Merge branch 'main' into lg-guardrail-llm

docs: update task guardrails str docs
2026-10-20 17:05:04 +02:00 · 2025-04-30 11:43:31 -03:00 · 2025-04-30 11:24:29 -03:00 · 2025-04-29 19:01:45 -03:00 · 2025-04-29 18:42:24 -03:00 · 2025-04-29 14:28:21 -03:00
18 changed files with 4935 additions and 1162 deletions
@@ -322,6 +322,10 @@ blog_task = Task(
   - On success: it returns a tuple of `(bool, Any)`. For example: `(True, validated_result)` 
   - On Failure: it returns a tuple of `(bool, str)`. For example: `(False, "Error message explain the failure")`

+### TaskGuardrail
+
+The `TaskGuardrail` class offers a robust mechanism for validating task outputs
+
 ### Error Handling Best Practices

 1. **Structured Error Responses**:
@@ -750,6 +754,8 @@ Task guardrails provide a powerful way to validate, transform, or filter task ou

 ### Basic Usage

+#### Define your own logic to validate
+
 ```python Code
 from typing import Tuple, Union
 from crewai import Task
@@ -769,6 +775,57 @@ task = Task(
 )
 ```

+#### Leverage a no-code approach for validation
+
+```python Code
+from crewai import Task
+
+task = Task(
+    description="Generate JSON data",
+    expected_output="Valid JSON object",
+    guardrail="Ensure the response is a valid JSON object"
+)
+```
+
+#### Using YAML
+
+```yaml
+research_task:
+  ...
+  guardrail: make sure each bullet contains a minimum of 100 words
+  ...
+```
+
+```python Code
+@CrewBase
+class InternalCrew:
+    agents_config = "config/agents.yaml"
+    tasks_config = "config/tasks.yaml"
+
+    ...
+    @task
+    def research_task(self):
+        return Task(config=self.tasks_config["research_task"])  # type: ignore[index]
+    ...
+```
+
+
+#### Use custom models for code generation
+
+```python Code
+from crewai import Task
+from crewai.llm import LLM
+
+task = Task(
+    description="Generate JSON data",
+    expected_output="Valid JSON object",
+    guardrail=TaskGuardrail(
+        description="Ensure the response is a valid JSON object",
+        llm=LLM(model="gpt-4o-mini"),
+    )
+)
+```
+
 ### How Guardrails Work

 1. **Optional Attribute**: Guardrails are an optional attribute at the task level, allowing you to add validation only where needed.
@@ -483,6 +483,7 @@ class LLM(BaseLLM):
                    full_response += chunk_content

                    # Emit the chunk event
+                    assert hasattr(crewai_event_bus, "emit")
                    crewai_event_bus.emit(
                        self,
                        event=LLMStreamChunkEvent(chunk=chunk_content),
@@ -611,6 +612,7 @@ class LLM(BaseLLM):
                return full_response

            # Emit failed event and re-raise the exception
+            assert hasattr(crewai_event_bus, "emit")
            crewai_event_bus.emit(
                self,
                event=LLMCallFailedEvent(error=str(e)),
@@ -633,7 +635,7 @@ class LLM(BaseLLM):
                current_tool_accumulator.function.arguments += (
                    tool_call.function.arguments
                )
-
+            assert hasattr(crewai_event_bus, "emit")
            crewai_event_bus.emit(
                self,
                event=LLMStreamChunkEvent(
@@ -806,6 +808,7 @@ class LLM(BaseLLM):
                    function_name, lambda: None
                )  # Ensure fn is always a callable
                logging.error(f"Error executing function '{function_name}': {e}")
+                assert hasattr(crewai_event_bus, "emit")
                crewai_event_bus.emit(
                    self,
                    event=LLMCallFailedEvent(error=f"Tool execution error: {str(e)}"),
@@ -843,6 +846,7 @@ class LLM(BaseLLM):
            LLMContextLengthExceededException: If input exceeds model's context limit
        """
        # --- 1) Emit call started event
+        assert hasattr(crewai_event_bus, "emit")
        crewai_event_bus.emit(
            self,
            event=LLMCallStartedEvent(
@@ -891,6 +895,7 @@ class LLM(BaseLLM):
                # whether to summarize the content or abort based on the respect_context_window flag
                raise
            except Exception as e:
+                assert hasattr(crewai_event_bus, "emit")
                crewai_event_bus.emit(
                    self,
                    event=LLMCallFailedEvent(error=str(e)),
@@ -905,6 +910,7 @@ class LLM(BaseLLM):
            response (str): The response from the LLM call.
            call_type (str): The type of call, either "tool_call" or "llm_call".
        """
+        assert hasattr(crewai_event_bus, "emit")
        crewai_event_bus.emit(
            self,
            event=LLMCallCompletedEvent(response=response, call_type=call_type),
@@ -246,6 +246,9 @@ def CrewBase(cls: T) -> T:
                    callback_functions[callback]() for callback in callbacks
                ]

