fix: run last llm call as structured output if passed

lib/crewai/src/crewai/agent/core.py

@@ -307,11 +307,15 @@ class Agent(BaseAgent):
         task_prompt = task.prompt()
 
         # If the task requires output in JSON or Pydantic format,
-        # append specific instructions to the task prompt to ensure
-        # that the final answer does not include any code block markers
-        # Skip this if task.response_model is set, as native structured outputs handle schema automatically
-        if (task.output_json or task.output_pydantic) and not task.response_model:
-            # Generate the schema based on the output format
+        # only append schema instructions if the LLM doesn't support function calling.
+        # When function calling is supported, the schema will be enforced via response_model
+        # in a separate call after the agent completes its reasoning.
+        if (
+            (task.output_json or task.output_pydantic)
+            and not task.response_model
+            and isinstance(self.llm, BaseLLM)
+            and not self.llm.supports_function_calling()
+        ):
             if task.output_json:
                 schema_dict = generate_model_description(task.output_json)
                 schema = json.dumps(schema_dict, indent=2)
@@ -522,10 +526,21 @@ class Agent(BaseAgent):
         for tool_result in self.tools_results:
             if tool_result.get("result_as_answer", False):
                 result = tool_result["result"]
+
+        output_str = result if isinstance(result, str) else result.get("output", "")
         crewai_event_bus.emit(
             self,
-            event=AgentExecutionCompletedEvent(agent=self, task=task, output=result),
+            event=AgentExecutionCompletedEvent(
+                agent=self, task=task, output=output_str
+            ),
         )
+
+        if isinstance(result, dict):
+            agent_finish = result.get("agent_finish")
+            if agent_finish and getattr(agent_finish, "pydantic", None) is not None:
+                return result
+            return output_str
+
         return result
 
     def _execute_with_timeout(self, task_prompt: str, task: Task, timeout: int) -> Any:
@@ -581,7 +596,7 @@ class Agent(BaseAgent):
                 "tools": self.agent_executor.tools_description,
                 "ask_for_human_input": task.human_input,
             }
-        )["output"]
+        )
 
     def create_agent_executor(
         self, tools: list[BaseTool] | None = None, task: Task | None = None

lib/crewai/src/crewai/agents/agent_adapters/langgraph/langgraph_adapter.py

@@ -64,7 +64,7 @@ class LangGraphAgentAdapter(BaseAgentAdapter):
         llm: Any = None,
         max_iterations: int = 10,
         agent_config: dict[str, Any] | None = None,
-        **kwargs,
+        **kwargs: Any,
     ) -> None:
         """Initialize the LangGraph agent adapter.
 
@@ -160,7 +160,7 @@ class LangGraphAgentAdapter(BaseAgentAdapter):
         task: Any,
         context: str | None = None,
         tools: list[BaseTool] | None = None,
-    ) -> str:
+    ) -> str | dict[str, Any]:
         """Execute a task using the LangGraph workflow.
 
         Configures the agent, processes the task through the LangGraph workflow,

lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py

@@ -106,7 +106,7 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
         task: Any,
         context: str | None = None,
         tools: list[BaseTool] | None = None,
-    ) -> str:
+    ) -> str | dict[str, Any]:
         """Execute a task using the OpenAI Assistant.
 
         Configures the assistant, processes the task, and handles event emission

lib/crewai/src/crewai/agents/agent_builder/base_agent.py

@@ -327,7 +327,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         task: Any,
         context: str | None = None,
         tools: list[BaseTool] | None = None,
-    ) -> str:
+    ) -> str | dict[str, Any]:
         pass
 
     @abstractmethod

lib/crewai/src/crewai/agents/crew_agent_executor.py

@@ -195,7 +195,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self._create_short_term_memory(formatted_answer)
         self._create_long_term_memory(formatted_answer)
         self._create_external_memory(formatted_answer)
-        return {"output": formatted_answer.output}
+        return {"output": formatted_answer.output, "agent_finish": formatted_answer}
 
     def _invoke_loop(self) -> AgentFinish:
         """Execute agent loop until completion.
@@ -301,6 +301,27 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 "Agent execution ended without reaching a final answer. "
                 f"Got {type(formatted_answer).__name__} instead of AgentFinish."
             )
+
+        if (
+            self.task
+            and (self.task.output_pydantic or self.task.output_json)
+            and self.llm.supports_function_calling()
+            and not self.response_model
+            and formatted_answer.pydantic is None
+        ):
+            structured_answer = get_llm_response(
+                llm=self.llm,
+                messages=self.messages,
+                callbacks=self.callbacks,
+                printer=self._printer,
+                from_task=self.task,
+                from_agent=self.agent,
+                response_model=self.task.output_pydantic or self.task.output_json,
+            )
+
+            if isinstance(structured_answer, BaseModel):
+                formatted_answer.pydantic = structured_answer
+
         self._show_logs(formatted_answer)
         return formatted_answer
 

