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fix: run last llm call as structured output if passed

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This commit is contained in:
Greyson Lalonde
2025-11-05 07:17:23 -05:00
parent 7380f7b794
commit 07ac8fb088
41 changed files with 17859 additions and 16272 deletions
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29
lib/crewai/src/crewai/agent/core.py
View File

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

4
lib/crewai/src/crewai/agents/agent_adapters/langgraph/langgraph_adapter.py
View File

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

2
lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py
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@@ -106,7 +106,7 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
task: Any, task: Any,
context: str | None = None, context: str | None = None,
tools: list[BaseTool] | None = None, tools: list[BaseTool] | None = None,
) -> str: ) -> str | dict[str, Any]:
"""Execute a task using the OpenAI Assistant. """Execute a task using the OpenAI Assistant.
Configures the assistant, processes the task, and handles event emission Configures the assistant, processes the task, and handles event emission

2
lib/crewai/src/crewai/agents/agent_builder/base_agent.py
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@@ -327,7 +327,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
task: Any, task: Any,
context: str | None = None, context: str | None = None,
tools: list[BaseTool] | None = None, tools: list[BaseTool] | None = None,
) -> str: ) -> str | dict[str, Any]:
pass pass
@abstractmethod @abstractmethod

23
lib/crewai/src/crewai/agents/crew_agent_executor.py
View File

@@ -195,7 +195,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self._create_short_term_memory(formatted_answer) self._create_short_term_memory(formatted_answer)
self._create_long_term_memory(formatted_answer) self._create_long_term_memory(formatted_answer)
self._create_external_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: def _invoke_loop(self) -> AgentFinish:
"""Execute agent loop until completion. """Execute agent loop until completion.
@@ -301,6 +301,27 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
"Agent execution ended without reaching a final answer. " "Agent execution ended without reaching a final answer. "
f"Got {type(formatted_answer).__name__} instead of AgentFinish." 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) self._show_logs(formatted_answer)
return formatted_answer return formatted_answer

17
lib/crewai/src/crewai/lite_agent.py
View File

@@ -305,8 +305,15 @@ class LiteAgent(FlowTrackable, BaseModel):
formatted_result: BaseModel | None = None formatted_result: BaseModel | None = None
if self.response_format: if self.response_format:
try: try:
# Cast to BaseModel to ensure type safety if (
result = self.response_format.model_validate_json(agent_finish.output) 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): if isinstance(result, BaseModel):
formatted_result = result formatted_result = result
except Exception as e: except Exception as e:
@@ -476,13 +483,17 @@ class LiteAgent(FlowTrackable, BaseModel):
enforce_rpm_limit(self.request_within_rpm_limit) enforce_rpm_limit(self.request_within_rpm_limit)
try: try:
use_response_model = (
self.response_format if not self._parsed_tools else None
)
answer = get_llm_response( answer = get_llm_response(
llm=cast(LLM, self.llm), llm=cast(LLM, self.llm),
messages=self._messages, messages=self._messages,
callbacks=self._callbacks, callbacks=self._callbacks,
printer=self._printer, printer=self._printer,
from_agent=self, from_agent=self,
response_model=self.response_format, response_model=use_response_model,
) )
except Exception as e: except Exception as e:

10
lib/crewai/src/crewai/llms/providers/openai/completion.py
View File

@@ -316,6 +316,16 @@ class OpenAICompletion(BaseLLM):
) )
return parsed_object 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) response: ChatCompletion = self.client.chat.completions.create(**params)
usage = self._extract_openai_token_usage(response) usage = self._extract_openai_token_usage(response)

