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base on 🤗 smolagents: a barebones library for agents that think in python code.  <p align="center">  <a href="https://github.com/huggingface/smolagents/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/huggingface/smolagents.svg?color=blue"></a> <a href="https://huggingface.co/docs/smolagents"><img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/smolagents/index.html.svg?down_color=red&down_message=offline&up_message=online"></a> <a href="https://github.com/huggingface/smolagents/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/smolagents.svg"></a> <a href="https://github.com/huggingface/smolagents/blob/main/CODE_OF_CONDUCT.md"><img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg"></a> </p> <h3 align="center"> <div style="display:flex;flex-direction:row;"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolagents/smolagents.png" alt="Hugging Face mascot as James Bond" width=400px> <p>Agents that think in code!</p> </div> </h3> `smolagents` is a library that enables you to run powerful agents in a few lines of code. It offers: ✨ **Simplicity**: the logic for agents fits in ~1,000 lines of code (see [agents.py](https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py)). We kept abstractions to their minimal shape above raw code! 🧑‍💻 **First-class support for Code Agents**. Our [`CodeAgent`](https://huggingface.co/docs/smolagents/reference/agents#smolagents.CodeAgent) writes its actions in code (as opposed to "agents being used to write code"). To make it secure, we support executing in sandboxed environments via [E2B](https://e2b.dev/) or via Docker. 🤗 **Hub integrations**: you can [share/pull tools or agents to/from the Hub](https://huggingface.co/docs/smolagents/reference/tools#smolagents.Tool.from_hub) for instant sharing of the most efficient agents! 🌐 **Model-agnostic**: smolagents supports any LLM. It can be a local `transformers` or `ollama` model, one of [many providers on the Hub](https://huggingface.co/blog/inference-providers), or any model from OpenAI, Anthropic and many others via our [LiteLLM](https://www.litellm.ai/) integration. 👁️ **Modality-agnostic**: Agents support text, vision, video, even audio inputs! Cf [this tutorial](https://huggingface.co/docs/smolagents/examples/web_browser) for vision. 🛠️ **Tool-agnostic**: you can use tools from [LangChain](https://huggingface.co/docs/smolagents/reference/tools#smolagents.Tool.from_langchain), [MCP](https://huggingface.co/docs/smolagents/reference/tools#smolagents.ToolCollection.from_mcp), you can even use a [Hub Space](https://huggingface.co/docs/smolagents/reference/tools#smolagents.Tool.from_space) as a tool. Full documentation can be found [here](https://huggingface.co/docs/smolagents/index). > [!NOTE] > Check the our [launch blog post](https://huggingface.co/blog/smolagents) to learn more about `smolagents`! ## Quick demo First install the package. ```bash pip install smolagents ``` Then define your agent, give it the tools it needs and run it! ```py from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel model = InferenceClientModel() agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model) agent.run("How many seconds would it take for a leopard at full speed to run through Pont des Arts?") ``` https://github.com/user-attachments/assets/cd0226e2-7479-4102-aea0-57c22ca47884 You can even share your agent to the Hub, as a Space repository: ```py agent.push_to_hub("m-ric/my_agent") # agent.from_hub("m-ric/my_agent") to load an agent from Hub ``` Our library is LLM-agnostic: you could switch the example above to any inference provider. <details> <summary> <b>InferenceClientModel, gateway for all <a href="https://huggingface.co/docs/inference-providers/index">inference providers</a> supported on HF</b></summary> ```py from smolagents import InferenceClientModel model = InferenceClientModel( model_id="deepseek-ai/DeepSeek-R1", provider="together", ) ``` </details> <details> <summary> <b>LiteLLM to access 100+ LLMs</b></summary> ```py from smolagents import LiteLLMModel model = LiteLLMModel( model_id="anthropic/claude-3-5-sonnet-latest", temperature=0.2, api_key=os.environ["ANTHROPIC_API_KEY"] ) ``` </details> <details> <summary> <b>OpenAI-compatible servers</b></summary> ```py import os from smolagents import OpenAIServerModel model = OpenAIServerModel( model_id="deepseek-ai/DeepSeek-R1", api_base="https://api.together.xyz/v1/", # Leave this blank to query OpenAI servers. api_key=os.environ["TOGETHER_API_KEY"], # Switch to the API key for the server you're targeting. ) ``` </details> <details> <summary> <b>Local `transformers` model</b></summary> ```py from smolagents import TransformersModel model = TransformersModel( model_id="Qwen/Qwen2.5-Coder-32B-Instruct", max_new_tokens=4096, device_map="auto" ) ``` </details> <details> <summary> <b>Azure models</b></summary> ```py import os from smolagents import AzureOpenAIServerModel model = AzureOpenAIServerModel( model_id = os.environ.get("AZURE_OPENAI_MODEL"), azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"), api_key=os.environ.get("AZURE_OPENAI_API_KEY"), api_version=os.environ.get("OPENAI_API_VERSION") ) ``` </details> <details> <summary> <b>Amazon Bedrock models</b></summary> ```py import os from smolagents import AmazonBedrockServerModel model = AmazonBedrockServerModel( model_id = os.environ.get("AMAZON_BEDROCK_MODEL_ID") ) ``` </details> ## CLI You can run agents from CLI using two commands: `smolagent` and `webagent`. `smolagent` is a generalist command to run a multi-step `CodeAgent` that can be equipped with various tools. ```bash smolagent "Plan a trip to Tokyo, Kyoto and Osaka between Mar 28 and Apr 7." --model-type "InferenceClientModel" --model-id "Qwen/Qwen2.5-Coder-32B-Instruct" --imports "pandas numpy" --tools "web_search" ``` Meanwhile `webagent` is a specific web-browsing