+            if guardrail := task_info.get("guardrail"):
+                self.tasks_config[task_name]["guardrail"] = guardrail
+
    # Include base class (qual)name in the wrapper class (qual)name.
    WrappedClass.__name__ = CrewBase.__name__ + "(" + cls.__name__ + ")"
    WrappedClass.__qualname__ = CrewBase.__qualname__ + "(" + cls.__name__ + ")"
@@ -140,9 +140,9 @@ class Task(BaseModel):
        default=None,
    )
    processed_by_agents: Set[str] = Field(default_factory=set)
-    guardrail: Optional[Callable[[TaskOutput], Tuple[bool, Any]]] = Field(
+    guardrail: Optional[Union[Callable[[TaskOutput], Tuple[bool, Any]], str]] = Field(
        default=None,
-        description="Function to validate task output before proceeding to next task",
+        description="Function or string description of a guardrail to validate task output before proceeding to next task",
    )
    max_retries: int = Field(
        default=3, description="Maximum number of retries when guardrail fails"
@@ -157,8 +157,12 @@ class Task(BaseModel):

    @field_validator("guardrail")
    @classmethod
-    def validate_guardrail_function(cls, v: Optional[Callable]) -> Optional[Callable]:
-        """Validate that the guardrail function has the correct signature and behavior.
+    def validate_guardrail_function(
+        cls, v: Optional[str | Callable]
+    ) -> Optional[str | Callable]:
+        """
+        If v is a callable, validate that the guardrail function has the correct signature and behavior.
+        If v is a string, return it as is.

        While type hints provide static checking, this validator ensures runtime safety by:
        1. Verifying the function accepts exactly one parameter (the TaskOutput)
@@ -171,16 +175,16 @@ class Task(BaseModel):
        - Clear error messages help users debug guardrail implementation issues

        Args:
-            v: The guardrail function to validate
+            v: The guardrail function to validate or a string describing the guardrail task

        Returns:
-            The validated guardrail function
+            The validated guardrail function or a string describing the guardrail task

        Raises:
            ValueError: If the function signature is invalid or return annotation
                       doesn't match Tuple[bool, Any]
        """
-        if v is not None:
+        if v is not None and callable(v):
            sig = inspect.signature(v)
            positional_args = [
                param
@@ -211,6 +215,7 @@ class Task(BaseModel):
                    )
        return v

+    _guardrail: Optional[Callable] = PrivateAttr(default=None)
    _original_description: Optional[str] = PrivateAttr(default=None)
    _original_expected_output: Optional[str] = PrivateAttr(default=None)
    _original_output_file: Optional[str] = PrivateAttr(default=None)
@@ -231,6 +236,20 @@ class Task(BaseModel):
                )
        return self

