chore: import upstream snapshot with attribution

2026-07-13 13:22:28 +08:00
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+---
+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/).
+
+|                                        |                                                                                                                   |
+| -------------------------------------- | ----------------------------------------------------------------------------------------------------------------- |
+| **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                      |
+
+## 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).
+
+It is also fully compatible with Haystack [Tools](../../tools/tool.mdx) and [Toolsets](../../tools/toolset.mdx) 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](../../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"])
+```
+
+### 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)
+```