agent using [helium](https://github.com/mherrmann/helium) (read more [here](https://github.com/huggingface/smolagents/blob/main/src/smolagents/vision_web_browser.py)). For instance: ```bash webagent "go to xyz.com/men, get to sale section, click the first clothing item you see. Get the product details, and the price, return them. note that I'm shopping from France" --model-type "LiteLLMModel" --model-id "gpt-4o" ``` ## How do Code agents work? Our [`CodeAgent`](https://huggingface.co/docs/smolagents/reference/agents#smolagents.CodeAgent) works mostly like classical ReAct agents - the exception being that the LLM engine writes its actions as Python code snippets. ```mermaid flowchart TB Task[User Task] Memory[agent.memory] Generate[Generate from agent.model] Execute[Execute Code action - Tool calls are written as functions] Answer[Return the argument given to 'final_answer'] Task -->|Add task to agent.memory| Memory subgraph ReAct[ReAct loop] Memory -->|Memory as chat messages| Generate Generate -->|Parse output to extract code action| Execute Execute -->|No call to 'final_answer' tool => Store execution logs in memory and keep running| Memory end Execute -->|Call to 'final_answer' tool| Answer %% Styling classDef default fill:#d4b702,stroke:#8b7701,color:#ffffff classDef io fill:#4a5568,stroke:#2d3748,color:#ffffff class Task,Answer io ``` Actions are now Python code snippets. Hence, tool calls will be performed as Python function calls. For instance, here is how the agent can perform web search over several websites in one single action: ```py requests_to_search = ["gulf of mexico america", "greenland denmark", "tariffs"] for request in requests_to_search: print(f"Here are the search results for {request}:", web_search(request)) ``` Writing actions as code snippets is demonstrated to work better than the current industry practice of letting the LLM output a dictionary of the tools it wants to call: [uses 30% fewer steps](https://huggingface.co/papers/2402.01030) (thus 30% fewer LLM calls) and [reaches higher performance on difficult benchmarks](https://huggingface.co/papers/2411.01747). Head to [our high-level intro to agents](https://huggingface.co/docs/smolagents/conceptual_guides/intro_agents) to learn more on that. Especially, since code execution can be a security concern (arbitrary code execution!), we provide options at runtime: - a secure python interpreter to run code more safely in your environment (more secure than raw code execution but still risky) - a sandboxed environment using [E2B](https://e2b.dev/) or Docker (removes the risk to your own system). On top of this [`CodeAgent`](https://huggingface.co/docs/smolagents/reference/agents#smolagents.CodeAgent) class, we still support the standard [`ToolCallingAgent`](https://huggingface.co/docs/smolagents/reference/agents#smolagents.ToolCallingAgent) that writes actions as JSON/text blobs. But we recommend always using `CodeAgent`. ## How smol is this library? We strived to keep abstractions to a strict minimum: the main code in `agents.py` has <1,000 lines of code. Still, we implement several types of agents: `CodeAgent` writes its actions as Python code snippets, and the more classic `ToolCallingAgent` leverages built-in tool calling methods. We also have multi-agent hierarchies, import from tool collections, remote code execution, vision models... By the way, why use a framework at all? Well, because a big part of this stuff is non-trivial. For instance, the code agent has to keep a consistent format for code throughout its system prompt, its parser, the execution. So our framework handles this complexity for you. But of course we still encourage you to hack into the source code and use only the bits that you need, to the exclusion of everything else! ## How strong are open models for agentic workflows? We've created [`CodeAgent`](https://huggingface.co/docs/smolagents/reference/agents#smolagents.CodeAgent) instances with some leading models, and compared them on [this benchmark](https://huggingface.co/datasets/m-ric/agents_medium_benchmark_2) that gathers questions from a few different benchmarks to propose a varied blend of challenges. [Find the benchmarking code here](https://github.com/huggingface/smolagents/blob/main/examples/smolagents_benchmark/run.py) for more detail on the agentic setup used, and see a comparison of using LLMs code agents compared to vanilla (spoilers: code agents works better). <p align="center"> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolagents/benchmark_code_agents.jpeg" alt="benchmark of different models on agentic workflows. Open model DeepSeek-R1 beats closed-source models." width=60% max-width=500px> </p> This comparison shows that open-source models can now take on the best closed models! ## Security Security is a critical consideration when working with code-executing agents. Our library provides: - Sandboxed execution options using [E2B](https://e2b.dev/) or Docker - Best practices for running agent code securely For security policies, vulnerability reporting, and more information on secure agent execution, please see our [Security Policy](SECURITY.md). ## Contribute Everyone is welcome to contribute, get started with our [contribution guide](https://github.com/huggingface/smolagents/blob/main/CONTRIBUTING.md). ## Cite smolagents If you use `smolagents` in your publication, please cite it by using the following BibTeX entry. ```bibtex @Misc{smolagents, title = {`smolagents`: a smol library to build great agentic systems.}, author = {Aymeric Roucher and Albert Villanova del Moral and Thomas Wolf and Leandro von Werra and Erik Kaunismäki}, howpublished = {\url{https://github.com/huggingface/smolagents}}, year = {2025} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"13435","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"

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