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smolagents

smolagents

AI Agent development framework that executes Python code snippets and integrates multilingual models

Agent DevelopmentDeveloper ToolsLarge Language Models (LLMs)
smolagents screenshot
OverviewAlternatives

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What is smolagents?

smolagents is an open-source, lightweight AI agent framework developed by Hugging Face that simplifies the creation of efficient AI agents. It emphasizes code agents—those that directly write and execute Python code snippets—offering higher efficiency, accuracy, and flexibility compared to traditional JSON or text-based agents. With a core codebase of approximately 1,000 lines, it supports all major language models including those from Hugging Face, OpenAI, and Anthropic, integrated via LiteLLM. Deeply integrated with the Hugging Face Hub, it enables easy sharing and loading of tools and agents, fostering a collaborative ecosystem. Secure execution is ensured through sandboxed environments like E2B, making smolagents a versatile, future-ready solution for AI agent development across diverse scenarios.

Key Features of smolagents?

Minimalist and Efficient Design
Streamlined codebase (approximately 1,000 lines) with minimal abstraction layers, enabling rapid and easily understandable Agent development.
Direct Code Agent Execution
Agents can directly generate and run Python code snippets, reducing steps and LLM calls by about 30%, improving performance and handling complex logic.
Secure Sandbox Execution
Supports code execution in isolated environments such as E2B, ensuring safe and controlled Agent operations.
Broad LLM Compatibility
Compatible with any large language model, including Hugging Face Hub, OpenAI, Anthropic, etc., via LiteLLM integration.
Deep Hugging Face Hub Integration
Enables sharing and loading tools and Agents from the Hub, promoting community collaboration and ecosystem growth.
Support for Traditional Tool-Calling Agents
In addition to code agents, supports agents that generate JSON or text actions, suitable for diverse scenarios.

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