feat: add reasoning token support to OpenRouter integration

integrations/openrouter/pyproject.toml

@@ -23,7 +23,7 @@ classifiers = [
   "Programming Language :: Python :: Implementation :: CPython",
   "Programming Language :: Python :: Implementation :: PyPy",
 ]
-dependencies = ["haystack-ai>=2.22.0"]
+dependencies = ["haystack-ai>=2.30.0"]
 
 [project.urls]
 Documentation = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/openrouter#readme"

integrations/openrouter/src/haystack_integrations/components/generators/openrouter/chat/chat_generator.py

@@ -2,18 +2,105 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
+import json
 from typing import Any
 
 from haystack import component, default_to_dict, logging
 from haystack.components.generators.chat import OpenAIChatGenerator
-from haystack.dataclasses import ChatMessage, StreamingCallbackT
+from haystack.components.generators.chat.openai import _check_finish_reason
+from haystack.components.generators.utils import _normalize_messages, _serialize_object
+from haystack.dataclasses import (
+    ChatMessage,
+    ReasoningContent,
+    StreamingCallbackT,
+    ToolCall,
+    select_streaming_callback,
+)
 from haystack.tools import ToolsType, _check_duplicate_tool_names, flatten_tools_or_toolsets, serialize_tools_or_toolset
 from haystack.utils import serialize_callable
 from haystack.utils.auth import Secret
+from openai.types.chat import ChatCompletion, ParsedChatCompletion
+from openai.types.chat.chat_completion import Choice
 
 logger = logging.getLogger(__name__)
 
 
+def _extract_reasoning(message: Any) -> ReasoningContent | None:
+    """Extract reasoning content from an OpenRouter API response message."""
+    # OpenRouter attaches reasoning content as extra attributes on the standard OpenAI SDK message,
+    # so we read them with getattr rather than relying on typed fields.
+    reasoning_text = getattr(message, "reasoning", None) or ""
+    raw_details = getattr(message, "reasoning_details", None) or []
+
+    if not reasoning_text and not raw_details:
+        return None
+
+    details = []
+    for d in raw_details:
+        if isinstance(d, dict):
+            details.append(d)
+        elif hasattr(d, "model_dump"):
+            details.append(d.model_dump())
+        else:
+            details.append(vars(d))
+
+    # Some models only return structured details without a flat `reasoning` string, so we
+    # reconstruct the text from the known detail types.
+    if not reasoning_text and details:
+        parts = []
+        for d in details:
+            dtype = d.get("type", "")
+            if dtype == "reasoning.text":
+                parts.append(d.get("text", ""))
+            elif dtype == "reasoning.summary":
+                parts.append(d.get("summary", ""))
+        reasoning_text = "".join(parts)
+
+    extra = {}
+    if details:
+        extra["reasoning_details"] = details
+
+    return ReasoningContent(reasoning_text=reasoning_text, extra=extra)
+
+
+def _convert_openrouter_completion_to_chat_message(
+    completion: ChatCompletion | ParsedChatCompletion, choice: Choice
+) -> ChatMessage:
+    """Convert an OpenRouter chat completion to a ChatMessage, including reasoning content."""
+    message = choice.message
+    text = message.content
+    tool_calls = []
+    if message.tool_calls:
+        for tc in message.tool_calls:
+            func = getattr(tc, "function", None)
+            if func is None:
+                continue
+            try:
+                arguments = json.loads(func.arguments)
+                tool_calls.append(ToolCall(id=tc.id, tool_name=func.name, arguments=arguments))
+            except json.JSONDecodeError:
+                logger.warning(
+                    "OpenRouter returned a malformed JSON string for tool call arguments. "
+                    "Tool call ID: {_id}, Tool name: {_name}, Arguments: {_arguments}",
+                    _id=tc.id,
+                    _name=func.name,
+                    _arguments=func.arguments,
+                )
+
+    logprobs = _serialize_object(choice.logprobs) if choice.logprobs else None
+    meta = {
+        "model": completion.model,
+        "index": choice.index,
+        "finish_reason": choice.finish_reason,
+        "usage": _serialize_object(completion.usage),
+    }
+    if logprobs:
+        meta["logprobs"] = logprobs
+
+    reasoning = _extract_reasoning(message)
+    return ChatMessage.from_assistant(text=text, tool_calls=tool_calls, meta=meta, reasoning=reasoning)
+
+
 @component
 class OpenRouterChatGenerator(OpenAIChatGenerator):
     """
@@ -26,9 +113,12 @@ class OpenRouterChatGenerator(OpenAIChatGenerator):
     parameter in `run` method.
 
