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Smolagents is a minimalist AI agent framework developed by the Hugging Face team designed to enable developers to create powerful AI agents with minimal code. It focuses on leveraging large language models (LLMs) to interact with the real world by executing Python code snippets, rather than generating actions as JSON or text. This approach enhances efficiency and accuracy, reducing the number of steps and LLM calls needed. Smolagents supports secure code execution in sandboxed environments and integrates seamlessly with various LLMs, including those hosted on the Hugging Face Hub, OpenAI, and Anthropic. Its compact codebase and user-friendly design make it suitable for developers looking to build robust, flexible AI agents quickly. The framework also supports traditional tool-calling agents and encourages community collaboration through deep integration with the Hugging Face Hub.
4.7/5
Efficient, accurate, and flexible
Minimalist and developer-friendly
To enable developers to build and deploy efficient AI agents that execute code-driven workflows with minimal effort.
Compatible with multiple LLMs including Hugging Face models, OpenAI, and Anthropic, and integrates with sandboxed execution environments for secure code running.
Open-source and free to use
Features with the highest value for users are highlighted here.
Minimalist codebase
Code agent execution
Sandboxed secure execution
Integration with multiple LLMs
Deep Hugging Face Hub integration
Support for traditional tool-calling agents
Efficient multi-step workflows
Easy tool sharing and importing
AI Developers
Data Scientists
Machine Learning Engineers
Hugging Face Community
Startups
Educational Institutions
Open Source Contributors
Research Labs
Software Engineers
Technical Enthusiasts
Open Source
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