+    @model_validator(mode="after")
+    def ensure_guardrail_is_callable(self) -> "Task":
+        if callable(self.guardrail):
+            self._guardrail = self.guardrail
+        elif isinstance(self.guardrail, str):
+            from crewai.tasks.task_guardrail import TaskGuardrail
+
+            assert self.agent is not None
+            self._guardrail = TaskGuardrail(
+                description=self.guardrail, llm=self.agent.llm
+            )
+
+        return self
+
    @field_validator("id", mode="before")
    @classmethod
    def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
@@ -407,10 +426,8 @@ class Task(BaseModel):
                output_format=self._get_output_format(),
            )

-            if self.guardrail:
-                guardrail_result = GuardrailResult.from_tuple(
-                    self.guardrail(task_output)
-                )
+            if self._guardrail:
+                guardrail_result = self._process_guardrail(task_output)
                if not guardrail_result.success:
                    if self.retry_count >= self.max_retries:
                        raise Exception(
@@ -464,13 +481,46 @@ class Task(BaseModel):
                    )
                )
                self._save_file(content)
-            crewai_event_bus.emit(self, TaskCompletedEvent(output=task_output, task=self))
+            crewai_event_bus.emit(
+                self, TaskCompletedEvent(output=task_output, task=self)
+            )
            return task_output
        except Exception as e:
            self.end_time = datetime.datetime.now()
            crewai_event_bus.emit(self, TaskFailedEvent(error=str(e), task=self))
            raise e  # Re-raise the exception after emitting the event

+    def _process_guardrail(self, task_output: TaskOutput) -> GuardrailResult:
+        assert self._guardrail is not None
+
+        from crewai.utilities.events import (
+            TaskGuardrailCompletedEvent,
+            TaskGuardrailStartedEvent,
+        )
+        from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+
+        result = self._guardrail(task_output)
+
+        crewai_event_bus.emit(
+            self,
+            TaskGuardrailStartedEvent(
+                guardrail=self._guardrail, retry_count=self.retry_count
+            ),
+        )
+
+        guardrail_result = GuardrailResult.from_tuple(result)
+
+        crewai_event_bus.emit(
+            self,
+            TaskGuardrailCompletedEvent(
+                success=guardrail_result.success,
+                result=guardrail_result.result,
+                error=guardrail_result.error,
+                retry_count=self.retry_count,
+            ),
+        )
+        return guardrail_result
+
    def prompt(self) -> str:
        """Prompt the task.