lib/crewai/src/crewai/lite_agent.py

@@ -305,10 +305,17 @@ class LiteAgent(FlowTrackable, BaseModel):
         formatted_result: BaseModel | None = None
         if self.response_format:
             try:
-                # Cast to BaseModel to ensure type safety
-                result = self.response_format.model_validate_json(agent_finish.output)
-                if isinstance(result, BaseModel):
-                    formatted_result = result
+                if (
+                    hasattr(agent_finish, "pydantic")
+                    and agent_finish.pydantic is not None
+                ):
+                    formatted_result = agent_finish.pydantic
+                else:
+                    result = self.response_format.model_validate_json(
+                        agent_finish.output
+                    )
+                    if isinstance(result, BaseModel):
+                        formatted_result = result
             except Exception as e:
                 self._printer.print(
                     content=f"Failed to parse output into response format: {e!s}",
@@ -476,13 +483,17 @@ class LiteAgent(FlowTrackable, BaseModel):
                 enforce_rpm_limit(self.request_within_rpm_limit)
 
                 try:
+                    use_response_model = (
+                        self.response_format if not self._parsed_tools else None
+                    )
+
                     answer = get_llm_response(
                         llm=cast(LLM, self.llm),
                         messages=self._messages,
                         callbacks=self._callbacks,
                         printer=self._printer,
                         from_agent=self,
-                        response_model=self.response_format,
+                        response_model=use_response_model,
                     )
 
                 except Exception as e:

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

@@ -316,6 +316,16 @@ class OpenAICompletion(BaseLLM):
                     )
                     return parsed_object
 
+                content = math_reasoning.content or ""
+                self._emit_call_completed_event(
+                    response=content,
+                    call_type=LLMCallType.LLM_CALL,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                    messages=params["messages"],
+                )
+                return content
+
             response: ChatCompletion = self.client.chat.completions.create(**params)
 
             usage = self._extract_openai_token_usage(response)

lib/crewai/src/crewai/task.py

@@ -12,6 +12,7 @@ import threading
 from typing import (
     Any,
     ClassVar,
+    TypedDict,
     cast,
     get_args,
     get_origin,
@@ -31,6 +32,7 @@ from pydantic_core import PydanticCustomError
 from typing_extensions import Self
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.agents.parser import AgentFinish
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.task_events import (
     TaskCompletedEvent,
@@ -60,6 +62,18 @@ from crewai.utilities.string_utils import interpolate_only
 _printer = Printer()
 
 
+class ExecutorResult(TypedDict, total=False):
+    """Type definition for agent executor return value.
+
+    Attributes:
+        output: The string output from the agent execution.
+        agent_finish: The AgentFinish object containing execution details and optional pydantic model.
+    """
+
+    output: str
+    agent_finish: AgentFinish
+
+
 class Task(BaseModel):
     """Class that represents a task to be executed.
 
@@ -519,13 +533,32 @@ class Task(BaseModel):
 
             self.processed_by_agents.add(agent.role)
             crewai_event_bus.emit(self, TaskStartedEvent(context=context, task=self))  # type: ignore[no-untyped-call]
-            result = agent.execute_task(
-                task=self,
-                context=context,
-                tools=tools,
+            executor_result = cast(
+                str | ExecutorResult,
+                agent.execute_task(
+                    task=self,
+                    context=context,
+                    tools=tools,
+                ),
             )
 
-            pydantic_output, json_output = self._export_output(result)
+            pydantic_output: BaseModel | None
+            json_output: dict[str, Any] | None
+
+            if isinstance(executor_result, dict) and "agent_finish" in executor_result:
+                result = executor_result["output"]
+                agent_finish = executor_result["agent_finish"]
+                if self.converter_cls is not None:
+                    pydantic_output, json_output = self._export_output(result)
+                elif agent_finish.pydantic is not None:
+                    pydantic_output = agent_finish.pydantic
+                    json_output = pydantic_output.model_dump()
+                else:
+                    pydantic_output, json_output = self._export_output(result)
+            else:
+                result = str(executor_result)
+                pydantic_output, json_output = self._export_output(result)
+
             task_output = TaskOutput(
                 name=self.name or self.description,
                 description=self.description,
@@ -929,12 +962,17 @@ Follow these guidelines:
             )
 
             # Regenerate output from agent
-            result = agent.execute_task(
+            retry_result = agent.execute_task(
                 task=self,
                 context=context,
                 tools=tools,
             )
 
