fix(llms/openai): fix mypy list-item type error in message conversion

_convert_message_to_responses_input_items() was annotated to return
list[dict[str, Any]], but the passthrough branch returns the LLMMessage
argument unchanged. Lists are invariant in mypy, so a bare LLMMessage
(TypedDict) isn't assignable into a list[dict[str, Any]] return - this
was flagged by CI's type-checker job across all Python versions.

Widened the return type (and the local list built in the tool_calls
branch) to list[dict[str, Any] | LLMMessage], matching what the
function actually returns.

Confirmed with a local mypy run and the full openai/agent_utils test
suites (176 passed).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

lib/crewai/src/crewai/llms/providers/openai/completion.py

@@ -643,6 +643,44 @@ class OpenAICompletion(BaseLLM):
             response_model=response_model,
         )
 
+    def _convert_message_to_responses_input_items(
+        self, message: LLMMessage
+    ) -> list[dict[str, Any] | LLMMessage]:
+        """Convert a Chat-Completions-style message into Responses API input items.
+
+        The Responses API has no message shape for an assistant turn carrying
+        ``tool_calls`` or for a ``tool`` role reply - those become standalone
+        ``function_call`` / ``function_call_output`` input items instead. Plain
+        user/assistant text messages pass through unchanged (accepted as-is by
+        the Responses API's lenient "easy input message" shape).
+        """
+        role = message.get("role")
+
+        if role == "assistant" and message.get("tool_calls"):
+            items: list[dict[str, Any] | LLMMessage] = []
+            for tool_call in message["tool_calls"]:
+                function = tool_call.get("function", {})
+                items.append(
+                    {
+                        "type": "function_call",
+                        "call_id": tool_call.get("id", ""),
+                        "name": function.get("name", ""),
+                        "arguments": function.get("arguments", ""),
+                    }
+                )
+            return items
+
+        if role == "tool":
+            return [
+                {
+                    "type": "function_call_output",
+                    "call_id": message.get("tool_call_id", ""),
+                    "output": message.get("content") or "",
+                }
+            ]
+
+        return [message]
+
     def _prepare_responses_params(
         self,
         messages: list[LLMMessage],
@@ -658,7 +696,7 @@ class OpenAICompletion(BaseLLM):
         - Internally-tagged tool format (flat structure)
         """
         instructions: str | None = self.instructions
-        input_messages: list[LLMMessage] = []
+        input_messages: list[Any] = []
 
         for message in messages:
             if message.get("role") == "system":
@@ -669,7 +707,9 @@ class OpenAICompletion(BaseLLM):
                 else:
                     instructions = content_str
             else:
-                input_messages.append(message)
+                input_messages.extend(
+                    self._convert_message_to_responses_input_items(message)
+                )
 
         # Prepend reasoning items for ZDR (zero-data-retention) chaining when configured
         final_input: list[Any] = []

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -1228,7 +1228,14 @@ def extract_tool_call_info(
         )
         func_info = tool_call.get("function", {})
         func_name = func_info.get("name", "") or tool_call.get("name", "")
-        func_args = func_info.get("arguments") or tool_call.get("input") or {}
+        # OpenAI Responses API function_call items are flat dicts using
+        # "arguments" (not "input") with no nested "function" key.
+        func_args = (
+            func_info.get("arguments")
+            or tool_call.get("arguments")
+            or tool_call.get("input")
+            or {}
+        )
         return call_id, sanitize_tool_name(func_name), func_args
     return None
 
@@ -1260,6 +1267,13 @@ def is_tool_call_list(response: list[Any]) -> bool:
     # Bedrock-style
     if isinstance(first_item, dict) and "name" in first_item and "input" in first_item:
         return True
+    # OpenAI Responses API-style (flat dict, no nested "function" key)
+    if (
+        isinstance(first_item, dict)
+        and "name" in first_item
+        and "arguments" in first_item
+    ):
+        return True
     # Gemini-style
     if hasattr(first_item, "function_call") and first_item.function_call:
         return True

lib/crewai/tests/llms/openai/test_openai.py

@@ -812,6 +812,109 @@ def test_openai_responses_api_with_system_message_extraction():
     assert result.isupper() or "HELLO" in result.upper()
 