@@ -0,0 +1,92 @@
+from typing import Any, Optional, Tuple
+
+from pydantic import BaseModel, Field
+
+from crewai.agent import Agent, LiteAgentOutput
+from crewai.llm import LLM
+from crewai.task import Task
+from crewai.tasks.task_output import TaskOutput
+
+
+class TaskGuardrailResult(BaseModel):
+    valid: bool = Field(
+        description="Whether the task output complies with the guardrail"
+    )
+    feedback: str | None = Field(
+        description="A feedback about the task output if it is not valid",
+        default=None,
+    )
+
+
+class TaskGuardrail:
+    """It validates the output of another task using an LLM.
+
+    This class is used to validate the output from a Task based on specified criteria.
+    It uses an LLM to validate the output and provides a feedback if the output is not valid.
+
+    Args:
+        description (str): The description of the validation criteria.
+        llm (LLM, optional): The language model to use for code generation.
+    """
+
+    def __init__(
+        self,
+        description: str,
+        llm: LLM,
+    ):
+        self.description = description
+
+        self.llm: LLM = llm
+
+    def _validate_output(self, task_output: TaskOutput) -> LiteAgentOutput:
+        agent = Agent(
+            role="Guardrail Agent",
+            goal="Validate the output of the task",
+            backstory="You are a expert at validating the output of a task. By providing effective feedback if the output is not valid.",
+            llm=self.llm,
+        )
+
+        query = f"""
+        Ensure the following task result complies with the given guardrail.
+
+        Task result:
+        {task_output.raw}
+
+        Guardrail:
+        {self.description}
+        
+        Your task:
+        - Confirm if the Task result complies with the guardrail.
+        - If not, provide clear feedback explaining what is wrong (e.g., by how much it violates the rule, or what specific part fails).
+        - Focus only on identifying issues — do not propose corrections.
+        - If the Task result complies with the guardrail, saying that is valid
+        """
+
+        result = agent.kickoff(query, response_format=TaskGuardrailResult)
+
+        return result
+
+    def __call__(self, task_output: TaskOutput) -> Tuple[bool, Any]:
+        """Validates the output of a task based on specified criteria.
+
+        Args:
+            task_output (TaskOutput): The output to be validated.
+
+        Returns:
+            Tuple[bool, Any]: A tuple containing:
+                - bool: True if validation passed, False otherwise
+                - Any: The validation result or error message
+        """
+
+        try:
+            result = self._validate_output(task_output)
+            assert isinstance(
+                result.pydantic, TaskGuardrailResult
+            ), "The guardrail result is not a valid pydantic model"
+
+            if result.pydantic.valid:
+                return True, task_output.raw
+            else:
+                return False, result.pydantic.feedback
+        except Exception as e:
+            return False, f"Error while validating the task output: {str(e)}"
@@ -9,6 +9,10 @@ from .crew_events import (
    CrewTestCompletedEvent,
    CrewTestFailedEvent,
 )
+from .task_guardrail_events import (
+    TaskGuardrailCompletedEvent,
+    TaskGuardrailStartedEvent,
+)
 from .agent_events import (
    AgentExecutionStartedEvent,
    AgentExecutionCompletedEvent,
@@ -34,6 +34,10 @@ from .task_events import (
    TaskFailedEvent,
    TaskStartedEvent,
 )
+from .task_guardrail_events import (
+    TaskGuardrailCompletedEvent,
+    TaskGuardrailStartedEvent,
+)
 from .tool_usage_events import (
    ToolUsageErrorEvent,
    ToolUsageFinishedEvent,
@@ -68,4 +72,6 @@ EventTypes = Union[
    LLMCallCompletedEvent,
    LLMCallFailedEvent,
    LLMStreamChunkEvent,
+    TaskGuardrailStartedEvent,
+    TaskGuardrailCompletedEvent,
 ]
@@ -0,0 +1,38 @@
+from typing import Any, Callable, Optional, Union
+
+from crewai.utilities.events.base_events import BaseEvent
+
+
+class TaskGuardrailStartedEvent(BaseEvent):
+    """Event emitted when a guardrail task starts
+
+    Attributes:
+        guardrail: The guardrail callable or TaskGuardrail instance
+        retry_count: The number of times the guardrail has been retried
+    """
+
+    type: str = "task_guardrail_started"
+    guardrail: Union[str, Callable]
+    retry_count: int
+
+    def __init__(self, **data):
+        from inspect import getsource
+
+        from crewai.tasks.task_guardrail import TaskGuardrail
+
+        super().__init__(**data)
+
+        if isinstance(self.guardrail, TaskGuardrail):
+            self.guardrail = self.guardrail.description.strip()
+        elif isinstance(self.guardrail, Callable):
+            self.guardrail = getsource(self.guardrail).strip()
+
+
+class TaskGuardrailCompletedEvent(BaseEvent):
+    """Event emitted when a guardrail task completes"""
+
+    type: str = "task_guardrail_completed"
+    success: bool
+    result: Any
+    error: Optional[str] = None
+    retry_count: int
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+      **\"Goodbye to All That\" by Robert Graves** - An autobiography that captures
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@@ -6,6 +6,7 @@ research_task:
  expected_output: >
    A list with 10 bullet points of the most relevant information about {topic}
  agent: researcher
+  guardrail: ensure each bullet contains its source

 reporting_task:
  description: >
@@ -1,4 +1,5 @@
 from typing import List
+from unittest.mock import patch

 import pytest

@@ -142,6 +143,15 @@ def test_agent_function_calling_llm():
    ), "agent's function_calling_llm is incorrect"