42
lib/crewai/src/crewai/task.py
View File

@@ -12,6 +12,7 @@ import threading
from typing import ( from typing import (
Any, Any,
ClassVar, ClassVar,
TypedDict,
cast, cast,
get_args, get_args,
get_origin, get_origin,
@@ -31,6 +32,7 @@ from pydantic_core import PydanticCustomError
from typing_extensions import Self from typing_extensions import Self
from crewai.agents.agent_builder.base_agent import BaseAgent 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.event_bus import crewai_event_bus
from crewai.events.types.task_events import ( from crewai.events.types.task_events import (
TaskCompletedEvent, TaskCompletedEvent,
@@ -60,6 +62,18 @@ from crewai.utilities.string_utils import interpolate_only
_printer = Printer() _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 Task(BaseModel):
"""Class that represents a task to be executed. """Class that represents a task to be executed.
@@ -519,13 +533,32 @@ class Task(BaseModel):
self.processed_by_agents.add(agent.role) self.processed_by_agents.add(agent.role)
crewai_event_bus.emit(self, TaskStartedEvent(context=context, task=self)) # type: ignore[no-untyped-call] crewai_event_bus.emit(self, TaskStartedEvent(context=context, task=self)) # type: ignore[no-untyped-call]
result = agent.execute_task( executor_result = cast(
str | ExecutorResult,
agent.execute_task(
task=self, task=self,
context=context, context=context,
tools=tools, tools=tools,
),
) )
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) 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( task_output = TaskOutput(
name=self.name or self.description, name=self.name or self.description,
description=self.description, description=self.description,
@@ -929,12 +962,17 @@ Follow these guidelines:
) )
# Regenerate output from agent # Regenerate output from agent
result = agent.execute_task( retry_result = agent.execute_task(
task=self, task=self,
context=context, context=context,
tools=tools, 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) pydantic_output, json_output = self._export_output(result)
task_output = TaskOutput( task_output = TaskOutput(
name=self.name or self.description, name=self.name or self.description,

6
lib/crewai/src/crewai/tools/agent_tools/base_agent_tools.py
View File

@@ -126,7 +126,11 @@ class BaseAgentTool(BaseTool):
logger.debug( logger.debug(
f"Created task for agent '{self.sanitize_agent_name(selected_agent.role)}': {task}" 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: except Exception as e:
# Handle task creation or execution errors # Handle task creation or execution errors
return self.i18n.errors("agent_tool_execution_error").format( return self.i18n.errors("agent_tool_execution_error").format(

3
lib/crewai/src/crewai/utilities/converter.py
View File

@@ -63,6 +63,9 @@ class Converter(OutputConverter):
], ],
response_model=self.model, response_model=self.model,
) )
if isinstance(response, self.model):
result = response
else:
result = self.model.model_validate_json(response) result = self.model.model_validate_json(response)
else: else:
response = self.llm.call( response = self.llm.call(

23
lib/crewai/tests/agents/test_lite_agent.py
View File

@@ -292,7 +292,7 @@ def test_sets_parent_flow_when_inside_flow():
captured_agent = None captured_agent = None
mock_llm = Mock(spec=LLM) 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 = [] mock_llm.stop = []
from crewai.types.usage_metrics import UsageMetrics 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 not guardrail_events["completed"][0].success
assert guardrail_events["completed"][1].success assert guardrail_events["completed"][1].success
assert ( 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 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") assert result.pydantic == Player(name="Lionel Messi", country="Argentina")
@pytest.mark.vcr(filter_headers=["authorization"])
def test_lite_agent_with_custom_llm_and_guardrails(): def test_lite_agent_with_custom_llm_and_guardrails():
"""Test that CustomLLM (inheriting from BaseLLM) works with guardrails.""" """Test that CustomLLM (inheriting from BaseLLM) works with guardrails."""
@@ -529,14 +530,14 @@ def test_lite_agent_with_custom_llm_and_guardrails():
) -> str: ) -> str:
self.call_count += 1 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): 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): # Default response for main agent execution
return f"Thought: I will analyze soccer players\nFinal Answer: {self.response}" return f"Thought: I will analyze soccer players\nFinal Answer: {self.response}"
return self.response
def supports_function_calling(self) -> bool: def supports_function_calling(self) -> bool:
return False return False
@@ -554,6 +555,8 @@ def test_lite_agent_with_custom_llm_and_guardrails():
backstory="You analyze soccer players and their performance.", backstory="You analyze soccer players and their performance.",
llm=custom_llm, llm=custom_llm,
guardrail="Only include Brazilian players", guardrail="Only include Brazilian players",
max_iterations=5,
guardrail_max_retries=5,
) )
result = agent.kickoff("Tell me about the best soccer players") 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", backstory="Test backstory",
llm=custom_llm2, llm=custom_llm2,
guardrail=test_guardrail, guardrail=test_guardrail,
max_iterations=5,
guardrail_max_retries=5,
) )
result2 = agent2.kickoff("Test message") result2 = agent2.kickoff("Test message")
@@ -592,11 +597,10 @@ def test_lite_agent_with_invalid_llm():
assert "Expected LLM instance of type BaseLLM" in str(exc_info.value) 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"]) @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""" """Test that Agent.kickoff() properly integrates platform tools with LiteAgent"""
with patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"}):
mock_response = Mock() mock_response = Mock()
mock_response.raise_for_status.return_value = None mock_response.raise_for_status.return_value = None
mock_response.json.return_value = { mock_response.json.return_value = {
@@ -617,7 +621,6 @@ def test_agent_kickoff_with_platform_tools(mock_get):
] ]
} }
} }
mock_get.return_value = mock_response
agent = Agent( agent = Agent(
role="Test Agent", role="Test Agent",

