---
title: "ToolInvoker"
id: toolinvoker
slug: "/toolinvoker"
description: "This component is designed to execute tool calls prepared by language models. It acts as a bridge between the language model's output and the actual execution of functions or tools that perform specific tasks."
---

# ToolInvoker

This component is designed to execute tool calls prepared by language models. It acts as a bridge between the language model's output and the actual execution of functions or tools that perform specific tasks.

|                                        |                                                                                                                                    |
| -------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| **Most common position in a pipeline** | After a Chat Generator                                                                                                             |
| **Mandatory init variables**           | “tools”: A list of [`Tools`](../../tools/tool.mdx) that can be invoked                                                                       |
| **Mandatory run variables**            | “messages”: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx) objects from a Chat Generator containing tool calls                       |
| **Output variables**                   | “tool_messages”: A list of `ChatMessage` objects with tool role. Each `ChatMessage` objects wraps the result of a tool invocation. |
| **API reference**                      | [Tools](/reference/tools-api)                                                                                                             |
| **GitHub link**                        | https://github.com/deepset-ai/haystack/blob/main/haystack/components/tools/tool_invoker.py                                       |

## Overview

A `ToolInvoker` is a component that processes `ChatMessage` objects containing tool calls.  It invokes the corresponding tools and returns the results as a list of `ChatMessage` objects. Each tool is defined with a name, description, parameters, and a function that performs the task. The `ToolInvoker` manages these tools and handles the invocation process.

You can pass multiple tools to the `ToolInvoker` component, and it will automatically choose the right tool to call based on tool calls produced by a Language Model.

The `ToolInvoker` has two additionally helpful parameters:

- `convert_result_to_json_string`: Use `json.dumps` (when True) or `str` (when False) to convert the result into a string.
- `raise_on_failure`: If True, it will raise an exception in case of errors. If False, it will return a `ChatMessage` object with `error=True` and a description of the error in `result`. Use this, for example, when you want to keep the Language Model running in a loop and fixing its errors.

:::note
ChatMessage and Tool Data Classes

Follow the links to learn more about [ChatMessage](../../concepts/data-classes/chatmessage.mdx) and [Tool](../../tools/tool.mdx) data classes.
:::

## Usage

### On its own

```python
from haystack.dataclasses import ChatMessage, ToolCall
from haystack.components.tools import ToolInvoker
from haystack.tools import Tool


## Tool definition
def dummy_weather_function(city: str):
    return f"The weather in {city} is 20 degrees."


parameters = {
    "type": "object",
    "properties": {"city": {"type": "string"}},
    "required": ["city"],
}
tool = Tool(
    name="weather_tool",
    description="A tool to get the weather",
    function=dummy_weather_function,
    parameters=parameters,
)

## Usually, the ChatMessage with tool_calls is generated by a Language Model
## Here, we create it manually for demonstration purposes
tool_call = ToolCall(tool_name="weather_tool", arguments={"city": "Berlin"})
message = ChatMessage.from_assistant(tool_calls=[tool_call])

## ToolInvoker initialization and run
invoker = ToolInvoker(tools=[tool])
result = invoker.run(messages=[message])

print(result)
```

```
```

### In a pipeline

The following code snippet shows how to process a user query about the weather. First, we define a `Tool` for fetching weather data, then we initialize a `ToolInvoker` to execute this tool, while using an `OpenAIChatGenerator` to generate responses. A `ConditionalRouter` is used in this pipeline to route messages based on whether they contain tool calls. The pipeline connects these components, processes a user message asking for the weather in Berlin, and outputs the result.

```python
from haystack.dataclasses import ChatMessage
from haystack.components.tools import ToolInvoker
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.routers import ConditionalRouter
from haystack.tools import Tool
from haystack import Pipeline
from typing import List  # Ensure List is imported

## Define a dummy weather tool
import random


def dummy_weather(location: str):
    return {
        "temp": f"{random.randint(-10, 40)} °C",
        "humidity": f"{random.randint(0, 100)}%",
    }


weather_tool = Tool(
    name="weather",
    description="A tool to get the weather",
    function=dummy_weather,
    parameters={
        "type": "object",
        "properties": {"location": {"type": "string"}},
        "required": ["location"],
    },
)

## Initialize the ToolInvoker with the weather tool
tool_invoker = ToolInvoker(tools=[weather_tool])

## Initialize the ChatGenerator
chat_generator = OpenAIChatGenerator(model="gpt-4o-mini", tools=[weather_tool])

## Define routing conditions
routes = [
    {
        "condition": "{{replies[0].tool_calls | length > 0}}",
        "output": "{{replies}}",
        "output_name": "there_are_tool_calls",
        "output_type": List[ChatMessage],  # Use direct type
    },
    {
        "condition": "{{replies[0].tool_calls | length == 0}}",
        "output": "{{replies}}",
        "output_name": "final_replies",
        "output_type": List[ChatMessage],  # Use direct type
    },
]

## Initialize the ConditionalRouter
router = ConditionalRouter(routes, unsafe=True)

## Create the pipeline
pipeline = Pipeline()
pipeline.add_component("generator", chat_generator)
pipeline.add_component("router", router)
pipeline.add_component("tool_invoker", tool_invoker)

## Connect components
pipeline.connect("generator.replies", "router")
pipeline.connect(
    "router.there_are_tool_calls",
    "tool_invoker.messages",
)  # Correct connection

## Example user message
user_message = ChatMessage.from_user("What is the weather in Berlin?")

## Run the pipeline
result = pipeline.run({"messages": [user_message]})

## Print the result
print(result)
```

```
{
   "tool_invoker":{
      "tool_messages":[
         "ChatMessage(_role=<ChatRole.TOOL":"tool"">",
         "_content="[
            "ToolCallResult(result=""{'temp': '33 °C', 'humidity': '79%'}",
            "origin=ToolCall(tool_name=""weather",
            "arguments="{
               "location":"Berlin"
            },
            "id=""call_pUVl8Cycssk1dtgMWNT1T9eT"")",
            "error=False)"
         ],
         "_name=None",
         "_meta="{

         }")"
      ]
   }
}
```

## Additional References

🧑‍🍳 Cookbooks:

- [Define & Run Tools](https://haystack.deepset.ai/cookbook/tools_support)
- [Newsletter Sending Agent with Haystack Tools](https://haystack.deepset.ai/cookbook/newsletter-agent)
- [Create a Swarm of Agents](https://haystack.deepset.ai/cookbook/swarm)
