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refactor: simplify TaskGuardrail to use LLM for validation, no code generation

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This commit is contained in:
Lucas Gomide
2025-04-29 09:50:03 -03:00
parent e3ab80f517
commit e940ff3cbd
14 changed files with 3883 additions and 4217 deletions
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8
docs/concepts/tasks.mdx
View File

@@ -324,11 +324,7 @@ blog_task = Task(
### TaskGuardrail
The `TaskGuardrail` class provides a sophisticated way to generate and execute validation code for task outputs. Here's how it works:
#### Code Execution
The generated code can be executed in two ways: Docker container (Default, Recommended) or current environment (unsafe mode)
The `TaskGuardrail` class offers a robust mechanism for validating task outputs
### Error Handling Best Practices
@@ -791,7 +787,7 @@ task = Task(
)
```
##### Using YAML
#### Using YAML
```yaml
research_task:

159
src/crewai/tasks/task_guardrail.py
View File

@@ -1,39 +1,40 @@
from typing import Any, Tuple
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
from crewai.utilities.printer import Printer
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:
"""A task that validates the output of another task using generated Python code.
This class generates and executes Python code to validate task outputs based on
specified criteria. It uses an LLM to generate the validation code and provides
safety guardrails for code execution.
The code is executed in a Docker container if available, otherwise it is executed in a sandboxed environment.
If unsafe mode is enabled, the code is executed in the current environment.
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.
task (Task, optional): The task whose output needs validation.
llm (LLM, optional): The language model to use for code generation.
additional_instructions (str, optional): Additional instructions for the guardrail task.
unsafe_mode (bool, optional): Whether to run the code in unsafe mode.
Raises:
ValueError: If no valid LLM is provided.
"""
generated_code: str = ""
def __init__(
self,
description: str,
task: Task | None = None,
llm: LLM | None = None,
additional_instructions: str = "",
unsafe_mode: bool = False,
):
self.description = description
@@ -47,84 +48,36 @@ class TaskGuardrail:
)
self.llm: LLM | None = llm or fallback_llm
self.additional_instructions = additional_instructions
self.unsafe_mode = unsafe_mode
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,
)
@property
def system_instructions(self) -> str:
"""System instructions for the LLM code generation.
query = f"""
Ensure the following task result complies with the given guardrail.
Returns:
str: Complete system instructions including security constraints.
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
"""
security_instructions = (
"- DO NOT wrap the output in markdown or use triple backticks. Return only raw Python code."
"- DO NOT use `exec`, `eval`, `compile`, `open`, `os`, `subprocess`, `socket`, `shutil`, or any other system-level modules.\n"
"- Your code must not perform any file I/O, shell access, or dynamic code execution."
)
return (
"You are a expert Python developer"
"You **must strictly** follow the task description, use the provided raw output as the input in your code. "
"Your code must:\n"
"- Return results with: print((True, data)) on success, or print((False, 'very detailed error message')) on failure. Make sure the final output is being assined to 'result' variable.\n"
"- Use the literal string of the task output (already included in your input) if needed.\n"
"- Generate the code **following strictly** the task description.\n"
"- Be valid Python 3 — executable as-is.\n"
f"{security_instructions}\n"
"Additional instructions (do not override the previous instructions):\n"
f"{self.additional_instructions}"
)
def user_instructions(self, task_output: TaskOutput) -> str:
"""Generates user instructions for the LLM code generation.
result = agent.kickoff(query, response_format=TaskGuardrailResult)
Args:
task_output (TaskOutput): The output to be validated.
Returns:
str: Instructions for generating validation code.
"""
return (
"Based on the task description below, generate Python 3 code that validates the task output. \n"
"Task description:\n"
f"{self.description}\n"
"Here is the raw output from the task: \n"
f"'{task_output.raw}' \n"
"Use this exact string literal inside your generated code (do not reference variables like task_output.raw)."
"Now generate Python code that follows the instructions above."
)
def generate_code(self, task_output: TaskOutput) -> str:
"""Generates Python code for validating the task output.
Args:
task_output (TaskOutput): The output to be validated.
"""
if self.llm is None:
raise ValueError("Provide a valid LLM to the TaskGuardrail")
response = self.llm.call(
messages=[
{
"role": "system",
"content": self.system_instructions,
},
{
"role": "user",
"content": self.user_instructions(task_output=task_output),
},
]
)
printer = Printer()
printer.print(
content=f"The following code was generated for the guardrail task:\n{response}\n",
color="cyan",
)
return response
return result
def __call__(self, task_output: TaskOutput) -> Tuple[bool, Any]:
"""Executes the validation code on the task output.
"""Validates the output of a task based on specified criteria.
Args:
task_output (TaskOutput): The output to be validated.
@@ -134,28 +87,16 @@ class TaskGuardrail:
- bool: True if validation passed, False otherwise
- Any: The validation result or error message
"""
import ast
from crewai_tools import CodeInterpreterTool
try:
result = self._validate_output(task_output)
assert isinstance(
result.pydantic, TaskGuardrailResult
), "The guardrail result is not a valid pydantic model"
self.generated_code = self.generate_code(task_output)
result = CodeInterpreterTool(
code=self.generated_code, unsafe_mode=self.unsafe_mode
).run()
error_messages = [
"Something went wrong while running the code",
"No result variable found", # when running in unsafe mode, the final output should be stored in the result variable
]
if any(msg in result for msg in error_messages):
return False, result
if isinstance(result, str):
try:
result = ast.literal_eval(result)
except Exception as e:
return False, f"Error parsing result: {str(e)}"
return result
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)}"