2469
lib/crewai/tests/cassettes/test_after_kickoff_modification.yaml
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1925
lib/crewai/tests/cassettes/test_agent_function_calling_llm.yaml
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267
lib/crewai/tests/cassettes/test_agent_kickoff_with_platform_tools.yaml
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@@ -1,30 +1,202 @@
interactions: interactions:
- request: - request:
body: '{"messages": [{"role": "system", "content": "You are Test Agent. Test backstory\nYour body: null
personal goal is: Test goal\n\nYou ONLY have access to the following tools, headers:
and should NEVER make up tools that are not listed here:\n\nTool Name: create_issue\nTool Accept:
Arguments: {''title'': {''description'': ''Issue title'', ''type'': ''str''}, - '*/*'
''body'': {''description'': ''Issue body'', ''type'': ''Union[str, NoneType]''}}\nTool Accept-Encoding:
Description: Create a GitHub issue\nDetailed Parameter Structure:\nObject with - gzip, deflate, zstd
properties:\n - title: Issue title (required)\n - body: Issue body (optional)\n\nIMPORTANT: Connection:
Use the following format in your response:\n\n```\nThought: you should always - keep-alive
think about what to do\nAction: the action to take, only one name of [create_issue], User-Agent:
just the name, exactly as it''s written.\nAction Input: the input to the action, - python-requests/2.32.5
just a simple JSON object, enclosed in curly braces, using \" to wrap keys and method: GET
values.\nObservation: the result of the action\n```\n\nOnce all necessary information uri: https://app.crewai.com/crewai_plus/api/v1/integrations/actions?apps=github
is gathered, return the following format:\n\n```\nThought: I now know the final response:
answer\nFinal Answer: the final answer to the original input question\n```"}, body:
{"role": "user", "content": "Create a GitHub issue"}], "model": "gpt-3.5-turbo", string: '{"error":"Invalid API key"}'
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lib/crewai/tests/conftest.py
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4
lib/crewai/tests/llms/openai/test_openai.py
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@@ -518,7 +518,9 @@ def test_openai_streaming_with_response_model():
result = llm.call("Test question", response_model=TestResponse) result = llm.call("Test question", response_model=TestResponse)
assert result is not None 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 assert mock_create.called
call_kwargs = mock_create.call_args[1] call_kwargs = mock_create.call_args[1]

2
lib/crewai/tests/test_task.py
View File

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

8
lib/crewai/tests/test_task_guardrails.py
View File

@@ -181,7 +181,8 @@ def test_task_guardrail_process_output(task_output):
result = guardrail(task_output) result = guardrail(task_output)
assert result[0] is False assert result[0] is False
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assert "10 words" in result[1].lower() or "fewer than" in result[1].lower()
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"result": None, "result": None,
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308
lib/crewai/tests/utilities/cassettes/test_convert_with_instructions.yaml
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