deepset-ai--haystack/docs-website/versioned_docs/version-2.21/pipeline-components/generators/openrouterchatgenerator.mdx

---
title: "OpenRouterChatGenerator"
id: openrouterchatgenerator
slug: "/openrouterchatgenerator"
description: "This component enables chat completion with any model hosted on [OpenRouter](https://openrouter.ai/)."
---

# OpenRouterChatGenerator

This component enables chat completion with any model hosted on [OpenRouter](https://openrouter.ai/).

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx)                                                                |
| **Mandatory init variables**           | `api_key`: An OpenRouter API key. Can be set with `OPENROUTER_API_KEY` env variable or passed to `init()` method. |
| **Mandatory run variables**            | `messages`: A list of [ChatMessage](../../concepts/data-classes/chatmessage.mdx) objects                                                      |
| **Output variables**                   | `replies`: A list of [ChatMessage](../../concepts/data-classes/chatmessage.mdx) objects                                                       |
| **API reference**                      | [OpenRouter](/reference/integrations-openrouter)                                                                         |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/openrouter                      |

</div>

## Overview

The `OpenRouterChatGenerator` enables you to use models from multiple providers (such as `openai/gpt-4o`, `anthropic/claude-3.5-sonnet`, and others) by making chat completion calls to the [OpenRouter API](https://openrouter.ai/docs/quickstart).

This generator also supports OpenRouter-specific features such as:

- Provider routing and model fallback that are configurable with the `generation_kwargs` parameter during initialization or runtime.
- Custom HTTP headers that can be supplied using the `extra_headers` parameter.

This component uses the same `ChatMessage` format as other Haystack Chat Generators for structured input and output. For more information, see the [ChatMessage documentation](../../concepts/data-classes/chatmessage.mdx).

### Tool Support

`OpenRouterChatGenerator` supports function calling through the `tools` parameter, which accepts flexible tool configurations:

- **A list of Tool objects**: Pass individual tools as a list
- **A single Toolset**: Pass an entire Toolset directly
- **Mixed Tools and Toolsets**: Combine multiple Toolsets with standalone tools in a single list

This allows you to organize related tools into logical groups while also including standalone tools as needed.

```python
from haystack.tools import Tool, Toolset
from haystack_integrations.components.generators.openrouter import OpenRouterChatGenerator

# Create individual tools
weather_tool = Tool(name="weather", description="Get weather info", ...)
news_tool = Tool(name="news", description="Get latest news", ...)

# Group related tools into a toolset
math_toolset = Toolset([add_tool, subtract_tool, multiply_tool])

# Pass mixed tools and toolsets to the generator
generator = OpenRouterChatGenerator(
    tools=[math_toolset, weather_tool, news_tool]  # Mix of Toolset and Tool objects
)
```

For more details on working with tools, see the [Tool](../../tools/tool.mdx) and [Toolset](../../tools/toolset.mdx) documentation.

### Initialization

To use this integration, you must have an active OpenRouter subscription with sufficient credits and an API key. You can provide it with the `OPENROUTER_API_KEY` environment variable or by using a [Secret](../../concepts/secret-management.mdx).

Then, install the `openrouter-haystack` integration:

```shell
pip install openrouter-haystack
```

### Streaming

`OpenRouterChatGenerator` supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) responses from the LLM, allowing tokens to be emitted as they are generated. To enable streaming, pass a callable to the `streaming_callback` parameter during initialization.

## Usage

### On its own

```python
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.openrouter import (
    OpenRouterChatGenerator,
)

client = OpenRouterChatGenerator()
response = client.run([ChatMessage.from_user("What are Agentic Pipelines? Be brief.")])
print(response["replies"][0].text)
```

With streaming and model routing:

```python
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.openrouter import (
    OpenRouterChatGenerator,
)

client = OpenRouterChatGenerator(
    model="openrouter/auto",
    streaming_callback=lambda chunk: print(chunk.content, end="", flush=True),
)

response = client.run([ChatMessage.from_user("What are Agentic Pipelines? Be brief.")])

## check the model used for the response
print("\n\n Model used: ", response["replies"][0].meta["model"])
```

With multimodal inputs:

```python
from haystack.dataclasses import ChatMessage, ImageContent
from haystack_integrations.components.generators.openrouter import (
    OpenRouterChatGenerator,
)

llm = OpenRouterChatGenerator(model="anthropic/claude-3-5-sonnet")

image = ImageContent.from_file_path("apple.jpg")
user_message = ChatMessage.from_user(
    content_parts=["What does the image show? Max 5 words.", image],
)

response = llm.run([user_message])["replies"][0].text
print(response)

# Red apple on straw.
```

### In a pipeline

```python
from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.openrouter import (
    OpenRouterChatGenerator,
)

prompt_builder = ChatPromptBuilder()
llm = OpenRouterChatGenerator(model="openai/gpt-4o-mini")

pipe = Pipeline()
pipe.add_component("builder", prompt_builder)
pipe.add_component("llm", llm)
pipe.connect("builder.prompt", "llm.messages")

messages = [
    ChatMessage.from_system("Give brief answers."),
    ChatMessage.from_user("Tell me about {{city}}"),
]

response = pipe.run(
    data={"builder": {"template": messages, "template_variables": {"city": "Berlin"}}},
)
print(response)
```