What is awesome-ai-agents?

awesome-ai-agents is a collection of ready to run example projects that show how to build AI agents, meaning programs that use a language model to complete a task rather than just answer a single question. Each example is provided in both Python and TypeScript, and the README promises every one can be cloned and run within about five minutes, with no shared setup or build step between examples. You simply clone the repository, pick an example folder, and follow that folder's own instructions. The repository organizes its examples into several categories. There are starter agents that each focus on one simple concept, such as a research agent that produces a markdown report, a code review agent, a data analyst agent that writes and runs its own code to analyze a CSV file, a web scraping agent, a customer support agent, and smaller utilities like a git commit message generator, a regex generator, and a Dockerfile generator. There are also advanced agents meant to show production style patterns, including a deep research agent that runs searches in parallel while tracking cost, a coding agent that plans, writes, tests, and fixes code in a loop, a computer use agent that controls a browser through screenshots, a pull request review agent, and a log analyzer. Beyond single agents, the repository includes multi agent examples, where several agents hand work off to each other, such as a small software development team made up of a product manager, architect, developer, and reviewer role. There is a retrieval augmented generation section showing patterns like a PDF chatbot and a codebase question and answer tool, and a Model Context Protocol section with example servers and clients, including a Postgres server and a GitHub server exposed as tools an agent can call. Every individual example follows the same layout: a README explaining what it does and how it works, a Python folder with its own script and requirements file, and a TypeScript folder with its own script and package file, plus an example environment file for adding API keys. Examples rely on either OpenAI or Anthropic models, and some also use a third party tool called Reader for fetching and reading web content. The project is licensed under MIT and welcomes contributions.