7
src/crewai/utilities/events/task_guardrail_events.py
View File

@@ -7,8 +7,8 @@ class TaskGuardrailStartedEvent(BaseEvent):
"""Event emitted when a guardrail task starts
Attributes:
messages: Content can be either a string or a list of dictionaries that support
multimodal content (text, images, etc.)
guardrail: The guardrail callable or TaskGuardrail instance
retry_count: The number of times the guardrail has been retried
"""
type: str = "task_guardrail_started"
@@ -23,8 +23,7 @@ class TaskGuardrailStartedEvent(BaseEvent):
super().__init__(**data)
if isinstance(self.guardrail, TaskGuardrail):
assert self.guardrail.generated_code is not None
self.guardrail = self.guardrail.generated_code.strip()
self.guardrail = self.guardrail.description.strip()
elif isinstance(self.guardrail, Callable):
self.guardrail = getsource(self.guardrail).strip()

1384
tests/cassettes/test_after_kickoff_modification.yaml
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1404
tests/cassettes/test_before_kickoff_modification.yaml
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1428
tests/cassettes/test_before_kickoff_with_none_input.yaml
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1032
tests/cassettes/test_guardrail_emits_events.yaml
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522
tests/cassettes/test_guardrail_using_llm.yaml
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A Complete History\" by Martin Gilbert** \n This complete history takes readers
through the entirety of the war, from its causes to its aftermath, using a wide
range of sources.\n\n9. **\"1914: The Year the World Ended\" by Paul Ham** \n Ham
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its long-lasting effects on the world.\n\n10. **\"War Horse\" by Michael Morpurgo** \n This
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war, highlighting the bond between animals and humans amidst the chaos.\n\nEach
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War, making them valuable resources for anyone interested in this pivotal period
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tests/config/tasks.yaml
View File

@@ -6,7 +6,7 @@ research_task:
expected_output: >
A list with 10 bullet points of the most relevant information about {topic}
agent: researcher
guardrail: make sure each bullet contains a minimum of 100 words
guardrail: ensure each bullet contains its source
reporting_task:
description: >

