smolagents

smolagents is Hugging Face's minimalist agent framework designed to make building tool-calling AI agents as simple as possible. The library embraces a philosophy of radical simplicity — the core agent loop is just a few hundred lines of code, making it easy to understand, debug, and extend. Despite its small footprint, smolagents supports sophisticated agent patterns including multi-step reasoning, tool calling, code generation, and multi-agent orchestration.

The framework provides two main agent types: ToolCallingAgent, which uses the model's native function calling capabilities, and CodeAgent, which generates and executes Python code to accomplish tasks. CodeAgent is particularly powerful — instead of being limited to pre-defined tool interfaces, the agent can write arbitrary Python code that combines tools, processes data, and implements custom logic on the fly.

Tool creation in smolagents is beautifully simple. Any Python function with a docstring and type hints automatically becomes an agent tool. The framework also provides a growing collection of pre-built tools for web search, image generation, text-to-speech, and more. Tools from the Hugging Face Hub can be loaded with a single line of code.

Multi-agent support lets you compose agents hierarchically — a manager agent can delegate subtasks to specialized worker agents, each with their own tools and capabilities. This pattern enables complex workflows while keeping individual agents focused and debuggable.

The framework is LLM-agnostic, supporting any model through a simple interface. It works with OpenAI, Anthropic, local Hugging Face models, and any provider via LiteLLM. For Hugging Face's own models, there's seamless integration with the Inference API and local transformers models.

smolagents integrates with Hugging Face's broader ecosystem including Gradio for building agent UIs, the Hub for sharing tools and agents, and Spaces for deployment. The framework's transparency — readable traces, inspectable code generation, and clear execution logs — makes it excellent for learning, teaching, and research.

For developers who find LangChain too heavy and want a framework they can fully understand and customize, smolagents offers the ideal balance of simplicity and capability. It's particularly popular in the research community and among developers who value code readability over framework magic.