diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py
index 41cd16d6daa..fbd361ee035 100644
--- a/libs/partners/openai/langchain_openai/chat_models/base.py
+++ b/libs/partners/openai/langchain_openai/chat_models/base.py
@@ -490,6 +490,15 @@ class BaseChatOpenAI(BaseChatModel):
     
     .. versionadded:: 0.2.14
     """
+    reasoning_summary: Optional[str] = None
+    """Generate a summary of the reasoning performed by the model (Responses API).
+
+    Reasoning models only, like OpenAI o1 and o3-mini.
+
+    Currently supported values are ``"concise"`` or ``"detailed"``.
+
+    .. versionadded:: 0.3.9
+    """
     tiktoken_model_name: Optional[str] = None
     """The model name to pass to tiktoken when using this class. 
     Tiktoken is used to count the number of tokens in documents to constrain 
@@ -541,6 +550,13 @@ class BaseChatOpenAI(BaseChatModel):
 
     If not specified then will be inferred based on invocation params.
 
+    .. versionadded:: 0.3.9
+    """
+    truncation: Optional[str] = None
+    """Truncation strategy (Responses API). Can be ``"auto"`` or ``"disabled"``
+    (default). If ``"auto"``, model may drop input items from the middle of the
+    message sequence to fit the context window.
+
     .. versionadded:: 0.3.9
     """
 
@@ -652,6 +668,8 @@ class BaseChatOpenAI(BaseChatModel):
             "n": self.n,
             "temperature": self.temperature,
             "reasoning_effort": self.reasoning_effort,
+            "reasoning_summary": self.reasoning_summary,
+            "truncation": self.truncation,
         }
 
         params = {
@@ -2877,8 +2895,13 @@ def _construct_responses_api_payload(
     for legacy_token_param in ["max_tokens", "max_completion_tokens"]:
         if legacy_token_param in payload:
             payload["max_output_tokens"] = payload.pop(legacy_token_param)
-    if "reasoning_effort" in payload:
-        payload["reasoning"] = {"effort": payload.pop("reasoning_effort")}
+    if "reasoning_effort" in payload or "reasoning_summary" in payload:
+        reasoning = {}
+        if "reasoning_effort" in payload:
+            reasoning["effort"] = payload.pop("reasoning_effort")
+        if "reasoning_summary" in payload:
+            reasoning["generate_summary"] = payload.pop("reasoning_summary")
+        payload["reasoning"] = reasoning
 
     payload["input"] = _construct_responses_api_input(messages)
     if tools := payload.pop("tools", None):
@@ -3127,6 +3150,17 @@ def _construct_lc_result_from_responses_api(
             if _FUNCTION_CALL_IDS_MAP_KEY not in additional_kwargs:
                 additional_kwargs[_FUNCTION_CALL_IDS_MAP_KEY] = {}
             additional_kwargs[_FUNCTION_CALL_IDS_MAP_KEY][output.call_id] = output.id
+        elif output.type == "computer_call":
+            tool_call = {
+                "type": "tool_call",
+                "name": output.type,
+                "args": output.action.model_dump(),
+                "id": output.call_id,
+            }
+            tool_calls.append(tool_call)
+            if _FUNCTION_CALL_IDS_MAP_KEY not in additional_kwargs:
+                additional_kwargs[_FUNCTION_CALL_IDS_MAP_KEY] = {}
+            additional_kwargs[_FUNCTION_CALL_IDS_MAP_KEY][output.call_id] = output.id
         elif output.type == "reasoning":
             additional_kwargs["reasoning"] = output.model_dump(
                 exclude_none=True, mode="json"