     Key Features and Compatibility:
-    - **Primary Compatibility**: Designed to work seamlessly with the OpenRouter chat completion endpoint.
+    - **Primary Compatibility**: Compatible 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.
+    - **Reasoning Support**: Extracts reasoning/thinking content from models that support it
+      (e.g., DeepSeek R1, Claude with extended thinking) and stores it in the `ReasoningContent`
+      field on `ChatMessage`. Reasoning content is only captured for non-streaming requests.
 
     This component uses the ChatMessage format for structuring both input and output,
     ensuring coherent and contextually relevant responses in chat-based text generation scenarios.
@@ -40,20 +130,20 @@ class OpenRouterChatGenerator(OpenAIChatGenerator):
 
     Usage example:
     ```python
-    from haystack_integrations.components.generators.openrouter import OpenRouterChatGenerator
+    from haystack_integrations.components.generators.openrouter import (
+        OpenRouterChatGenerator,
+    )
     from haystack.dataclasses import ChatMessage
 
     messages = [ChatMessage.from_user("What's Natural Language Processing?")]
 
-    client = OpenRouterChatGenerator()
+    client = OpenRouterChatGenerator(
+        model="deepseek/deepseek-r1",
+        generation_kwargs={"reasoning": {"effort": "high"}},
+    )
     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=<ChatRole.ASSISTANT: 'assistant'>, _name=None,
-    >>_meta={'model': 'openai/gpt-5-mini', 'index': 0, 'finish_reason': 'stop',
-    >>'usage': {'prompt_tokens': 15, 'completion_tokens': 36, 'total_tokens': 51}})]}
+    print(response["replies"][0].reasoning)  # Access reasoning content
+    print(response["replies"][0].text)  # Access final answer
     ```
     """
 
@@ -98,14 +188,11 @@ def __init__(
                 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.
+            - `reasoning`: A dict to configure reasoning/thinking tokens for models that support it.
+                Example: `{"effort": "high"}` or `{"max_tokens": 2000}`.
+                Reasoning content is only captured for non-streaming requests.
+                See [OpenRouter reasoning docs](https://openrouter.ai/docs/use-cases/reasoning-tokens).
             - `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.
         :param 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.
@@ -187,6 +274,12 @@ def _prepare_api_call(
         # adapt ChatMessage(s) to the format expected by the OpenAI API
         openai_formatted_messages = [message.to_openai_dict_format() for message in messages]
 
