Guide · Open source
How to use awesome-ai-apps: clone, explore and run examples
Updated · written with AI from the project's own documentation and checked against our catalogue data
awesome-ai-apps is an MIT-licensed, Python-based collection of example projects, tutorials and starter recipes for building LLM-powered applications such as AI agents, voice assistants, RAG apps and MCP-backed tools. It is aimed at developers, students and engineers who want working code to study. This guide shows you how to get a local copy, find the example that matches your goal, and run one.
Awesome AI AppsArindam200/awesome-ai-apps · ★ 16.7k · MIT Install options & downloads Quick start
- 1Install Git and Python. Make sure Git and Python are available in your terminal. These are the prerequisites listed in the README.
- 2Clone the repository. Run git clone https://github.com/Arindam200/awesome-ai-apps to download the examples to your machine.
- 3Choose a category. Pick a section such as Starter Agents, RAG Applications or Voice Agents based on what you want to learn or build.
- 4Open the project folder. Go into the folder linked from the README for your chosen example and read its own instructions.
- 5Set up the environment. Create a Python environment and install the dependencies that the project's README lists.
- 6Add your API keys and run. Provide the keys the project needs, then run the entry point its README describes and study the code.
What is awesome-ai-apps and what does it contain?
The README describes the repository as a collection of 132 projects, tutorials and recipes. Each entry is a separate example rather than one application, and the examples span several frameworks and stacks.
The main categories listed in the README are Starter Agents, Simple Agents, Voice Agents, MCP Agents, Memory Agents, RAG Applications, Advanced Agents and Fine-Tuning, plus a section of tutorials and videos.
- Starter Agents: quick-start agents for learning and extending different AI frameworks
- Voice Agents: voice assistant examples
- MCP Agents: agents that use MCP-backed tools
- RAG Applications: retrieval-augmented generation examples
- Fine-Tuning: fine-tuning examples
What do you need before you start?
The install method is clone-and-build. The README's prerequisites are Git and Python. Most entries assume familiarity with Python and with API keys for the model providers they call, so have those ready before you start a project.
- Git, to clone the repository
- Python, to run the example projects
- Familiarity with Python and API keys (required by most entries)
How do you clone the repository?
Open a terminal and run the clone command from the README. This creates a local folder with all the example projects.
git clone https://github.com/Arindam200/awesome-ai-appsHow do you pick the right example?
Start from your goal rather than from the file list. The README links each entry to its folder, for example starter_ai_agents/langchain_starter for a LangChain tool-calling agent or starter_ai_agents/autogen_starter for a Microsoft AutoGen agent. Read the one-line description next to each link to see the framework and purpose.
If you are comparing frameworks, open two starters that solve a similar task and compare how each one defines tools and runs the agent. If you want a RAG or voice template, use the RAG Applications or Voice Agents section as your starting point.
- Learning a framework: pick a Starter Agent for that framework
- Comparing frameworks: open two starters with a similar task
- Building a RAG or voice app: start from that section's template
How do you run an individual project?
Each project is its own folder with its own requirements, so follow the README inside that folder rather than assuming one setup works for all. Set up the Python environment and supply any API keys the project asks for, then run the entry point its README describes.
Treat each example as a guide or starter. The README describes these as guides and starters rather than supported products, so expect to adapt them.
- Open the project folder and read its own README
- Create the Python environment and install what it lists
- Add the API keys it requires
What should you keep in mind when using the examples?
The repository is useful for learning and prototyping. It is not described as production-ready, so review the code for security, error handling and cost before using any pattern in a live service.
If you want a single polished product to use directly, this repository is not that. It is a set of separate examples.
- Not a single product: each entry is independent
- Not production-hardened: described as guides and starters
- Check API key handling and costs before deploying
What people use it for
- Learning how a framework such as LangChain or AutoGen defines and calls tools
- Studying a RAG example to see how documents are retrieved and passed to a model
- Starting a voice assistant prototype from a Voice Agents example
- Comparing how two frameworks handle the same agent task
- Using an MCP Agents example as a reference for tool integration
Frequently asked questions
Is awesome-ai-apps free to use?
The repository is licensed under MIT. The README does not list prices, but the examples call model and service providers that may require your own API keys and may charge for usage.
What do I need to install awesome-ai-apps?
The README lists Git and Python as prerequisites. You clone the repository with git clone and then set up each project individually.
Do I need to know Python to use it?
Yes. The README notes that most entries assume familiarity with Python and API keys, so non-developers will find it hard to run the examples.
Can I use awesome-ai-apps in production?
The README describes the projects as guides and starters rather than supported products, so they are not production-hardened. Review and adapt the code before using it in a live service.
How does awesome-ai-apps compare to LangChain, CrewAI or LlamaIndex?
Those are frameworks you build with, while awesome-ai-apps is a collection of example projects that show how to build with various frameworks. Many examples use LangChain, CrewAI or AutoGen, so the repository works well alongside them as a reference.
Where do I start if I am new to AI agents?
Start with the Starter Agents section and pick one framework you want to learn. Read its folder README, run the example, and then compare it with a second starter for a similar task.
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Our verdict
Use awesome-ai-apps if you are a developer, student or engineer who wants working Python examples to study or to use as a starting template for agents, RAG or voice apps. Pick something else if you need a single polished product you can use without coding, or production-hardened code that a team can rely on.
Install options & downloads