+def test_task_guardrail():
+    crew = InternalCrew()
+    research_task = crew.research_task()
+    assert research_task.guardrail == "ensure each bullet contains its source"
+
+    reporting_task = crew.reporting_task()
+    assert reporting_task.guardrail is None
+
+
@pytest.mark.vcr(filter_headers=["authorization"])
 def test_before_kickoff_modification():
    crew = InternalCrew()
@@ -1,11 +1,16 @@
-"""Tests for task guardrails functionality."""
-
-from unittest.mock import Mock
+from unittest.mock import ANY, Mock, patch

 import pytest

-from crewai.task import Task
+from crewai import Agent, Task
+from crewai.llm import LLM
+from crewai.tasks.task_guardrail import TaskGuardrail
 from crewai.tasks.task_output import TaskOutput
+from crewai.utilities.events import (
+    TaskGuardrailCompletedEvent,
+    TaskGuardrailStartedEvent,
+)
+from crewai.utilities.events.crewai_event_bus import crewai_event_bus


 def test_task_without_guardrail():
@@ -22,7 +27,7 @@ def test_task_without_guardrail():
    assert result.raw == "test result"


-def test_task_with_successful_guardrail():
+def test_task_with_successful_guardrail_func():
    """Test that successful guardrail validation passes transformed result."""

    def guardrail(result: TaskOutput):
@@ -127,3 +132,138 @@ def test_guardrail_error_in_context():

