<!---
Copyright 2025 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->

# Contribute to smolagents

Everyone is welcome to contribute, and we value everybody's contribution. Code
contributions are not the only way to help the community. Answering questions, helping
others, and improving the documentation are also immensely valuable.

It also helps us if you spread the word! Reference the library in blog posts
about the awesome projects it made possible, shout out on Twitter every time it has
helped you, or simply ⭐ the repository to say thank you.

However you choose to contribute, please be mindful and respect our
[code of conduct](https://github.com/huggingface/smolagents/blob/main/CODE_OF_CONDUCT.md).

**This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).**

## Ways to contribute

There are several ways you can contribute to smolagents.

* Submit issues related to bugs or desired new features.
* Contribute to the examples or to the documentation.
* Fix outstanding issues with the existing code.

> All contributions are equally valuable to the community. 🥰

## Submitting a bug-related issue or feature request

At any moment, feel welcome to open an issue, citing your exact error traces and package versions if it's a bug.
It's often even better to open a PR with your proposed fixes/changes!

Do your best to follow these guidelines when submitting a bug-related issue or a feature
request. It will make it easier for us to come back to you quickly and with good
feedback.

### Did you find a bug?

The smolagents library is robust and reliable thanks to users who report the problems they encounter.

Before you report an issue, we would really appreciate it if you could **make sure the bug was not
already reported** (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the
library itself, and not your code.

Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so
we can quickly resolve it:

* Your **OS type and version**, as well as your environment versions (versions of rust, python, and dependencies).
* A short, self-contained, code snippet that allows us to reproduce the bug.
* The *full* traceback if an exception is raised.
* Attach any other additional information, like screenshots, you think may help.

### Do you want a new feature?

If there is a new feature you'd like to see in smolagents, please open an issue and describe:

1. What is the *motivation* behind this feature? Is it related to a problem or frustration with the library? Is it
   a feature related to something you need for a project? Is it something you worked on and think it could benefit
   the community?

   Whatever it is, we'd love to hear about it!

2. Describe your requested feature in as much detail as possible. The more you can tell us about it, the better
   we'll be able to help you.
3. Provide a *code snippet* that demonstrates the feature's usage.
4. If the feature is related to a paper, please include a link.

If your issue is well written we're already 80% of the way there by the time you create it.

## Do you want to add documentation?

We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know
how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be
happy to make the changes or help you make a contribution if you're interested!

## Fixing outstanding issues

If you notice an issue with the existing code and have a fix in mind, feel free to [start contributing](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request) and open
a Pull Request!

### Making code changes

To install dev dependencies, run:
```
pip install -e ".[dev]"
```

When making changes to the codebase, please check that it follows the repo's code quality requirements by running:
To check code quality of the source code:
```
make quality
```

If the checks fail, you can run the formatter with:
```
make style
```

And commit the changes.

To run tests locally, run this command:
```bash
make test
```
</details>

## I want to become a maintainer of the project. How do I get there?

smolagents is a project led and managed by Hugging Face. We are more than
happy to have motivated individuals from other organizations join us as maintainers with the goal of helping smolagents
make a dent in the world of Agents.

If you are such an individual (or organization), please reach out to us and let's collaborate.

CONTRIBUTING.md


A smol library to build great agents!

`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/).

🤗 **Hub integrations**: you can [share/pull tools to/from the Hub](https://huggingface.co/docs/smolagents/reference/tools#smolagents.Tool.from_hub), and more is to come!

🌐 **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), [Anthropic's 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, HfApiModel

model = HfApiModel()
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 hub:
```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.

 HfApiModel, gateway for 4 inference providers

```py
from smolagents import HfApiModel

model = HfApiModel(
    model_id="deepseek-ai/DeepSeek-R1",
    provider="together",
)
```

 LiteLLM to access 100+ LLMs

```py
from smolagents import LiteLLMModel

model = LiteLLMModel(
    "anthropic/claude-3-5-sonnet-latest",
    temperature=0.2,
    api_key=os.environ["ANTHROPIC_API_KEY"]
)
```

 OpenAI-compatible servers

```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.
)
```

 Local `transformers` model

```py
from smolagents import TransformersModel

model = TransformersModel(
    model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
    max_new_tokens=4096,
    device_map="auto"
)
```

 Azure models

```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")
)
```

## 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 "HfApiModel" --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/) (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/benchmark.ipynb) 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).

This comparison shows that open-source models can now take on the best closed models!

## 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}
}
```

README.md

🤗 smolagents: a barebones library for agents. Agents write python code to call tools and orchestrate other agents.


<!---
Copyright 2024 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->

# Generating the documentation

To generate the documentation, you have to build it. Several packages are necessary to build the doc.

First, you need to install the project itself by running the following command at the root of the code repository:

```bash
pip install -e .
```

You also need to install 2 extra packages:

```bash
# `hf-doc-builder` to build the docs
pip install git+https://github.com/huggingface/doc-builder@main
# `watchdog` for live reloads
pip install watchdog
```

---
**NOTE**

You only need to generate the documentation to inspect it locally (if you're planning changes and want to
check how they look before committing for instance). You don't have to commit the built documentation.

---

## Building the documentation

Once you have setup the `doc-builder` and additional packages with the pip install command above,
you can generate the documentation by typing the following command:

```bash
doc-builder build smolagents docs/source/en/ --build_dir ~/tmp/test-build
```

You can adapt the `--build_dir` to set any temporary folder that you prefer. This command will create it and generate
the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite
Markdown editor.

## Previewing the documentation

To preview the docs, run the following command:

```bash
doc-builder preview smolagents docs/source/en/
```

The docs will be viewable at [http://localhost:5173](http://localhost:5173). You can also preview the docs once you
have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.

