mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-14 18:48:29 +00:00
feat: renaming GuardrailTask to TaskGuardrail
This commit is contained in:
Lucas Gomide
@@ -480,22 +480,22 @@ class Task(BaseModel):
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raise ValueError("Guardrail is not set")
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from crewai.utilities.events import (
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GuardrailTaskCompletedEvent,
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GuardrailTaskStartedEvent,
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TaskGuardrailCompletedEvent,
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TaskGuardrailStartedEvent,
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)
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from crewai.utilities.events.crewai_event_bus import crewai_event_bus
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crewai_event_bus.emit(
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self,
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GuardrailTaskStartedEvent(
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TaskGuardrailStartedEvent(
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guardrail=self.guardrail, retry_count=self.retry_count
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),
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)
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if isinstance(self.guardrail, str):
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from crewai.tasks.guardrail_task import GuardrailTask
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from crewai.tasks.task_guardrail import TaskGuardrail
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result = GuardrailTask(description=self.guardrail, task=self)(task_output)
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result = TaskGuardrail(description=self.guardrail, task=self)(task_output)
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else:
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result = self.guardrail(task_output)
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@@ -503,7 +503,7 @@ class Task(BaseModel):
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crewai_event_bus.emit(
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self,
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GuardrailTaskCompletedEvent(
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TaskGuardrailCompletedEvent(
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success=guardrail_result.success,
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result=guardrail_result.result,
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error=guardrail_result.error,
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@@ -1,174 +0,0 @@
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from typing import Any, Tuple
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from crewai.llm import LLM
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from crewai.task import Task
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from crewai.tasks.task_output import TaskOutput
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from crewai.utilities.printer import Printer
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class GuardrailTask:
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"""A task that validates the output of another task using generated Python code.
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This class generates and executes Python code to validate task outputs based on
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specified criteria. It uses an LLM to generate the validation code and provides
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safety guardrails for code execution. The code is executed in a Docker container
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if available, otherwise it is executed in the current environment.
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Args:
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description (str): The description of the validation criteria.
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task (Task, optional): The task whose output needs validation.
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llm (LLM, optional): The language model to use for code generation.
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additional_instructions (str, optional): Additional instructions for the guardrail task.
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unsafe_mode (bool, optional): Whether to run the code in unsafe mode.
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Raises:
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ValueError: If no valid LLM is provided.
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"""
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def __init__(
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self,
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description: str,
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task: Task | None = None,
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llm: LLM | None = None,
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additional_instructions: str = "",
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unsafe_mode: bool | None = None,
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):
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self.description = description
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fallback_llm: LLM | None = (
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task.agent.llm
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if task is not None
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and hasattr(task, "agent")
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and task.agent is not None
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and hasattr(task.agent, "llm")
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else None
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)
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self.llm: LLM | None = llm or fallback_llm
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self.additional_instructions = additional_instructions
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self.unsafe_mode = unsafe_mode
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@property
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def system_instructions(self) -> str:
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"""System instructions for the LLM code generation.
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Returns:
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str: Complete system instructions including security constraints.
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"""
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security_instructions = (
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"- DO NOT wrap the output in markdown or use triple backticks. Return only raw Python code."
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"- DO NOT use `exec`, `eval`, `compile`, `open`, `os`, `subprocess`, `socket`, `shutil`, or any other system-level modules.\n"
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"- Your code must not perform any file I/O, shell access, or dynamic code execution."
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)
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return (
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"You are a expert Python developer"
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"You **must strictly** follow the task description, use the provided raw output as the input in your code. "
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"Your code must:\n"
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"- 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"
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"- Use the literal string of the task output (already included in your input) if needed.\n"
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"- Generate the code **following strictly** the task description.\n"
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"- Be valid Python 3 — executable as-is.\n"
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f"{security_instructions}\n"
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"Additional instructions (do not override the previous instructions):\n"
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f"{self.additional_instructions}"
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)
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def user_instructions(self, task_output: TaskOutput) -> str:
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"""Generates user instructions for the LLM code generation.
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Args:
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task_output (TaskOutput): The output to be validated.
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Returns:
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str: Instructions for generating validation code.
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"""
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return (
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"Based on the task description below, generate Python 3 code that validates the task output. \n"
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"Task description:\n"
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f"{self.description}\n"
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"Here is the raw output from the task: \n"
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f"'{task_output.raw}' \n"
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"Use this exact string literal inside your generated code (do not reference variables like task_output.raw)."