    assert "Task failed guardrail validation" in str(exc_info.value)
    assert "Expected JSON, got string" in str(exc_info.value)
+
+
+@pytest.fixture
+def sample_agent():
+    return Agent(role="Test Agent", goal="Test Goal", backstory="Test Backstory")
+
+
+@pytest.fixture
+def task_output():
+    return TaskOutput(
+        raw="""
+        Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever
+        """,
+        description="Test task",
+        expected_output="Output",
+        agent="Test Agent",
+    )
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_task_guardrail_process_output(task_output):
+    guardrail = TaskGuardrail(
+        description="Ensure the result has less than 10 words", llm=LLM(model="gpt-4o")
+    )
+
+    result = guardrail(task_output)
+    assert result[0] is False
+
+    assert "exceeding the guardrail limit of fewer than" in result[1].lower()
+
+    guardrail = TaskGuardrail(
+        description="Ensure the result has less than 500 words", llm=LLM(model="gpt-4o")
+    )
+
+    result = guardrail(task_output)
+    assert result[0] is True
+    assert result[1] == task_output.raw
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_guardrail_emits_events(sample_agent):
+    started_guardrail = []
+    completed_guardrail = []
+
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(TaskGuardrailStartedEvent)
+        def handle_guardrail_started(source, event):
+            started_guardrail.append(
+                {"guardrail": event.guardrail, "retry_count": event.retry_count}
+            )
+
+        @crewai_event_bus.on(TaskGuardrailCompletedEvent)
+        def handle_guardrail_completed(source, event):
+            completed_guardrail.append(
+                {
+                    "success": event.success,
+                    "result": event.result,
+                    "error": event.error,
+                    "retry_count": event.retry_count,
+                }
+            )
+
+        task = Task(
+            description="Gather information about available books on the First World War",
+            agent=sample_agent,
+            expected_output="A list of available books on the First World War",
+            guardrail="Ensure the authors are from Italy",
+        )
+
+        result = task.execute_sync(agent=sample_agent)
+
+        def custom_guardrail(result: TaskOutput):
+            return (True, "good result from callable function")
+
+        task = Task(
+            description="Test task",
+            expected_output="Output",
+            guardrail=custom_guardrail,
+        )
+
+        task.execute_sync(agent=sample_agent)
+
+        expected_started_events = [
+            {"guardrail": "Ensure the authors are from Italy", "retry_count": 0},
+            {"guardrail": "Ensure the authors are from Italy", "retry_count": 1},
+            {
+                "guardrail": """def custom_guardrail(result: TaskOutput):
+            return (True, "good result from callable function")""",
+                "retry_count": 0,
+            },
+        ]
+
+        expected_completed_events = [
+            {
+                "success": False,
+                "result": None,
+                "error": "The task result does not comply with the guardrail because none of "
+                "the listed authors are from Italy. All authors mentioned are from "
+                "different countries, including Germany, the UK, the USA, and others, "
+                "which violates the requirement that authors must be Italian.",
+                "retry_count": 0,
+            },
+            {"success": True, "result": result.raw, "error": None, "retry_count": 1},
+            {
+                "success": True,
+                "result": "good result from callable function",
+                "error": None,
+                "retry_count": 0,
+            },
+        ]
+        assert started_guardrail == expected_started_events
+        assert completed_guardrail == expected_completed_events
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_guardrail_when_an_error_occurs(sample_agent, task_output):
+    with (
+        patch(
+            "crewai.Agent.kickoff",
+            side_effect=Exception("Unexpected error"),
+        ),
+        pytest.raises(
+            Exception,
+            match="Error while validating the task output: Unexpected error",
+        ),
+    ):
+        task = Task(
+            description="Gather information about available books on the First World War",
+            agent=sample_agent,
+            expected_output="A list of available books on the First World War",
+            guardrail="Ensure the authors are from Italy",
+            max_retries=0,
+        )
+        task.execute_sync(agent=sample_agent)
Author	SHA1	Message	Date
Lucas Gomide	a3652ca120	Merge branch 'main' into lg-guardrail-llm	2025-04-30 11:43:31 -03:00
Lucas Gomide	4f6ab1f579	docs: update task guardrails str docs	2025-04-30 11:24:29 -03:00
Lucas Gomide	77eb69a24a	Merge branch 'main' into lg-guardrail-llm	2025-04-29 19:01:45 -03:00
Lucas Gomide	27952cfb7a	refactor: drop task paramenter from TaskGuardrail This parameter was used to get the model from the `task.agent` which is a quite bit redudant since we could propagate the llm directly	2025-04-29 18:42:24 -03:00
Lucas Gomide	922a7ba9bd	docs: update TaskGuardrail doc strings	2025-04-29 14:28:21 -03:00
Lucas Gomide	f42491fad0	Merge branch 'main' into lg-guardrail-llm	2025-04-29 11:01:20 -03:00
Lucas Gomide	e940ff3cbd	refactor: simplify TaskGuardrail to use LLM for validation, no code generation	2025-04-29 10:43:16 -03:00
Lucas Gomide	e3ab80f517	Merge branch 'main' into lg-guardrail-llm	2025-04-25 11:47:24 -03:00
Lucas Gomide	50b603d3d2	feat: support to define a task guardrail using YAML config	2025-04-25 11:43:45 -03:00
Lucas Gomide	e5d0cc8ac1	fix: attempt to fix type-checker	2025-04-23 17:02:34 -03:00
Lucas Gomide	b6817b601d	test: remove useless or duplicated test	2025-04-23 16:51:38 -03:00
Lucas Gomide	4f61de8e08	refactor: replace if/raise with assert For this use case `assert` is more appropriate choice	2025-04-23 16:51:38 -03:00
Lucas Gomide	098a9ba519	feat: remove Docker availability check from TaskGuardrail The CodeInterpreterTool already ensures compliance with this requirement.	2025-04-23 16:51:38 -03:00
Lucas Gomide	885c1d40b7	feat: ensure guardrail is callable while initializing Task	2025-04-23 16:51:38 -03:00
Lucas Gomide	05e99bdfe5	feat: renaming GuardrailTask to TaskGuardrail	2025-04-23 16:51:38 -03:00
Lucas Gomide	0ed683241d	feat: allow to set unsafe_mode from Guardrail task	2025-04-23 16:51:38 -03:00
Lucas Gomide	09543cd705	feat: handle malformed or invalid response from CodeInterpreterTool	2025-04-23 16:51:38 -03:00
Lucas Gomide	50453c6984	feat: add auto-discovery for Guardrail code execution mode	2025-04-23 16:51:38 -03:00
Lucas Gomide	91b618b4e0	feat: support to define a guardrail task no-code	2025-04-23 16:51:38 -03:00