areu01or00/smolagents

smolagents is a lightweight library that lets you build AI agents capable of breaking down complex tasks and taking actions on their own. Instead of just generating text, these agents write and execute actual code to solve problems, like searching the web, processing data, or interacting with external tools. Here is how it works under the hood: a typical AI agent gets a task, thinks about what to do, calls a tool, and then uses the result to decide the next step. The twist with this project's primary approach is that the AI writes its action plan as Python code rather than a structured text blob. Writing actions as code is proven to be more efficient, using about 30% fewer steps and achieving better results on difficult benchmarks. Because running AI-generated code can be a security risk, the library includes options to run that code safely in isolated sandboxes, keeping your actual system safe. This tool is for developers, founders, or product managers who want to build AI workflows that actually do things. For example, you could build an agent that browses a retail website, navigates to the sale section, finds a specific item, and returns the price. You could also create an agent that takes a natural language request, plans a multi-city trip, and uses web searches to gather the necessary travel details. It works with almost any underlying AI model, whether you want to use a local open-source model or commercial APIs like OpenAI or Anthropic. What stands out about the project is its commitment to simplicity. The core logic is under 1,000 lines of code, meaning it avoids the heavy complexity of larger frameworks. Despite its small size, it supports multi-agent hierarchies, handles text, vision, and audio inputs, and lets you share your custom tools or agents with others via the Hugging Face Hub. It provides just enough structure to keep AI actions consistent without getting in your way.