fix(agent): keep task output models out of tool loop

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

@@ -1053,11 +1053,7 @@ def create_agent_executor(
                 respect_context_window=self.respect_context_window,
                 request_within_rpm_limit=rpm_limit_fn,
                 callbacks=[TokenCalcHandler(self._token_process)],
-                response_model=(
-                    task.response_model or task.output_pydantic or task.output_json
-                )
-                if task
-                else None,
+                response_model=task.response_model if task else None,
             )
 
     def _update_executor_parameters(
@@ -1093,11 +1089,7 @@ def _update_executor_parameters(
             self.agent_executor.stop = stop_words
         self.agent_executor.tools_names = get_tool_names(tools)
         self.agent_executor.tools_description = render_text_description_and_args(tools)
-        self.agent_executor.response_model = (
-            (task.response_model or task.output_pydantic or task.output_json)
-            if task
-            else None
-        )
+        self.agent_executor.response_model = task.response_model if task else None
 
         self.agent_executor.tools_handler = self.tools_handler
         self.agent_executor.request_within_rpm_limit = rpm_limit_fn

lib/crewai/tests/agents/test_agent.py

@@ -5,6 +5,9 @@
 from unittest import mock
 from unittest.mock import MagicMock, patch
 
+import pytest
+from pydantic import BaseModel
+
 from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
 from crewai.cli.constants import DEFAULT_LLM_MODEL
 from crewai.events.event_bus import crewai_event_bus
@@ -19,7 +22,6 @@
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
 from crewai.utilities.errors import AgentRepositoryError
-import pytest
 
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
@@ -799,6 +801,49 @@ def test_using_system_prompt():
     assert agent.agent_executor.prompt.get("system")
 
 
+def test_output_models_do_not_become_executor_response_model():
+    """output_pydantic/json should be post-processed after the tool loop."""
+
+    class ScoreOutput(BaseModel):
+        score: int
+
+    class NativeOutput(BaseModel):
+        answer: str
+
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+    )
+
+    pydantic_task = Task(
+        description="Return a score",
+        expected_output="A score",
+        output_pydantic=ScoreOutput,
+        agent=agent,
+    )
+    agent.create_agent_executor(task=pydantic_task)
+    assert agent.agent_executor.response_model is None
+
+    native_task = Task(
+        description="Return a native structured answer",
+        expected_output="A structured answer",
+        response_model=NativeOutput,
+        agent=agent,
+    )
+    agent.create_agent_executor(task=native_task)
+    assert agent.agent_executor.response_model is NativeOutput
+
+    json_task = Task(
+        description="Return JSON",
+        expected_output="A JSON score",
+        output_json=ScoreOutput,
+        agent=agent,
+    )
+    agent.create_agent_executor(task=json_task)
+    assert agent.agent_executor.response_model is None
+
+
 def test_system_and_prompt_template():
     agent = Agent(
         role="{topic} specialist",