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Smolagents.org
Overview :
Smolagents is a minimalist AI agent framework developed by the Hugging Face team, designed to allow developers to deploy powerful agents with minimal code. It focuses on code agents, which execute tasks by writing and executing Python code snippets, rather than generating JSON or text blocks. This approach leverages the capabilities of large language models (LLMs) to generate and understand code, providing better composability, flexibility, and rich utilization of training data to efficiently handle complex logic and object management. Smolagents integrates deeply with Hugging Face Hub for easy sharing and loading of tools, promoting community collaboration. Additionally, it supports traditional tool-calling agents and is compatible with various LLMs, including models from Hugging Face Hub and those integrated through LiteLLM from OpenAI, Anthropic, and others. The introduction of Smolagents lowers the barrier to developing AI agents, enabling developers to more easily build and deploy AI-driven applications.
Target Users :
The target audience is developers who want to quickly build and deploy AI-driven applications. With its minimalist design and compatibility with various LLMs, Smolagents is the ideal choice for developers at different skill levels to rapidly get started and implement AI capabilities. Whether you are a beginner or an experienced developer, Smolagents enables you to easily create powerful AI agents to tackle various programming and automation tasks.
Use Cases
Create a travel planning agent that calls the Google Maps API to get travel times and plans a day-long bike tour of Paris.
Build a text-to-SQL agent that automatically generates and tests SQL queries, helping developers quickly implement data query functionalities.
Develop an infographic generator that uses AI to create visual data charts, enhancing data presentation.
Features
Minimal codebase: Core code is around 1000 lines, reducing abstraction layers and simplifying the development process.
User-friendly: Developers can quickly define agents, provide tools, and run them immediately without complex configuration.
Code agents: Focus on code agents that execute tasks by running Python code snippets, enhancing efficiency and accuracy.
Efficient execution: Compared to standard tool-calling methods, code agents reduce about 30% of steps and LLM calls, performing better in complex benchmarks.
Safe execution: Supports running code in a sandbox environment (e.g., E2B) to ensure safe code execution.
Multi-LLM compatibility: Easily integrate models from Hugging Face Hub and other models from OpenAI, Anthropic, and more.
How to Use
1. Install Smolagents: Use the pip command `pip install smolagents` to install.
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2. Import the required classes: Import CodeAgent, the necessary tool classes, and LLM model classes from the Smolagents library.
3. Define the agent: Create an instance of CodeAgent, passing in the required list of tools and LLM model.
4. Write tool functions: Depending on task requirements, write tool functions in Python code to implement specific functionalities.
5. Run the agent: Call the agent's run method, passing in the task description; the agent will automatically execute the task and return the results.
6. Share tools: Share your custom tool functions with the community using the `push_to_hub` method on Hugging Face Hub.
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