26
tests/project_test.py
View File

@@ -143,31 +143,17 @@ def test_agent_function_calling_llm():
), "agent's function_calling_llm is incorrect"
# VCR could not record the request to localhost from Docker to get it version, so we need to mock the tool.
# TODO: We can remove this mock after some issue such as https://github.com/kevin1024/vcrpy/issues/519 been addressed
@pytest.fixture
def code_interpreter_tool_mock():
with patch(
"crewai_tools.tools.code_interpreter_tool.code_interpreter_tool.CodeInterpreterTool._run",
return_value="(True, 'good result')",
):
yield
def test_task_guardrail(code_interpreter_tool_mock):
def test_task_guardrail():
crew = InternalCrew()
research_task = crew.research_task()
assert (
research_task.guardrail
== "make sure each bullet contains a minimum of 100 words"
)
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(code_interpreter_tool_mock):
def test_before_kickoff_modification():
crew = InternalCrew()
inputs = {"topic": "LLMs"}
result = crew.crew().kickoff(inputs=inputs)
@@ -175,7 +161,7 @@ def test_before_kickoff_modification(code_interpreter_tool_mock):
@pytest.mark.vcr(filter_headers=["authorization"])
def test_after_kickoff_modification(code_interpreter_tool_mock):
def test_after_kickoff_modification():
crew = InternalCrew()
# Assuming the crew execution returns a dict
result = crew.crew().kickoff({"topic": "LLMs"})
@@ -186,14 +172,14 @@ def test_after_kickoff_modification(code_interpreter_tool_mock):
@pytest.mark.vcr(filter_headers=["authorization"])
def test_before_kickoff_with_none_input(code_interpreter_tool_mock):
def test_before_kickoff_with_none_input():
crew = InternalCrew()
crew.crew().kickoff(None)
# Test should pass without raising exceptions
@pytest.mark.vcr(filter_headers=["authorization"])
def test_multiple_before_after_kickoff(code_interpreter_tool_mock):
def test_multiple_before_after_kickoff():
@CrewBase
class MultipleHooksCrew:
agents: List[BaseAgent]

