It is a barebones library called **smolagents** designed to enable the creation and orchestration of agents that write Python code to call tools and manage other agents.
It is a barebones library called **smolagents** designed to enable the creation and orchestration of agents that write Python code to call tools and manage other agents. The library emphasizes simplicity, with its core logic fitting into approximately 1,000 lines of code, and provides minimal abstractions to keep the system lightweight and efficient.
**smolagents** offers first-class support for **Code Agents**, which write their actions as Python code snippets rather than using traditional methods like JSON or text blobs. This approach has been shown to reduce the number of steps required by 30%, leading to fewer LLM calls and improved performance on complex benchmarks. To ensure security, the library supports executing code in sandboxed environments via **E2B**.
The library is **model-agnostic**, meaning it can work with any large language model (LLM), including local models like **transformers** or **ollama**, models from providers on the **Hugging Face Hub**, or models from **OpenAI**, **Anthropic**, and others via **LiteLLM** integration. It is also **modality-agnostic**, supporting text, vision, video, and audio inputs, and **tool-agnostic**, allowing the use of tools from **LangChain**, **Anthropic’s MCP**, or even a **Hub Space** as a tool.
**smolagents** includes integrations with the **Hugging Face Hub**, enabling users to share and pull tools. It also provides a **CLI** with commands like `smolagent` for running multi-step Code Agents and `webagent` for web-browsing tasks. The library supports both **CodeAgent** and the more traditional **ToolCallingAgent**, though the former is recommended for its efficiency.
The library is designed to handle complex tasks, such as maintaining consistent code formats across system prompts, parsers, and execution, while encouraging users to customize and use only the components they need. Benchmarks show that open-source models using **smolagents** can compete with closed models in agentic workflows. Contributions are welcome, and users are encouraged to cite the library in publications using the provided BibTeX entry.
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