smolagents
Lightweight agent library built around code-writing agents
smolagents is profiled here as a LLM tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
smolagents is an open-source agent library from Hugging Face that keeps the core small while supporting agents that act by writing and running code. Its CodeAgent has the model express tool calls as Python snippets, which often expresses multi-step plans more compactly than chains of structured JSON calls. The library is model-agnostic, working with models from the Hugging Face Hub or any provider, and it ships with sandboxed execution and a set of ready tools so a working agent comes together in a few lines.
Key Capabilities:
CodeAgent that performs actions by writing and executing Python
A minimal core that keeps the agent loop small and readable
Model-agnostic support for Hub models and external provider APIs
Sandboxed execution for running generated code safely
Built-in tools for web search, page reading, and Python execution
Integration with the Hugging Face Hub for sharing agents and tools
Alternative tools
- Amazon Redshift
Cloud data warehouse for large-scale analytics on AWS
- Agno
High-performance framework for building and running agents
- GraphRAG
Graph-based retrieval-augmented generation from Microsoft Research
- Reka
Multimodal models that reason across text, image, and video
- LLM Guard
Open-source security toolkit for LLM interactions
- Llama Guard
Open safeguard model for classifying LLM inputs and outputs