---
title: "OpenRouter"
id: integrations-openrouter
description: "OpenRouter integration for Haystack"
slug: "/integrations-openrouter"
---
## Module haystack\_integrations.components.generators.openrouter.chat.chat\_generator
### OpenRouterChatGenerator
Enables text generation using OpenRouter generative models.
For supported models, see [OpenRouter docs](https://openrouter.ai/models).
Users can pass any text generation parameters valid for the OpenRouter chat completion API
directly to this component using the `generation_kwargs` parameter in `__init__` or the `generation_kwargs`
parameter in `run` method.
Key Features and Compatibility:
- **Primary Compatibility**: Designed to work seamlessly with the OpenRouter chat completion endpoint.
- **Streaming Support**: Supports streaming responses from the OpenRouter chat completion endpoint.
- **Customizability**: Supports all parameters supported by the OpenRouter chat completion endpoint.
This component uses the ChatMessage format for structuring both input and output,
ensuring coherent and contextually relevant responses in chat-based text generation scenarios.
Details on the ChatMessage format can be found in the
[Haystack docs](https://docs.haystack.deepset.ai/docs/chatmessage)
For more details on the parameters supported by the OpenRouter API, refer to the
[OpenRouter API Docs](https://openrouter.ai/docs/quickstart).
Usage example:
```python
from haystack_integrations.components.generators.openrouter import OpenRouterChatGenerator
from haystack.dataclasses import ChatMessage
messages = [ChatMessage.from_user("What's Natural Language Processing?")]
client = OpenRouterChatGenerator()
response = client.run(messages)
print(response)
>>{'replies': [ChatMessage(_content='Natural Language Processing (NLP) is a branch of artificial intelligence
>>that focuses on enabling computers to understand, interpret, and generate human language in a way that is
>>meaningful and useful.', _role=, _name=None,
>>_meta={'model': 'openai/gpt-4o-mini', 'index': 0, 'finish_reason': 'stop',
>>'usage': {'prompt_tokens': 15, 'completion_tokens': 36, 'total_tokens': 51}})]}
```
#### OpenRouterChatGenerator.\_\_init\_\_
```python
def __init__(*,
api_key: Secret = Secret.from_env_var("OPENROUTER_API_KEY"),
model: str = "openai/gpt-4o-mini",
streaming_callback: Optional[StreamingCallbackT] = None,
api_base_url: Optional[str] = "https://openrouter.ai/api/v1",
generation_kwargs: Optional[Dict[str, Any]] = None,
tools: Optional[ToolsType] = None,
timeout: Optional[float] = None,
extra_headers: Optional[Dict[str, Any]] = None,
max_retries: Optional[int] = None,
http_client_kwargs: Optional[Dict[str, Any]] = None)
```
Creates an instance of OpenRouterChatGenerator. Unless specified otherwise,
the default model is `openai/gpt-4o-mini`.
**Arguments**:
- `api_key`: The OpenRouter API key.
- `model`: The name of the OpenRouter chat completion model to use.
- `streaming_callback`: A callback function that is called when a new token is received from the stream.
The callback function accepts StreamingChunk as an argument.
- `api_base_url`: The OpenRouter API Base url.
For more details, see OpenRouter [docs](https://openrouter.ai/docs/quickstart).
- `generation_kwargs`: Other parameters to use for the model. These parameters are all sent directly to
the OpenRouter endpoint. See [OpenRouter API docs](https://openrouter.ai/docs/quickstart) for more details.
Some of the supported parameters:
- `max_tokens`: The maximum number of tokens the output text can have.
- `temperature`: What sampling temperature to use. Higher values mean the model will take more risks.
Try 0.9 for more creative applications and 0 (argmax sampling) for ones with a well-defined answer.
- `top_p`: An alternative to sampling with temperature, called nucleus sampling, where the model
considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens
comprising the top 10% probability mass are considered.
- `stream`: Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent
events as they become available, with the stream terminated by a data: [DONE] message.
- `safe_prompt`: Whether to inject a safety prompt before all conversations.
- `random_seed`: The seed to use for random sampling.
- `response_format`: A JSON schema or a Pydantic model that enforces the structure of the model's response.
If provided, the output will always be validated against this
format (unless the model returns a tool call).
For details, see the [OpenAI Structured Outputs documentation](https://platform.openai.com/docs/guides/structured-outputs).
Notes:
- This parameter accepts Pydantic models and JSON schemas for latest models starting from GPT-4o.
- For structured outputs with streaming,
the `response_format` must be a JSON schema and not a Pydantic model.
- `tools`: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
list of `Tool` objects or a `Toolset` instance.
- `timeout`: The timeout for the OpenRouter API call.
- `extra_headers`: Additional HTTP headers to include in requests to the OpenRouter API.
This can be useful for adding site URL or title for rankings on openrouter.ai
For more details, see OpenRouter [docs](https://openrouter.ai/docs/quickstart).
- `max_retries`: Maximum number of retries to contact OpenAI after an internal error.
If not set, it defaults to either the `OPENAI_MAX_RETRIES` environment variable, or set to 5.
- `http_client_kwargs`: A dictionary of keyword arguments to configure a custom `httpx.Client`or `httpx.AsyncClient`.
For more information, see the [HTTPX documentation](https://www.python-httpx.org/api/`client`).
#### OpenRouterChatGenerator.to\_dict
```python
def to_dict() -> Dict[str, Any]
```
Serialize this component to a dictionary.
**Returns**:
The serialized component as a dictionary.