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Smolagents is a minimalist AI agent framework developed by the Hugging Face team, designed to enable developers to deploy robust agents with just a few lines of code. It focuses on simplicity and efficiency, allowing large language models (LLMs) to interact seamlessly with the real world.

Unlike many AI agent frameworks that have complex abstractions and configurations, smolagents maintains a compact codebase of about 1,000 lines. It emphasizes ease of use and supports code agents, where agents execute Python code snippets directly, enhancing efficiency and reducing the number of required steps and LLM calls.

Code agents are a key feature of smolagents. Instead of generating actions as JSON or text blobs, code agents write and execute Python code snippets to perform tasks. This approach leverages the LLM’s ability to generate and understand code, offering better composability and flexibility in expressing complex logic.

You can install smolagents using pip with the command: `pip install smolagents`. To get started, define an agent by importing the necessary classes, provide the required tools and LLM model, and run your tasks. For example: `from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel; agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel()); agent.run('Your task here')`.

Smolagents is designed for broad compatibility with LLMs. It integrates seamlessly with models hosted on the Hugging Face Hub via Transformers, as well as with models from OpenAI, Anthropic, and others through LiteLLM integration. This flexibility allows you to choose the most suitable LLM for your project needs.

Yes, in addition to code agents, smolagents supports traditional tool-calling agents. In this mode, actions are written as JSON or text blobs, which can be suitable for specific scenarios where executing code snippets may not be ideal.

Smolagents prioritizes secure code execution by supporting sandboxed environments. For instance, it can utilize E2B (Execution to Binary), which provides a secure and isolated environment for running code snippets. This ensures that code execution does not compromise the host system’s security.

Code agents offer several benefits: better composability, efficient object management, high flexibility, and rich training data utilization. Code naturally allows for function nesting and reuse, making it easier to express complex logic. Managing and passing objects is more straightforward in code compared to JSON. Code can represent any computational operation, providing immense versatility. Since LLMs are trained on large amounts of code, they are proficient at generating and understanding code snippets.

Smolagents has deep integration with the Hugging Face Hub, enabling developers to easily share and load tools. This fosters a collaborative ecosystem where tools and resources can be contributed and accessed by the community, enhancing the functionality and reach of smolagents.

You can explore the following resources to learn more about smolagents: official documentation on the Hugging Face website, getting started guides, example projects on GitHub, and community forums for insights and support.