 
+def test_openai_responses_api_converts_assistant_tool_calls_message():
+    """Regression: assistant messages carrying tool_calls (Chat-Completions
+    shape) must become standalone function_call input items, since the
+    Responses API has no message shape for an assistant tool-call turn.
+    """
+    llm = OpenAICompletion(model="gpt-4o-mini", api="responses")
+
+    messages = [
+        {"role": "user", "content": "Fetch https://example.com"},
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "call_abc123",
+                    "type": "function",
+                    "function": {
+                        "name": "fetch_page",
+                        "arguments": '{"url": "https://example.com"}',
+                    },
+                }
+            ],
+        },
+    ]
+
+    params = llm._prepare_responses_params(messages)
+
+    assert params["input"][0] == {"role": "user", "content": "Fetch https://example.com"}
+    assert params["input"][1] == {
+        "type": "function_call",
+        "call_id": "call_abc123",
+        "name": "fetch_page",
+        "arguments": '{"url": "https://example.com"}',
+    }
+
+
+def test_openai_responses_api_converts_tool_result_message():
+    """Regression: tool-role messages (Chat-Completions shape) must become
+    function_call_output input items for the Responses API.
+    """
+    llm = OpenAICompletion(model="gpt-4o-mini", api="responses")
+
+    messages = [
+        {
+            "role": "tool",
+            "tool_call_id": "call_abc123",
+            "name": "fetch_page",
+            "content": "<html>page text</html>",
+        },
+    ]
+
+    params = llm._prepare_responses_params(messages)
+
+    assert params["input"] == [
+        {
+            "type": "function_call_output",
+            "call_id": "call_abc123",
+            "output": "<html>page text</html>",
+        }
+    ]
+
+
+def test_openai_responses_api_multi_turn_tool_conversation_shape():
+    """Regression: a full multi-turn tool-calling conversation (user ->
+    assistant tool_calls -> tool result) must convert entirely into valid
+    Responses API input items, with no leftover Chat-Completions-only keys
+    ("tool_calls", "tool_call_id") that the Responses API would reject.
+    """
+    llm = OpenAICompletion(model="gpt-4o-mini", api="responses")
+
+    messages = [
+        {"role": "user", "content": "Fetch https://example.com"},
+        {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [
+                {
+                    "id": "call_abc123",
+                    "type": "function",
+                    "function": {
+                        "name": "fetch_page",
+                        "arguments": '{"url": "https://example.com"}',
+                    },
+                }
+            ],
+        },
+        {
+            "role": "tool",
+            "tool_call_id": "call_abc123",
+            "name": "fetch_page",
+            "content": "<html>page text</html>",
+        },
+    ]
+
+    params = llm._prepare_responses_params(messages)
+
+    for item in params["input"]:
+        assert "tool_calls" not in item
+        assert "tool_call_id" not in item
+    assert params["input"][1]["type"] == "function_call"
+    assert params["input"][2]["type"] == "function_call_output"
+
+
 @pytest.mark.vcr()
 def test_openai_responses_api_streaming():
     """Test Responses API with streaming enabled."""

lib/crewai/tests/utilities/test_agent_utils.py

@@ -25,6 +25,8 @@ from crewai.utilities.agent_utils import (
     _split_messages_into_chunks,
     convert_tools_to_openai_schema,
     execute_single_native_tool_call,
+    extract_tool_call_info,
+    is_tool_call_list,
     NativeToolCallResult,
     parse_tool_call_args,
     summarize_messages,
@@ -981,6 +983,88 @@ class TestParallelSummarizationVCR:
         assert "report.pdf" in summary_msg["files"]
 
 
+class TestIsToolCallListResponsesApiShape:
+    """Regression tests: OpenAI Responses API tool-call dicts must be recognized.
+
+    Responses API function_call output items are flat dicts shaped
+    {"id", "name", "arguments"} - no nested "function" key, and "arguments"
+    instead of Anthropic/Bedrock-style "input".
+    """
+
+    def test_responses_api_dict_is_recognized_as_tool_call(self) -> None:
+        response = [
+            {
+                "id": "call_abc123",
+                "name": "fetch_page",
+                "arguments": '{"url": "https://example.com"}',
+            }
+        ]
+        assert is_tool_call_list(response) is True
+
+    def test_plain_text_answer_not_misclassified(self) -> None:
+        assert is_tool_call_list(["just a string, not a tool call"]) is False
+
+    def test_empty_list_returns_false(self) -> None:
+        assert is_tool_call_list([]) is False
+
+    def test_chat_completions_style_still_recognized(self) -> None:
+        response = [{"function": {"name": "fetch_page", "arguments": "{}"}}]
+        assert is_tool_call_list(response) is True
+
+    def test_bedrock_anthropic_style_still_recognized(self) -> None:
+        response = [{"name": "fetch_page", "input": {"url": "https://example.com"}}]
+        assert is_tool_call_list(response) is True
+
+
+class TestExtractToolCallInfoResponsesApiShape:
+    """Regression tests: extract_tool_call_info must parse Responses API dicts."""
+
+    def test_responses_api_dict_extracts_real_arguments(self) -> None:
+        tool_call = {
+            "id": "call_abc123",
+            "name": "fetch_page",
+            "arguments": '{"url": "https://example.com"}',
+        }
+        result = extract_tool_call_info(tool_call)
+        assert result is not None
+        call_id, func_name, func_args = result
+        assert call_id == "call_abc123"
+        assert func_name == "fetch_page"
+        assert func_args == '{"url": "https://example.com"}'
+
+    def test_responses_api_dict_does_not_return_empty_args(self) -> None:
+        tool_call = {
+            "id": "call_xyz",
+            "name": "fetch_page",
+            "arguments": '{"url": "https://example.com"}',
+        }
+        _, _, func_args = extract_tool_call_info(tool_call)
+        assert func_args != {}
+
+    def test_bedrock_anthropic_style_still_uses_input(self) -> None:
+        tool_call = {"name": "fetch_page", "input": {"url": "https://example.com"}}
+        _, func_name, func_args = extract_tool_call_info(tool_call)
+        assert func_name == "fetch_page"
+        assert func_args == {"url": "https://example.com"}
+
+    def test_chat_completions_style_still_uses_nested_function(self) -> None:
+        tool_call = {
+            "id": "call_1",
+            "function": {"name": "fetch_page", "arguments": "{}"},
+        }
+        _, func_name, func_args = extract_tool_call_info(tool_call)
+        assert func_name == "fetch_page"
+        assert func_args == "{}"
+
+    def test_non_dict_unrecognized_shape_returns_none(self) -> None:
+        assert extract_tool_call_info("just a string") is None
+
+    def test_unrecognized_dict_shape_returns_empty_name_and_args(self) -> None:
+        call_id, func_name, func_args = extract_tool_call_info({"unrelated": "data"})
+        assert func_name == ""
+        assert func_args == {}
+
+
 class TestParseToolCallArgs:
     """Unit tests for parse_tool_call_args."""