196
tests/test_task_guardrails.py
View File

@@ -139,127 +139,32 @@ def sample_agent():
return Agent(role="Test Agent", goal="Test Goal", backstory="Test Backstory")
@pytest.mark.vcr(filter_headers=["authorization"])
def test_guardrail_using_llm(sample_agent):
task = Task(
description="Test task",
expected_output="Output",
guardrail="Ensure the output is equal to 'good result'",
)
with patch(
"crewai.tasks.task_guardrail.TaskGuardrail.__call__",
side_effect=[(False, "bad result"), (True, "good result")],
) as mock_guardrail:
task.execute_sync(agent=sample_agent)
assert mock_guardrail.call_count == 2
task.guardrail = TaskGuardrail(
description="Ensure the output is equal to 'good result'",
llm=LLM(model="gpt-4o-mini"),
)
with patch(
"crewai.tasks.task_guardrail.TaskGuardrail.__call__",
side_effect=[(False, "bad result"), (True, "good result")],
) as mock_guardrail:
task.execute_sync(agent=sample_agent)
assert mock_guardrail.call_count == 2
@pytest.fixture
def task_output():
return TaskOutput(
raw="Test output",
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",
)
def test_task_guardrail_initialization_no_llm(task_output):
"""Test TaskGuardrail initialization fails without LLM"""
with pytest.raises(ValueError, match="Provide a valid LLM to the TaskGuardrail"):
TaskGuardrail(description="Test")(task_output)
@pytest.fixture
def mock_llm():
llm = Mock(spec=LLM)
llm.call.return_value = """
output = 'Sample book data'
if isinstance(output, str):
result = (True, output)
else:
result = (False, 'Invalid output format')
print(result)
"""
return llm
@pytest.mark.parametrize(
"tool_run_output",
[
{
"output": "(True, 'Valid output')",
"expected_result": True,
"expected_output": "Valid output",
},
{
"output": "(False, 'Invalid output format')",
"expected_result": False,
"expected_output": "Invalid output format",
},
{
"output": "Something went wrong while running the code, Invalid output format",
"expected_result": False,
"expected_output": "Something went wrong while running the code, Invalid output format",
},
{
"output": "No result variable found",
"expected_result": False,
"expected_output": "No result variable found",
},
{
"output": (False, "Invalid output format"),
"expected_result": False,
"expected_output": "Invalid output format",
},
{
"output": "bla-bla-bla",
"expected_result": False,
"expected_output": "Error parsing result: malformed node or string on line 1",
},
],
)
@patch("crewai_tools.CodeInterpreterTool.run")
def test_task_guardrail_execute_code(mock_run, mock_llm, tool_run_output, task_output):
mock_run.return_value = tool_run_output["output"]
guardrail = TaskGuardrail(description="Test validation", llm=mock_llm)
@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")
result = guardrail(task_output)
assert result[0] == tool_run_output["expected_result"]
assert tool_run_output["expected_output"] in result[1]
assert result[0] is False
assert "exceeding the guardrail limit of fewer than" in result[1].lower()
@patch("crewai_tools.CodeInterpreterTool.run")
def test_guardrail_using_additional_instructions(mock_run, mock_llm, task_output):
mock_run.return_value = "(True, 'Valid output')"
additional_instructions = (
"This is an additional instruction created by the user follow it strictly"
)
guardrail = TaskGuardrail(
description="Test validation",
llm=mock_llm,
additional_instructions=additional_instructions,
)
guardrail = TaskGuardrail(description="Ensure the result has less than 500 words")
guardrail(task_output)
assert additional_instructions in str(mock_llm.call.call_args)
result = guardrail(task_output)
assert result[0] is True
assert result[1] == task_output.raw
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -287,26 +192,13 @@ def test_guardrail_emits_events(sample_agent):
)
task = Task(
description="Test task",
expected_output="Output",
guardrail="Ensure the output is equal to 'good result'",
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",
)
with (
patch(
"crewai_tools.CodeInterpreterTool.run",
side_effect=[
"Something went wrong while running the code",
(True, "good result"),
],
),
patch(
"crewai.tasks.task_guardrail.TaskGuardrail.generate_code",
return_value="""def guardrail(result: TaskOutput):
return (True, result.raw.upper())""",
),
):
task.execute_sync(agent=sample_agent)
result = task.execute_sync(agent=sample_agent)
def custom_guardrail(result: TaskOutput):
return (True, "good result from callable function")
@@ -320,35 +212,26 @@ def test_guardrail_emits_events(sample_agent):
task.execute_sync(agent=sample_agent)
expected_started_events = [
{
"guardrail": """def guardrail(result: TaskOutput):
return (True, result.raw.upper())""",
"retry_count": 0,
},
{
"guardrail": """def guardrail(result: TaskOutput):
return (True, result.raw.upper())""",
"retry_count": 1,
},
{"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": "Something went wrong while running the code",
"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": "good result",
"error": None,
"retry_count": 1,
},
{"success": True, "result": result.raw, "error": None, "retry_count": 1},
{
"success": True,
"result": "good result from callable function",
@@ -360,20 +243,23 @@ def test_guardrail_emits_events(sample_agent):
assert completed_guardrail == expected_completed_events
@pytest.mark.parametrize("unsafe_mode", [True, False])
def test_task_guardrail_force_code_tool_unsafe_mode(mock_llm, task_output, unsafe_mode):
guardrail = TaskGuardrail(
description="Test validation", llm=mock_llm, unsafe_mode=unsafe_mode
)
@pytest.mark.vcr(filter_headers=["authorization"])
def test_guardrail_when_an_error_occurs(sample_agent, task_output):
with (
patch(
"crewai_tools.CodeInterpreterTool.__init__", return_value=None
) as mock_init,
patch(
"crewai_tools.CodeInterpreterTool.run", return_value=(True, "Valid output")
"crewai.Agent.kickoff",
side_effect=Exception("Unexpected error"),
),
pytest.raises(
Exception,
match="Error while validating the task output: Unexpected error",
),
):
result = guardrail(task_output)
mock_init.assert_called_once_with(code=ANY, unsafe_mode=unsafe_mode)
assert result == (True, "Valid output")
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)