There is a repository that collects more than 100 open-source AI projects -- from simple agents to multi-agent teams, voice agents, MCP integrations, and RAG applications -- all in one place, with code you can open, read, and run.
There is a repository that collects more than 100 open-source AI projects -- from simple agents to multi-agent teams, voice agents, MCP integrations, and RAG applications -- all in one place, with code you can open, read, and run.
Awesome LLM Apps is a curated collection of over 100 open-source AI agent, agent skill, and RAG projects, released under the Apache-2.0 license. The repository is not a framework or a unified platform. It is a shelf of independent example projects, each in its own folder with its own code, dependencies, and setup instructions. You find a problem that matches yours, open the code, see how the pieces connect, and adapt it.
The examples are organized into categories: starter AI agents, advanced AI agents, multi-agent teams, voice AI agents, MCP agents, RAG tutorials, agent skills, and generative UI agents. The repository includes examples using Claude, Gemini, GPT, DeepSeek, Llama, Qwen, and other open-source models, so you can find something close to whatever stack you are already using. At the time of research, the repository showed approximately 128.6 thousand GitHub stars.
Running an example is straightforward. You clone the repository, navigate to the specific project folder you want to try, install its requirements, and launch it. Many projects use Streamlit for a quick web interface. For example, the travel agent quickstart involves cloning the repo, entering the starter_ai_agents/ai_travel_agent directory, running pip install -r requirements.txt, and launching with streamlit run travel_agent.py. Of course, you still need to add your own API keys and read the README for each specific project, since every example has its own configuration requirements.
The project count, star count, and model list can change over time. The "hand-built, tested end-to-end" claim is the author's description, not an independent audit. Each project uses different dependencies, API keys, and external services. Code that runs is not the same as code that is production-ready. Before using any example in a real product, you need to evaluate security, API costs, data privacy, error handling, and operational stability. The repository is best treated as a learning and prototyping resource, not a template library for production deployment.
Awesome LLM Apps is for developers, students, and builders who want to learn AI application patterns from real, runnable code. If you are prototyping an AI feature and want to see how someone else solved a similar problem, this is an excellent starting point. If you need a production system, take the ideas and structure from here, but do not copy-paste directly into production without your own engineering review.
This is a repository worth bookmarking if you want to learn from real code or build prototypes quickly. For production, borrow the patterns and structure, but do your own engineering diligence.