838f266a8c
Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: Aymeric Roucher <69208727+aymeric-roucher@users.noreply.github.com>
67 lines
4.9 KiB
Plaintext
67 lines
4.9 KiB
Plaintext
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# Why use smolagents
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In this module, we will explore the pros and cons of using [smolagents](https://huggingface.co/docs/smolagents/en/index), helping you make an informed decision about whether it's the right framework for your needs.
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## What is `smolagents`?
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`smolagents` is a simple yet powerful framework for building AI agents. It provides LLMs with the _agency_ to interact with the real world, such as searching or generating images.
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As we learned in unit 1, AI agents are programs that use LLMs to generate **'thoughts'** based on **'observations'** to perform **'actions'**. Let's explore how this is implemented in smolagents.
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### Key Advantages of `smolagents`
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- **Simplicity:** Minimal code complexity and abstractions, to make the framework easy to understand, adopt and extend
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- **Flexible LLM Support:** Works with any LLM through integration with Hugging Face tools and external APIs
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- **Code-First Approach:** First-class support for Code Agents that write their actions directly in code, removing the need for parsing and simplifying tool calling
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- **HF Hub Integration:** Seamless integration with the Hugging Face Hub, allowing the use of Gradio Spaces as tools
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### When to use smolagents?
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With these advantages in mind, when should we use smolagents over other frameworks?
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smolagents is ideal when:
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- You need a **lightweight and minimal solution.**
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- You want to **experiment quickly** without complex configurations.
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- Your **application logic is straightforward.**
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### Code vs. JSON Actions
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Unlike other frameworks where agents write actions in JSON, `smolagents` **focuses on tool calls in code**, simplifying the execution process. This is because there's no need to parse the JSON in order to build code that calls the tools: the output can be executed directly.
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The following diagram illustrates this difference:
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To review the difference between Code vs JSON Actions, you can revisit [the Actions Section in Unit 1](https://huggingface.co/learn/agents-course/unit1/actions#actions-enabling-the-agent-to-engage-with-its-environment).
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### Agent Types in `smolagents`
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Agents in `smolagents` operate as **multi-step agents**.
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Each [`MultiStepAgent`](https://huggingface.co/docs/smolagents/main/en/reference/agents#smolagents.MultiStepAgent) performs:
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- One thought
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- One tool call and execution
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In addition to using **[CodeAgent](https://huggingface.co/docs/smolagents/main/en/reference/agents#smolagents.CodeAgent)** as the primary type of agent, smolagents also supports **[ToolCallingAgent](https://huggingface.co/docs/smolagents/main/en/reference/agents#smolagents.ToolCallingAgent)**, which writes tool calls in JSON.
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We will explore each agent type in more detail in the following sections.
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<Tip>
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In smolagents, tools are defined using <code>@tool</code> decorator wrapping a python function or the <code>Tool</code> class.
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</Tip>
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### Model Integration in `smolagents`
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`smolagents` supports flexible LLM integration, allowing you to use any callable model that meets [certain criteria](https://huggingface.co/docs/smolagents/main/en/reference/models). The framework provides several predefined classes to simplify model connections:
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- **[TransformersModel](https://huggingface.co/docs/smolagents/main/en/reference/models#smolagents.TransformersModel):** Implements a local `transformers` pipeline for seamless integration.
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- **[HfApiModel](https://huggingface.co/docs/smolagents/main/en/reference/models#smolagents.HfApiModel):** Supports [serverless inference](https://huggingface.co/docs/huggingface_hub/main/en/guides/inference) calls through [Hugging Face's infrastructure](https://huggingface.co/docs/api-inference/index), or via a growing number of [third-party inference providers](https://huggingface.co/docs/huggingface_hub/main/en/guides/inference#supported-providers-and-tasks).
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- **[LiteLLMModel](https://huggingface.co/docs/smolagents/main/en/reference/models#smolagents.LiteLLMModel):** Leverages [LiteLLM](https://www.litellm.ai/) for lightweight model interactions.
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- **[OpenAIServerModel](https://huggingface.co/docs/smolagents/main/en/reference/models#smolagents.OpenAIServerModel):** Connects to any service that offers an OpenAI API interface.
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- **[AzureOpenAIServerModel](https://huggingface.co/docs/smolagents/main/en/reference/models#smolagents.AzureOpenAIServerModel):** Supports integration with any Azure OpenAI deployment.
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This flexibility ensures that developers can choose the model and service most suitable for their specific use cases, and allows for easy experimentation.
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Now that we understood why and when to use smolagents, let's dive deeper into this powerful library!
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## Resources
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- [smolagents Blog](https://huggingface.co/blog/smolagents) - Introduction to smolagents and code interactions |