---
**NOTE**

The `preview` command only works with existing doc files. When you add a completely new file, you need to update
`_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).

---

## Adding a new element to the navigation bar

Accepted files are Markdown (.md).

Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/smolagents/blob/main/docs/source/_toctree.yml) file.

## Renaming section headers and moving sections

It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.

Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.

So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:

```
Sections that were moved:

[ <a href="#section-b">Section A</a><a id="section-a"></a> ]
```
and of course, if you moved it to another file, then:

```
Sections that were moved:

[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
```

Use the relative style to link to the new file so that the versioned docs continue to work.

For an example of a rich moved section set please see the very end of [the transformers Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.md).

## Writing Documentation - Specification

The `huggingface/smolagents` documentation follows the
[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings,
although we can write them directly in Markdown.

### Adding a new tutorial

Adding a new tutorial or section is done in two steps:

- Add a new Markdown (.md) file under `./source`.
- Link that file in `./source/_toctree.yml` on the correct toc-tree.

Make sure to put your new file under the proper section. If you have a doubt, feel free to ask in a Github Issue or PR.

### Translating

When translating, refer to the guide at [./TRANSLATING.md](https://github.com/huggingface/smolagents/blob/main/docs/TRANSLATING.md).

### Writing source documentation

Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names
and objects like True, None, or any strings should usually be put in `code`.

When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool
adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or
function to be in the main package.

If you want to create a link to some internal class or function, you need to
provide its path. For instance: \[\`utils.ModelOutput\`\]. This will be converted into a link with
`utils.ModelOutput` in the description. To get rid of the path and only keep the name of the object you are
linking to in the description, add a ~: \[\`~utils.ModelOutput\`\] will generate a link with `ModelOutput` in the description.

The same works for methods so you can either use \[\`XXXClass.method\`\] or \[~\`XXXClass.method\`\].

#### Defining arguments in a method

Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and
an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its
description:

```
    Args:
        n_layers (`int`): The number of layers of the model.
```

If the description is too long to fit in one line, another indentation is necessary before writing the description
after the argument.

Here's an example showcasing everything so far:

```
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AlbertTokenizer`]. See [`~PreTrainedTokenizer.encode`] and
            [`~PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
```

For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the
following signature:

```
def my_function(x: str = None, a: float = 1):
```

then its documentation should look like this:

```
    Args:
        x (`str`, *optional*):
            This argument controls ...
        a (`float`, *optional*, defaults to 1):
            This argument is used to ...
```

Note that we always omit the "defaults to \`None\`" when None is the default for any argument. Also note that even
if the first line describing your argument type and its default gets long, you can't break it on several lines. You can
however write as many lines as you want in the indented description (see the example above with `input_ids`).

#### Writing a multi-line code block

Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:

````
```
# first line of code
# second line
# etc
```
````

#### Writing a return block

The return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation.
The first line should be the type of the return, followed by a line return. No need to indent further for the elements
building the return.

Here's an example of a single value return:

```
    Returns:
        `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
```

Here's an example of a tuple return, comprising several objects:

```
    Returns:
        `tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
        - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` --
          Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
        - **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
          Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
```

#### Adding an image

Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference
them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
to this dataset.

#### Writing documentation examples

The syntax for Example docstrings can look as follows:

```
    Example:

    ```python
    >>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
    >>> from datasets import load_dataset
    >>> import torch

    >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
    >>> dataset = dataset.sort("id")
    >>> sampling_rate = dataset.features["audio"].sampling_rate

    >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
    >>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

    >>> # audio file is decoded on the fly
    >>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
    >>> with torch.no_grad():
    ...     logits = model(**inputs).logits
    >>> predicted_ids = torch.argmax(logits, dim=-1)

    >>> # transcribe speech
    >>> transcription = processor.batch_decode(predicted_ids)
    >>> transcription[0]
    'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL'
    ```
```

The docstring should give a minimal, clear example of how the respective model
is to be used in inference and also include the expected (ideally sensible)
output.
Often, readers will try out the example before even going through the function
or class definitions. Therefore, it is of utmost importance that the example
works as expected.

docs/README.md


---
name: Bug report
about: The clearer your bug report, the faster it will be fixed!
title: "[BUG]"
labels: bug
assignees: ''

---

**Describe the bug**
A clear and concise description of what the bug is.

**Code to reproduce the error**
The simplest code snippet that produces your bug.

**Error logs (if any)**
Provide error logs if there are any.

**Expected behavior**
A clear and concise description of what you expected to happen.

**Packages version:**
Run `pip freeze | grep smolagents` and paste it here.

**Additional context**
Add any other context about the problem here.


---
name: Custom issue template
about: Describe this issue template's purpose here.
title: ''
labels: ''
assignees: ''

---


---
name: Feature request
about: Suggest an idea for this project
title: ''
labels: enhancement
assignees: ''

---

**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

**Describe the solution you'd like**
A clear and concise description of what you want to happen.

**Is this not possible with the current options.**
Make sure to consider if what you're requesting can be done with current abstractions.

**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.

**Additional context**
Add any other context or screenshots about the feature request here.


# Open Deep Research

Welcome to this open replication of [OpenAI's Deep Research](https://openai.com/index/introducing-deep-research/)!

Read more about this implementation's goal and methods [in our blog post](https://huggingface.co/blog/open-deep-research).

This agent achieves 55% pass@1 on GAIA validation set, vs 67% for Deep Research.

To install it, first run
```bash
pip install -r requirements.txt
```

And install smolagents dev version
```bash
pip install -e ../../.[dev]
```

Then you're good to go! Run the run.py script, as in:
```bash
python run.py --model-id "o1" "Your question here!"
```