fix llm guardrail import and docs

docs/concepts/tasks.mdx

@@ -369,7 +369,7 @@ blog_task = Task(
    - Type hints are recommended but optional
 
 2. **Return Values**:
-   - On success: it returns a tuple of `(bool, Any)`. For example: `(True, validated_result)` 
+   - 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")`
 
 ### LLMGuardrail
@@ -380,7 +380,7 @@ The `LLMGuardrail` class offers a robust mechanism for validating task outputs.
 
 1. **Structured Error Responses**:
 ```python Code
-from crewai import TaskOutput
+from crewai import TaskOutput, LLMGuardrail
 
 def validate_with_context(result: TaskOutput) -> Tuple[bool, Any]:
     try:

src/crewai/__init__.py

@@ -9,6 +9,7 @@ from crewai.llm import LLM
 from crewai.llms.base_llm import BaseLLM
 from crewai.process import Process
 from crewai.task import Task
+from crewai.tasks.llm_guardrail import LLMGuardrail
 from crewai.tasks.task_output import TaskOutput
 
 warnings.filterwarnings(
@@ -29,4 +30,5 @@ __all__ = [
     "Flow",
     "Knowledge",
     "TaskOutput",
+    "LLMGuardrail",
 ]