+        # OpenRouter expects reasoning_details to be sent back in multi-turn conversations, but
+        # to_openai_dict_format() strips reasoning, so we re-inject it into the formatted message dicts.
+        for i, chat_msg in enumerate(messages):
+            if chat_msg.reasoning and chat_msg.reasoning.extra.get("reasoning_details"):
+                openai_formatted_messages[i]["reasoning_details"] = chat_msg.reasoning.extra["reasoning_details"]
+
         flattened_tools = flatten_tools_or_toolsets(tools or self.tools)
         tools_strict = tools_strict if tools_strict is not None else self.tools_strict
         _check_duplicate_tool_names(flattened_tools)
@@ -227,3 +320,156 @@ def _prepare_api_call(
         if response_format:
             final_args["response_format"] = response_format
         return final_args
+
+    @component.output_types(replies=list[ChatMessage])
+    def run(
+        self,
+        messages: list[ChatMessage] | str,
+        streaming_callback: StreamingCallbackT | None = None,
+        generation_kwargs: dict[str, Any] | None = None,
+        *,
+        tools: ToolsType | None = None,
+        tools_strict: bool | None = None,
+    ) -> dict[str, list[ChatMessage]]:
+        """
+        Invokes chat completion on the OpenRouter API.
+
+        :param messages:
+            A list of ChatMessage instances representing the input messages.
+            If a string is provided, it is converted to a list containing a ChatMessage with user role.
+        :param streaming_callback:
+            A callback function that is called when a new token is received from the stream.
+        :param generation_kwargs:
+            Additional keyword arguments for text generation. These parameters will
+            override the parameters passed during component initialization.
+            For details on OpenRouter API parameters, see
+            [OpenRouter docs](https://openrouter.ai/docs/quickstart).
+        :param tools: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+            If set, it will override the `tools` parameter provided during initialization.
+        :param tools_strict:
+            Whether to enable strict schema adherence for tool calls.
+
+        :returns:
+            A dictionary with the following key:
+            - `replies`: A list containing the generated responses as ChatMessage instances.
+        """
+        messages = _normalize_messages(messages)
+        if not self._is_warmed_up:
+            self.warm_up()
+
+        if len(messages) == 0:
+            return {"replies": []}
+
+        streaming_callback = select_streaming_callback(
+            init_callback=self.streaming_callback, runtime_callback=streaming_callback, requires_async=False
+        )
+
+        # Reasoning content is reconstructed from the full response message, which is not available while
+        # streaming, so we warn the user that it will not be captured in this mode.
+        if streaming_callback is not None:
+            merged_kwargs = {**self.generation_kwargs, **(generation_kwargs or {})}
+            if merged_kwargs.get("reasoning"):
+                logger.warning(
+                    "Streaming with reasoning is active. Reasoning content will not be captured during "
+                    "streaming. Use non-streaming mode to extract reasoning content."
+                )
+
+        api_args = self._prepare_api_call(
+            messages=messages,
+            streaming_callback=streaming_callback,
+            generation_kwargs=generation_kwargs,
+            tools=tools,
+            tools_strict=tools_strict,
+        )
+        openai_endpoint = api_args.pop("openai_endpoint")
+        chat_completion = getattr(self.client.chat.completions, openai_endpoint)(**api_args)
+
+        if streaming_callback is not None:
+            # streaming uses the inherited handler so reasoning extraction is intentionally skipped
+            completions = self._handle_stream_response(chat_completion, streaming_callback)
+        else:
+            assert isinstance(chat_completion, ChatCompletion), "Unexpected response type for non-streaming request."
+            completions = [
+                _convert_openrouter_completion_to_chat_message(chat_completion, choice)
+                for choice in chat_completion.choices
+            ]
+
+        for message in completions:
+            _check_finish_reason(message.meta)
+
+        return {"replies": completions}
+
+    @component.output_types(replies=list[ChatMessage])
+    async def run_async(
+        self,
+        messages: list[ChatMessage] | str,
+        streaming_callback: StreamingCallbackT | None = None,
+        generation_kwargs: dict[str, Any] | None = None,
+        *,
+        tools: ToolsType | None = None,
+        tools_strict: bool | None = None,
+    ) -> dict[str, list[ChatMessage]]:
+        """
+        Asynchronously invokes chat completion on the OpenRouter API.
+
+        :param messages:
+            A list of ChatMessage instances representing the input messages.
+            If a string is provided, it is converted to a list containing a ChatMessage with user role.
+        :param streaming_callback:
+            A callback function that is called when a new token is received from the stream.
+            Must be a coroutine.
+        :param generation_kwargs:
+            Additional keyword arguments for text generation.
+        :param tools: A list of Tool and/or Toolset objects, or a single Toolset.
+        :param tools_strict:
+            Whether to enable strict schema adherence for tool calls.
+
+        :returns:
+            A dictionary with the following key:
+            - `replies`: A list containing the generated responses as ChatMessage instances.
+        """
+        messages = _normalize_messages(messages)
+        if not self._is_warmed_up:
+            self.warm_up()
+
+        if len(messages) == 0:
+            return {"replies": []}
+
+        streaming_callback = select_streaming_callback(
+            init_callback=self.streaming_callback, runtime_callback=streaming_callback, requires_async=True
+        )
+
+        # Reasoning content is reconstructed from the full response message, which is not available while
+        # streaming, so we warn the user that it will not be captured in this mode.
+        if streaming_callback is not None:
+            merged_kwargs = {**self.generation_kwargs, **(generation_kwargs or {})}
+            if merged_kwargs.get("reasoning"):
+                logger.warning(
+                    "Streaming with reasoning is active. Reasoning content will not be captured during "
+                    "streaming. Use non-streaming mode to extract reasoning content."
+                )
+
+        api_args = self._prepare_api_call(
+            messages=messages,
+            streaming_callback=streaming_callback,
+            generation_kwargs=generation_kwargs,
+            tools=tools,
+            tools_strict=tools_strict,
+        )
+        openai_endpoint = api_args.pop("openai_endpoint")
+        chat_completion = await getattr(self.async_client.chat.completions, openai_endpoint)(**api_args)
+
+        if streaming_callback is not None:
+            # streaming uses the inherited handler so reasoning extraction is intentionally skipped
+            completions = await self._handle_async_stream_response(chat_completion, streaming_callback)
+        else:
+            assert isinstance(chat_completion, ChatCompletion), "Unexpected response type for non-streaming request."
+            completions = [
+                _convert_openrouter_completion_to_chat_message(chat_completion, choice)
+                for choice in chat_completion.choices
+            ]
+
+        for message in completions:
+            _check_finish_reason(message.meta)
+
+        return {"replies": completions}