+            if isinstance(retry_result, dict) and "output" in retry_result:
+                result = retry_result["output"]
+            else:
+                result = str(retry_result)
+
             pydantic_output, json_output = self._export_output(result)
             task_output = TaskOutput(
                 name=self.name or self.description,

lib/crewai/src/crewai/tools/agent_tools/base_agent_tools.py

@@ -126,7 +126,11 @@ class BaseAgentTool(BaseTool):
             logger.debug(
                 f"Created task for agent '{self.sanitize_agent_name(selected_agent.role)}': {task}"
             )
-            return selected_agent.execute_task(task_with_assigned_agent, context)
+            result = selected_agent.execute_task(task_with_assigned_agent, context)
+
+            if isinstance(result, dict) and "output" in result:
+                return result["output"]
+            return str(result)
         except Exception as e:
             # Handle task creation or execution errors
             return self.i18n.errors("agent_tool_execution_error").format(

lib/crewai/src/crewai/utilities/converter.py

@@ -63,7 +63,10 @@ class Converter(OutputConverter):
                     ],
                     response_model=self.model,
                 )
-                result = self.model.model_validate_json(response)
+                if isinstance(response, self.model):
+                    result = response
+                else:
+                    result = self.model.model_validate_json(response)
             else:
                 response = self.llm.call(
                     [

lib/crewai/tests/agents/test_lite_agent.py

@@ -292,7 +292,7 @@ def test_sets_parent_flow_when_inside_flow():
     captured_agent = None
 
     mock_llm = Mock(spec=LLM)
-    mock_llm.call.return_value = "Test response"
+    mock_llm.call.return_value = "Thought: I can answer this\nFinal Answer: Test response"
     mock_llm.stop = []
 
     from crewai.types.usage_metrics import UsageMetrics
@@ -382,7 +382,7 @@ def test_guardrail_is_called_using_string():
     assert not guardrail_events["completed"][0].success
     assert guardrail_events["completed"][1].success
     assert (
-        "Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players"
+        "The top 10 best Brazilian soccer players, based on their current performance, skill, and influence, include"
         in result.raw
     )
 
@@ -508,6 +508,7 @@ def test_agent_output_when_guardrail_returns_base_model():
     assert result.pydantic == Player(name="Lionel Messi", country="Argentina")
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_lite_agent_with_custom_llm_and_guardrails():
     """Test that CustomLLM (inheriting from BaseLLM) works with guardrails."""
 
@@ -529,13 +530,13 @@ def test_lite_agent_with_custom_llm_and_guardrails():
         ) -> str:
             self.call_count += 1
 
+            # Check if this is a guardrail validation call (contains schema for valid/feedback)
             if "valid" in str(messages) and "feedback" in str(messages):
-                return '{"valid": true, "feedback": null}'
+                # Return JSON wrapped in proper ReAct format for guardrail
+                return 'Thought: I need to validate the output\nFinal Answer: {"valid": true, "feedback": null}'
 
-            if "Thought:" in str(messages):
-                return f"Thought: I will analyze soccer players\nFinal Answer: {self.response}"
-
-            return self.response
+            # Default response for main agent execution
+            return f"Thought: I will analyze soccer players\nFinal Answer: {self.response}"
 
         def supports_function_calling(self) -> bool:
             return False
@@ -554,6 +555,8 @@ def test_lite_agent_with_custom_llm_and_guardrails():
         backstory="You analyze soccer players and their performance.",
         llm=custom_llm,
         guardrail="Only include Brazilian players",
+      max_iterations=5,
+      guardrail_max_retries=5,
     )
 
     result = agent.kickoff("Tell me about the best soccer players")
@@ -572,6 +575,8 @@ def test_lite_agent_with_custom_llm_and_guardrails():
         backstory="Test backstory",
         llm=custom_llm2,
         guardrail=test_guardrail,
+      max_iterations=5,
+      guardrail_max_retries=5,
     )
 
     result2 = agent2.kickoff("Test message")
@@ -592,46 +597,44 @@ def test_lite_agent_with_invalid_llm():
         assert "Expected LLM instance of type BaseLLM" in str(exc_info.value)
 
 
-@patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"})
-@patch("crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get")
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_kickoff_with_platform_tools(mock_get):
+def test_agent_kickoff_with_platform_tools():
     """Test that Agent.kickoff() properly integrates platform tools with LiteAgent"""
-    mock_response = Mock()
-    mock_response.raise_for_status.return_value = None
-    mock_response.json.return_value = {
-        "actions": {
-            "github": [
-                {
-                    "name": "create_issue",
-                    "description": "Create a GitHub issue",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "title": {"type": "string", "description": "Issue title"},
-                            "body": {"type": "string", "description": "Issue body"},
+    with patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"}):
+        mock_response = Mock()
+        mock_response.raise_for_status.return_value = None
+        mock_response.json.return_value = {
+            "actions": {
+                "github": [
+                    {
+                        "name": "create_issue",
+                        "description": "Create a GitHub issue",
+                        "parameters": {
+                            "type": "object",
+                            "properties": {
+                                "title": {"type": "string", "description": "Issue title"},
+                                "body": {"type": "string", "description": "Issue body"},
+                            },
+                            "required": ["title"],
                         },
-                        "required": ["title"],
-                    },
-                }
-            ]
+                    }
+                ]
+            }
         }
-    }
-    mock_get.return_value = mock_response
 
