Add HallucinationGuardrail no-op implementation with tests (#2869)

- Add `HallucinationGuardrail` class as enterprise feature placeholder
- Update LLM guardrail events to support `HallucinationGuardrail` instances
- Add comprehensive tests for `HallucinationGuardrail` initialization and behavior
- Add integration tests for `HallucinationGuardrail` with task execution system
- Ensure no-op behavior always returns True

src/crewai/tasks/hallucination_guardrail.py

@@ -0,0 +1,96 @@
+"""Hallucination Guardrail Placeholder for CrewAI.
+
+This is a no-op version of the HallucinationGuardrail for the open-source repository.
+
+Classes:
+    HallucinationGuardrail: Placeholder guardrail that validates task outputs.
+"""
+
+from typing import Any, Optional, Tuple
+
+from crewai.llm import LLM
+from crewai.tasks.task_output import TaskOutput
+from crewai.utilities.logger import Logger
+
+
+class HallucinationGuardrail:
+    """Placeholder for the HallucinationGuardrail feature.
+
+    Attributes:
+        context: The reference context that outputs would be checked against.
+        llm: The language model that would be used for evaluation.
+        threshold: Optional minimum faithfulness score that would be required to pass.
+        tool_response: Optional tool response information that would be used in evaluation.
+
+    Examples:
+        >>> # Basic usage with default verdict logic
+        >>> guardrail = HallucinationGuardrail(
+        ...     context="AI helps with various tasks including analysis and generation.",
+        ...     llm=agent.llm
+        ... )
+
+        >>> # With custom threshold for stricter validation
+        >>> strict_guardrail = HallucinationGuardrail(
+        ...     context="Quantum computing uses qubits in superposition.",
+        ...     llm=agent.llm,
+        ...     threshold=8.0  # Would require score >= 8 to pass in enterprise version
+        ... )
+
+        >>> # With tool response for additional context
+        >>> guardrail_with_tools = HallucinationGuardrail(
+        ...     context="The current weather data",
+        ...     llm=agent.llm,
+        ...     tool_response="Weather API returned: Temperature 22°C, Humidity 65%"
+        ... )
+    """
+
+    def __init__(
+        self,
+        context: str,
+        llm: LLM,
+        threshold: Optional[float] = None,
+        tool_response: str = "",
+    ):
+        """Initialize the HallucinationGuardrail placeholder.
+
+        Args:
+            context: The reference context that outputs would be checked against.
+            llm: The language model that would be used for evaluation.
+            threshold: Optional minimum faithfulness score that would be required to pass.
+            tool_response: Optional tool response information that would be used in evaluation.
+        """
+        self.context = context
+        self.llm: LLM = llm
+        self.threshold = threshold
+        self.tool_response = tool_response
+        self._logger = Logger(verbose=True)
+        self._logger.log(
+            "warning",
+            """Hallucination detection is a no-op in open source, use it for free at https://app.crewai.com\n""",
+            color="red",
+        )
+
+    @property
+    def description(self) -> str:
+        """Generate a description of this guardrail for event logging."""
+        return "HallucinationGuardrail (no-op)"
+
+    def __call__(self, task_output: TaskOutput) -> Tuple[bool, Any]:
+        """Validate a task output against hallucination criteria.
+
+        In the open source, this method always returns that the output is valid.
+
+        Args:
+            task_output: The output to be validated.
+
+        Returns:
+            A tuple containing:
+                - True
+                - The raw task output
+        """
+        self._logger.log(
+            "warning",
+            "Premium hallucination detection skipped (use for free at https://app.crewai.com)\n",
+            color="red",
+        )
+        return True, task_output.raw

src/crewai/utilities/events/llm_guardrail_events.py

@@ -19,10 +19,13 @@ class LLMGuardrailStartedEvent(BaseEvent):
         from inspect import getsource
 
         from crewai.tasks.llm_guardrail import LLMGuardrail
+        from crewai.tasks.hallucination_guardrail import HallucinationGuardrail
 
         super().__init__(**data)
 
-        if isinstance(self.guardrail, LLMGuardrail):
+        if isinstance(self.guardrail, LLMGuardrail) or isinstance(
+            self.guardrail, HallucinationGuardrail
+        ):
             self.guardrail = self.guardrail.description.strip()
         elif isinstance(self.guardrail, Callable):
             self.guardrail = getsource(self.guardrail).strip()