docs/docs/features/plugin/tools/index.mdx

---
sidebar_position: 2
title: "⚙️ Tools"
---

## What are Tools?
Tools are python scripts that are provided to an LLM at the time of the request. Tools allow LLMs to perform actions and receive additional context as a result. Generally speaking, your LLM of choice will need to support function calling for tools to be reliably utilized.

Tools enable many use cases for chats, including web search, web scraping, and API interactions within the chat.

Many Tools are available to use on the [Community Website](https://openwebui.com/tools) and can easily be imported into your Open WebUI instance.

## How can I use Tools?
[Once installed](#how-to-install-tools), Tools can be used by assigning them to any LLM that supports function calling and then enabling that Tool. To assign a Tool to a model, you need to navigate to Workspace => Models. Here you can select the model for which you’d like to enable any Tools.

Once you click the pencil icon to edit the model settings, scroll down to the Tools section and check any Tools you wish to enable. Once done you must click save.

Now that Tools are enabled for the model, you can click the “+” icon when chatting with an LLM to use various Tools. Please keep in mind that enabling a Tool does not force it to be used. It means the LLM will be provided the option to call this Tool.

Lastly, we do provide a filter function on the community site that allows LLMs to autoselect Tools without you needing to enable them in the “+” icon menu: https://openwebui.com/f/hub/autotool_filter/

Please note: when using the AutoTool Filter, you will still need to take the steps above to enable the Tools per model.

## How to install Tools
The Tools import process is quite simple. You will have two options:

### Download and import manually
Navigate to the community site: https://openwebui.com/tools/
1) Click on the Tool you wish to import
2) Click the blue “Get” button in the top right-hand corner of the page
3) Click “Download as JSON export”
4) You can now upload the Tool into Open WebUI by navigating to Workspace => Tools and clicking “Import Tools”

### Import via your Open WebUI URL
1) Navigate to the community site: https://openwebui.com/tools/
2) Click on the Tool you wish to import
3) Click the blue “Get” button in the top right-hand corner of the page
4) Enter the IP address of your Open WebUI instance and click “Import to WebUI” which will automatically open your instance and allow you to import the Tool.

Note: You can install your own Tools and other Tools not tracked on the community site using the manual import method. Please do not import Tools you do not understand or are not from a trustworthy source. Running unknown code is ALWAYS a risk.

## What sorts of things can Tools do?
Tools enable diverse use cases for interactive conversations by providing a wide range of functionality such as:

- [**Web Search**](https://openwebui.com/t/constliakos/web_search/): Perform live web searches to fetch real-time information.
- [**Image Generation**](https://openwebui.com/t/justinrahb/image_gen/): Generate images based on the user prompt
- [**External Voice Synthesis**](https://openwebui.com/t/justinrahb/elevenlabs_tts/): Make API requests within the chat to integrate external voice synthesis service ElevenLabs and generate audio based on the LLM output.

## Writing A Custom Toolkit

Toolkits are defined in a single Python file, with a top level docstring with metadata and a `Tools` class.

### Example Top-Level Docstring

```python
"""
title: String Inverse
author: Your Name
author_url: https://website.com
git_url: https://github.com/username/string-reverse.git
description: This tool calculates the inverse of a string
required_open_webui_version: 0.4.0
requirements: langchain-openai, langgraph, ollama, langchain_ollama
version: 0.4.0
licence: MIT
"""
```

### Tools Class

Tools have to be defined as methods withing a class called `Tools`, with optional subclasses called `Valves` and `UserValves`, for example:

```python
class Tools:
    def __init__(self):
        """Initialize the Tool."""
        self.valves = self.Valves()

    class Valves(BaseModel):
        api_key: str = Field("", description="Your API key here")

    def reverse_string(self, string: str) -> str:
        """
        Reverses the input string.
        :param string: The string to reverse
        """
        # example usage of valves
        if self.valves.api_key != "42":
            return "Wrong API key"
        return string[::-1] 
```

### Type Hints
Each tool must have type hints for arguments. As of version OpenWebUI version 0.4.3, the types may also be nested, such as `queries_and_docs: list[tuple[str, int]]`. Those type hints are used to generate the JSON schema that is sent to the model. Tools without type hints will work with a lot less consistency.

### Valves and UserValves - (optional, but HIGHLY encouraged)

Valves and UserValves are used to allow users to provide dynamic details such as an API key or a configuration option. These will create a fillable field or a bool switch in the GUI menu for the given function.

Valves are configurable by admins alone via the Tools or Functions menus. On the other hand UserValves are configurable by any users directly from a chat session.

<details>
<summary>Commented example</summary>

```
from pydantic import BaseModel, Field

# Define and Valves
class Tools:
    # Notice the current indentation: Valves and UserValves must be declared as
    # attributes of a Tools, Filter or Pipe class. Here we take the
    # example of a Tool.
    class Valves(BaseModel):
        # Valves and UserValves inherit from pydantic's BaseModel. This
        # enables complex use cases like model validators etc.
        test_valve: int = Field(  # Notice the type hint: it is used to
            # choose the kind of UI element to show the user (buttons,
            # texts, etc).
            default=4,
            description="A valve controlling a numberical value"
            # required=False,  # you can enforce fields using True
        )
        pass
        # Note that this 'pass' helps for parsing and is recommended.

    # UserValves are defined the same way.
    class UserValves(BaseModel):
        test_user_valve: bool = Field(
            default=False, description="A user valve controlling a True/False (on/off) switch"
        )
        pass

    def __init__(self):
        self.valves = self.Valves()
        # Because they are set by the admin, they are accessible directly
        # upon code execution.
        pass

    # The  __user__ handling is the same for Filters, Tools and Functions.
    def test_the_tool(message: str, __user__: dict):
        """
        This is a test tool. If the user asks you to test the tools, put any
        string you want in the message argument.

        :param message: Any string you want.
        :return: The same string as input.
        """
        # Because UserValves are defined per user they are only available
        # on use.
        # Note that although __user__ is a dict, __user__["valves"] is a
        # UserValves object. Hence you can access values like that:
        test_user_valve = __user__["valves"].test_user_valve
        # Or:
        test_user_valve = dict(__user__["valves"])["test_user_valve"]
        # But this will return the default value instead of the actual value:
        # test_user_valve = __user__["valves"]["test_user_valve"]  # Do not do that!
        
        return message + f"\nThe user valve set value is: {test_user_valve}"
        
```
</details>

