include Python and potentially API keys for specific LLM providers.

Highlighted Details

• Features a wide array of AI Agents, from starter projects to advanced and multimodal agents.
• Includes numerous RAG implementations, covering various techniques like agentic RAG, CRAG, and local RAG.
• Showcases advanced LLM applications such as chatbots with memory, fine-tuning, and multi-LLM systems.
• Highlights a Global AI Agent Hackathon with significant cash prizes and credits.

Maintenance & Community

The project is actively maintained and encourages community contributions via GitHub Issues and Pull Requests. Users are encouraged to star the repository for updates.

Licensing & Compatibility

The repository itself is not explicitly licensed in the provided README. Individual projects within the collection may have their own licenses, requiring careful review for commercial use or closed-source integration.

Limitations & Caveats

The README does not specify a repository-wide license, which may pose challenges for commercial adoption. Setup and execution complexity will vary significantly between individual projects, requiring careful attention to each project's specific README.