smolagents is a library that enables you to run powerful agents in a few lines of code. It offers:
✨ Simplicity: the logic for agents fits in ~1,000 lines of code (see agents.py). We kept abstractions to their minimal shape above raw code!
🧑💻 First-class support for Code Agents. Our CodeAgent writes its actions in code (as opposed to "agents being used to write code"). To make it secure, we support executing in sandboxed environments via E2B or via Docker.
🤗 Hub integrations: you can share/pull tools or agents to/from the Hub for instant sharing of the most efficient agents!
🌐 Model-agnostic: smolagents supports any LLM. It can be a local transformers or ollama model, one of many providers on the Hub, or any model from OpenAI, Anthropic and many others via our LiteLLM integration.
👁️ Modality-agnostic: Agents support text, vision, video, even audio inputs! Cf this tutorial for vision.
🛠️ Tool-agnostic: you can use tools from any MCP server, from LangChain, you can even use a Hub Space as a tool.
Full documentation can be found here.
Note
Check the our launch blog post to learn more about smolagents!
MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.
Why MCP?
MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:
- A growing list of pre-built integrations that your LLM can directly plug into
- The flexibility to switch between LLM providers and vendors
- Best practices for securing your data within your infrastructure
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