
Most common position in a pipeline	After a ChatPromptBuilder
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 objects
Output variables	“replies”: A list of ChatMessage objects
API reference	TBD
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/openrouter

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.

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.

It is also fully compatible with Haystack Tools and Toolsets that allow function-calling capabilities with supported models.

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.

Then, install the openrouter-haystack integration:

pip install openrouter-haystack

Streaming

OpenRouterChatGenerator supports streaming 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

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:

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"])

In a pipeline

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)