Smolagents

A compact codebase with minimal abstractions simplifies development of robust AI agents.

Developers can swiftly define agents, provide tools, and run them immediately without intricate configurations.

Agents are designed to write and execute Python code snippets directly to perform actions.

Code agents offer enhanced efficiency and accuracy, reducing steps and LLM calls by about 30%.

Integrates effortlessly with any large language model, including those from Hugging Face, OpenAI, and Anthropic.

Supports running code in sandboxed environments like E2B for protected and isolated execution.

Allows easy sharing and loading of tools, fostering collaboration and ecosystem growth within the community.

Offers flexibility by also supporting conventional tool-calling agents where actions are defined as JSON or text.

Code naturally supports function nesting and reuse, allowing for the expression of complex logic in agent workflows.

Code can represent any operation a computer can perform, providing immense versatility for agents.

Simplifies the management and transfer of objects within agent workflows compared to JSON.

Matches the performance of proprietary models using efficient open-source code agents in various benchmarks.