-    agent = Agent(
-        role="Test Agent",
-        goal="Test goal",
-        backstory="Test backstory",
-        llm=LLM(model="gpt-3.5-turbo"),
-        apps=["github"],
-        verbose=True
-    )
+        agent = Agent(
+            role="Test Agent",
+            goal="Test goal",
+            backstory="Test backstory",
+            llm=LLM(model="gpt-3.5-turbo"),
+            apps=["github"],
+            verbose=True
+        )
 
-    result = agent.kickoff("Create a GitHub issue")
+        result = agent.kickoff("Create a GitHub issue")
 
-    assert isinstance(result, LiteAgentOutput)
-    assert result.raw is not None
+        assert isinstance(result, LiteAgentOutput)
+        assert result.raw is not None
 
 
 @patch.dict("os.environ", {"EXA_API_KEY": "test_exa_key"})

lib/crewai/tests/cassettes/test_agent_kickoff_with_platform_tools.yaml

@@ -1,30 +1,202 @@
 interactions:
 - request:
-    body: '{"messages": [{"role": "system", "content": "You are Test Agent. Test backstory\nYour
-      personal goal is: Test goal\n\nYou ONLY have access to the following tools,
-      and should NEVER make up tools that are not listed here:\n\nTool Name: create_issue\nTool
-      Arguments: {''title'': {''description'': ''Issue title'', ''type'': ''str''},
-      ''body'': {''description'': ''Issue body'', ''type'': ''Union[str, NoneType]''}}\nTool
-      Description: Create a GitHub issue\nDetailed Parameter Structure:\nObject with
-      properties:\n  - title: Issue title (required)\n  - body: Issue body (optional)\n\nIMPORTANT:
-      Use the following format in your response:\n\n```\nThought: you should always
-      think about what to do\nAction: the action to take, only one name of [create_issue],
-      just the name, exactly as it''s written.\nAction Input: the input to the action,
-      just a simple JSON object, enclosed in curly braces, using \" to wrap keys and
-      values.\nObservation: the result of the action\n```\n\nOnce all necessary information
-      is gathered, return the following format:\n\n```\nThought: I now know the final
-      answer\nFinal Answer: the final answer to the original input question\n```"},
-      {"role": "user", "content": "Create a GitHub issue"}], "model": "gpt-3.5-turbo",
-      "stream": false}'
+    body: null
+    headers:
+      Accept:
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@@ -203,6 +203,7 @@ def clear_event_bus_handlers(setup_test_environment):
     from crewai.experimental.evaluation.evaluation_listener import (
         EvaluationTraceCallback,
     )
+    from crewai.rag.config.utils import clear_rag_config
 
     yield
 
@@ -215,6 +216,9 @@ def clear_event_bus_handlers(setup_test_environment):
     callback.current_agent_id = None
     callback.current_task_id = None
 
+    # Clear RAG config to prevent ChromaDB state from persisting across tests
+    clear_rag_config()
+
 
 @pytest.fixture(scope="module")
 def vcr_config(request) -> dict:

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

@@ -518,7 +518,9 @@ def test_openai_streaming_with_response_model():
         result = llm.call("Test question", response_model=TestResponse)
 
         assert result is not None
-        assert isinstance(result, str)
+        assert isinstance(result, TestResponse)
+        assert result.answer == "test"
+        assert result.confidence == 0.95
 
         assert mock_create.called
         call_kwargs = mock_create.call_args[1]

lib/crewai/tests/test_task.py

@@ -630,7 +630,7 @@ def test_output_json_to_another_task():
 
     crew = Crew(agents=[scorer], tasks=[task1, task2])
     result = crew.kickoff()
-    assert '{"score": 4}' == result.json
+    assert '{"score": 2}' == result.json
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])

lib/crewai/tests/test_task_guardrails.py

@@ -181,7 +181,8 @@ def test_task_guardrail_process_output(task_output):
     result = guardrail(task_output)
     assert result[0] is False
 
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+    # Check that error message indicates word limit violation (wording may vary)
+    assert "10 words" in result[1].lower() or "fewer than" in result[1].lower()
 
     guardrail = LLMGuardrail(
         description="Ensure the result has less than 500 words", llm=LLM(model="gpt-4o")
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             {
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