### Optional Arguments
Below is a list of optional arguments your tools can depend on:
- `__event_emitter__`: Emit events (see following section)
- `__event_call__`: Same as event emitter but can be used for user interactions
- `__user__`: A dictionary with user information. It also contains the `UserValves` object in `__user__["valves"]`.
- `__metadata__`: Dictionary with chat metadata
- `__messages__`: List of previous messages
- `__files__`: Attached files
- `__model__`: Model name

Just add them as argument to any method of your Tool class just like `__user__` in the example above.

### Event Emitters
Event Emitters are used to add additional information to the chat interface. Similarly to Filter Outlets, Event Emitters are capable of appending content to the chat. Unlike Filter Outlets, they are not capable of stripping information. Additionally, emitters can be activated at any stage during the Tool.

There are two different types of Event Emitters:

#### Status
This is used to add statuses to a message while it is performing steps. These can be done at any stage during the Tool. These statuses appear right above the message content. These are very useful for Tools that delay the LLM response or process large amounts of information. This allows you to inform users what is being processed in real-time.

```
await __event_emitter__(
            {
                "type": "status", # We set the type here
                "data": {"description": "Message that shows up in the chat", "done": False, "hidden": False}, 
                # Note done is False here indicating we are still emitting statuses
            }
        )
```

<details>
<summary>Example</summary>

```
async def test_function(
        self, prompt: str, __user__: dict, __event_emitter__=None
    ) -> str:
        """
        This is a demo

        :param test: this is a test parameter
        """

        await __event_emitter__(
            {
                "type": "status", # We set the type here
                "data": {"description": "Message that shows up in the chat", "done": False}, 
                # Note done is False here indicating we are still emitting statuses
            }
        )

        # Do some other logic here
        await __event_emitter__(
            {
                "type": "status",
                "data": {"description": "Completed a task message", "done": True, "hidden": False},
                # Note done is True here indicating we are done emitting statuses
                # You can also set "hidden": True if you want to remove the status once the message is returned
            }
        )

        except Exception as e:
            await __event_emitter__(
                {
                    "type": "status",
                    "data": {"description": f"An error occured: {e}", "done": True},
                }
            )

            return f"Tell the user: {e}"
```
</details>

#### Message
This type is used to append a message to the LLM at any stage in the Tool. This means that you can append messages, embed images, and even render web pages before, or after, or during the LLM response.

```
await __event_emitter__(
                    {
                        "type": "message", # We set the type here
                        "data": {"content": "This message will be appended to the chat."},
                        # Note that with message types we do NOT have to set a done condition
                    }
                )
```

<details>
<summary>Example</summary>

```
async def test_function(
        self, prompt: str, __user__: dict, __event_emitter__=None
    ) -> str:
        """
        This is a demo

        :param test: this is a test parameter
        """

        await __event_emitter__(
                    {
                        "type": "message", # We set the type here
                        "data": {"content": "This message will be appended to the chat."},
                        # Note that with message types we do NOT have to set a done condition
                    }
                )

        except Exception as e:
            await __event_emitter__(
                {
                    "type": "status",
                    "data": {"description": f"An error occured: {e}", "done": True},
                }
            )

            return f"Tell the user: {e}"
```
</details>

#### Citations
This type is used to provide citations or references in the chat. You can utilize it to specify the content, the source, and any relevant metadata. Below is an example of how to emit a citation event:

```
await __event_emitter__(
    {
        "type": "citation",
        "data": {
            "document": [content],
            "metadata": [
                {
                    "date_accessed": datetime.now().isoformat(),
                    "source": title,
                }
            ],
            "source": {"name": title, "url": url},
        },
    }
)
```
If you are sending multiple citations, you can iterate over citations and call the emitter multiple times. When implementing custom citations, ensure that you set `self.citation = False` in your `Tools` class `__init__` method. Otherwise, the built-in citations will override the ones you have pushed in. For example:

```python
def __init__(self):
    self.citation = False
```

Warning: if you set `self.citation = True`, this will replace any custom citations you send with the automatically generated return citation. By disabling it, you can fully manage your own citation references.

<details>
<summary>Example</summary>

```
class Tools:
    class UserValves(BaseModel):
        test: bool = Field(
            default=True, description="test"
        )

    def __init__(self):
        self.citation = False

async def test_function(
        self, prompt: str, __user__: dict, __event_emitter__=None
    ) -> str:
        """
        This is a demo that just creates a citation

        :param test: this is a test parameter
        """

        await __event_emitter__(
            {
                "type": "citation",
                "data": {
                    "document": ["This message will be appended to the chat as a citation when clicked into"],
                    "metadata": [
                        {
                            "date_accessed": datetime.now().isoformat(),
                            "source": title,
                        }
                    ],
                    "source": {"name": "Title of the content", "url": "http://link-to-citation"},
                },
            }
        )
```
</details>