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"Now generate Python code that follows the instructions above."
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)
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def generate_code(self, task_output: TaskOutput) -> str:
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"""Generates Python code for validating the task output.
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Args:
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task_output (TaskOutput): The output to be validated.
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Returns:
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str: Generated Python code for validation.
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"""
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if self.llm is None:
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raise ValueError("Provide a valid LLM to the GuardrailTask")
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response = self.llm.call(
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messages=[
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{
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"role": "system",
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"content": self.system_instructions,
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},
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{
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"role": "user",
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"content": self.user_instructions(task_output=task_output),
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},
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]
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)
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printer = Printer()
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printer.print(
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content=f"The following code was generated for the guardrail task:\n{response}\n",
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color="cyan",
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)
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return response
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def __call__(self, task_output: TaskOutput) -> Tuple[bool, Any]:
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"""Executes the validation code on the task output.
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Args:
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task_output (TaskOutput): The output to be validated.
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Returns:
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Tuple[bool, Any]: A tuple containing:
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- bool: True if validation passed, False otherwise
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- Any: The validation result or error message
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"""
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import ast
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from crewai_tools import CodeInterpreterTool
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code = self.generate_code(task_output)
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unsafe_mode = (
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self.unsafe_mode
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if self.unsafe_mode is not None
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else not self.check_docker_available()
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)
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result = CodeInterpreterTool(code=code, unsafe_mode=unsafe_mode).run()
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error_messages = [
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"Something went wrong while running the code",
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"No result variable found", # when running in unsafe mode, the final output should be stored in the result variable
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]
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if any(msg in result for msg in error_messages):
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return False, result
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if isinstance(result, str):
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try:
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result = ast.literal_eval(result)
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except Exception as e:
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return False, f"Error parsing result: {str(e)}"
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return result
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def check_docker_available(self) -> bool:
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import subprocess
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try:
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subprocess.run(["docker", "--version"], check=True)
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return True
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except (subprocess.CalledProcessError, FileNotFoundError):
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return False
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@@ -9,9 +9,9 @@ from .crew_events import (
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CrewTestCompletedEvent,
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CrewTestFailedEvent,
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)
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from .guardrail_task_events import (
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GuardrailTaskCompletedEvent,
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GuardrailTaskStartedEvent,
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from .task_guardrail_events import (
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TaskGuardrailCompletedEvent,
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TaskGuardrailStartedEvent,
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)
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from .agent_events import (
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AgentExecutionStartedEvent,
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@@ -23,10 +23,6 @@ from .flow_events import (
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MethodExecutionFinishedEvent,
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MethodExecutionStartedEvent,
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)
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from .guardrail_task_events import (
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GuardrailTaskCompletedEvent,
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GuardrailTaskStartedEvent,
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)
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from .llm_events import (
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LLMCallCompletedEvent,
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LLMCallFailedEvent,
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@@ -38,6 +34,10 @@ from .task_events import (
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TaskFailedEvent,
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TaskStartedEvent,
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)
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from .task_guardrail_events import (
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TaskGuardrailCompletedEvent,
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TaskGuardrailStartedEvent,
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)
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from .tool_usage_events import (
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ToolUsageErrorEvent,
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ToolUsageFinishedEvent,
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@@ -72,6 +72,6 @@ EventTypes = Union[
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LLMCallCompletedEvent,
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LLMCallFailedEvent,
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LLMStreamChunkEvent,
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GuardrailTaskStartedEvent,
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GuardrailTaskCompletedEvent,
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TaskGuardrailStartedEvent,
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TaskGuardrailCompletedEvent,
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]
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@@ -1,28 +0,0 @@
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from typing import Any, Callable, Optional, Union
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from pydantic import BaseModel
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from crewai.utilities.events.base_events import BaseEvent
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class GuardrailTaskStartedEvent(BaseEvent):
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"""Event emitted when a guardrail task starts
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Attributes:
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messages: Content can be either a string or a list of dictionaries that support
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multimodal content (text, images, etc.)
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"""
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type: str = "guardrail_task_started"
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guardrail: Union[str, Callable]
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retry_count: int
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class GuardrailTaskCompletedEvent(BaseEvent):
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"""Event emitted when a guardrail task completes"""
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type: str = "guardrail_task_completed"
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success: bool
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result: Any
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error: Optional[str] = None
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retry_count: int
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