fix(fireworks): bind_tools(strict: bool) and reasoning_content (#34343)

Extract strict from kwargs and pass it to convert_to_openai_tool when
converting tools. This ensures that when strict is provided, it's
properly used during tool conversion and removed from kwargs before
calling the parent bind method.

Also extract reasoning_content from API responses and store it in
additional_kwargs for AIMessage objects.

Fixes https://github.com/langchain-ai/langchain/issues/34341 and
https://github.com/langchain-ai/langchain/issues/34342

---------

Co-authored-by: Mason Daugherty <mason@langchain.dev>

libs/partners/fireworks/langchain_fireworks/chat_models.py

@@ -114,8 +114,12 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
         # Also Fireworks returns None for tool invocations
         content = _dict.get("content", "") or ""
         additional_kwargs: dict = {}
+        if reasoning_content := _dict.get("reasoning_content"):
+            additional_kwargs["reasoning_content"] = reasoning_content
+
         if function_call := _dict.get("function_call"):
             additional_kwargs["function_call"] = dict(function_call)
+
         tool_calls = []
         invalid_tool_calls = []
         if raw_tool_calls := _dict.get("tool_calls"):
@@ -678,7 +682,10 @@ class ChatFireworks(BaseChatModel):
             **kwargs: Any additional parameters to pass to
                 `langchain_fireworks.chat_models.ChatFireworks.bind`
         """  # noqa: E501
-        formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
+        strict = kwargs.pop("strict", None)
+        formatted_tools = [
+            convert_to_openai_tool(tool, strict=strict) for tool in tools
+        ]
         if tool_choice is not None and tool_choice:
             if isinstance(tool_choice, str) and (
                 tool_choice not in ("auto", "any", "none")

libs/partners/fireworks/tests/integration_tests/test_chat_models.py

@@ -18,15 +18,30 @@ from langchain_fireworks import ChatFireworks
 _MODEL = "accounts/fireworks/models/gpt-oss-120b"
 
 
-def test_tool_choice_bool() -> None:
+@pytest.mark.parametrize("strict", [None, True, False])
-    """Test that tool choice is respected just passing in True."""
+def test_tool_choice_bool(strict: bool | None) -> None:  # noqa: FBT001
+    """Test that tool choice is respected with different strict values."""
     llm = ChatFireworks(model="fireworks/kimi-k2-instruct-0905")
 
     class MyTool(BaseModel):
         name: str
         age: int
 
-    with_tool = llm.bind_tools([MyTool], tool_choice=True)
+    kwargs = {"tool_choice": True}
+    if strict is not None:
+        kwargs["strict"] = strict
+    with_tool = llm.bind_tools([MyTool], **kwargs)
+
+    # Verify that strict is correctly set in the tool definition
+    assert hasattr(with_tool, "kwargs")
+    tools = with_tool.kwargs.get("tools", [])
+    assert len(tools) == 1
+    tool_def = tools[0]
+    assert "function" in tool_def
+    if strict is None:
+        assert "strict" not in tool_def["function"]
+    else:
+        assert tool_def["function"].get("strict") is strict
 
     resp = with_tool.invoke("Who was the 27 year old named Erick?")
     assert isinstance(resp, AIMessage)

libs/partners/fireworks/tests/unit_tests/test_chat_models.py

@@ -0,0 +1,38 @@
+"""Unit tests for ChatFireworks."""
+
+from __future__ import annotations
+
+from langchain_core.messages import AIMessage
+
+from langchain_fireworks.chat_models import _convert_dict_to_message
+
+
+def test_convert_dict_to_message_with_reasoning_content() -> None:
+    """Test that reasoning_content is correctly extracted from API response."""
+    response_dict = {
+        "role": "assistant",
+        "content": "The answer is 42.",
+        "reasoning_content": "Let me think about this step by step...",
+    }
+
+    message = _convert_dict_to_message(response_dict)
+
+    assert isinstance(message, AIMessage)
+    assert message.content == "The answer is 42."
+    assert "reasoning_content" in message.additional_kwargs
+    expected_reasoning = "Let me think about this step by step..."
+    assert message.additional_kwargs["reasoning_content"] == expected_reasoning
+
+
+def test_convert_dict_to_message_without_reasoning_content() -> None:
+    """Test that messages without reasoning_content work correctly."""
+    response_dict = {
+        "role": "assistant",
+        "content": "The answer is 42.",
+    }
+
+    message = _convert_dict_to_message(response_dict)
+
+    assert isinstance(message, AIMessage)
+    assert message.content == "The answer is 42."
+    assert "reasoning_content" not in message.additional_kwargs