fix: resolve Self import on python 3.10

src/crewai/agent.py

@@ -1,6 +1,6 @@
 import shutil
 import subprocess
-from typing import Any, Dict, List, Literal, Optional, Sequence, Type, Union
+from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
@@ -155,6 +155,13 @@ class Agent(BaseAgent):
         default=None,
         description="The Agent's role to be used from your repository.",
     )
+    guardrail: Optional[Union[Callable[[Any], Tuple[bool, Any]], str]] = Field(
+        default=None,
+        description="Function or string description of a guardrail to validate agent output"
+    )
+    guardrail_max_retries: int = Field(
+        default=3, description="Maximum number of retries when guardrail fails"
+    )
 
     @model_validator(mode="before")
     def validate_from_repository(cls, v):
@@ -780,6 +787,8 @@ class Agent(BaseAgent):
             response_format=response_format,
             i18n=self.i18n,
             original_agent=self,
+            guardrail=self.guardrail,
+            guardrail_max_retries=self.guardrail_max_retries,
         )
 
         return lite_agent.kickoff(messages)

src/crewai/lite_agent.py

@@ -1,9 +1,14 @@
 import asyncio
+import inspect
 import uuid
-from datetime import datetime
-from typing import Any, Callable, Dict, List, Optional, Type, Union, cast
+from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union, cast, get_args, get_origin
 
-from pydantic import BaseModel, Field, InstanceOf, PrivateAttr, model_validator
+try:
+    from typing import Self
+except ImportError:
+    from typing_extensions import Self
+
+from pydantic import BaseModel, Field, InstanceOf, PrivateAttr, model_validator, field_validator
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
@@ -18,6 +23,7 @@ from crewai.llm import LLM
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.utilities import I18N
+from crewai.utilities.guardrail import process_guardrail
 from crewai.utilities.agent_utils import (
     enforce_rpm_limit,
     format_message_for_llm,
@@ -35,7 +41,7 @@ from crewai.utilities.agent_utils import (
     render_text_description_and_args,
     show_agent_logs,
 )
-from crewai.utilities.converter import convert_to_model, generate_model_description
+from crewai.utilities.converter import generate_model_description
 from crewai.utilities.events.agent_events import (
     LiteAgentExecutionCompletedEvent,
     LiteAgentExecutionErrorEvent,
@@ -146,6 +152,15 @@ class LiteAgent(FlowTrackable, BaseModel):
         default=[], description="Callbacks to be used for the agent"
     )
 
+    # Guardrail Properties
+    guardrail: Optional[Union[Callable[[LiteAgentOutput], Tuple[bool, Any]], str]] = Field(
+        default=None,
+        description="Function or string description of a guardrail to validate agent output"
+    )
+    guardrail_max_retries: int = Field(
+        default=3, description="Maximum number of retries when guardrail fails"
+    )
+
     # State and Results
     tools_results: List[Dict[str, Any]] = Field(
         default=[], description="Results of the tools used by the agent."
@@ -163,6 +178,9 @@ class LiteAgent(FlowTrackable, BaseModel):
     _messages: List[Dict[str, str]] = PrivateAttr(default_factory=list)
     _iterations: int = PrivateAttr(default=0)
     _printer: Printer = PrivateAttr(default_factory=Printer)
+    _guardrail: Optional[Callable] = PrivateAttr(default=None)
+    _guardrail_retry_count: int = PrivateAttr(default=0)
+
 
     @model_validator(mode="after")
     def setup_llm(self):
@@ -184,6 +202,60 @@ class LiteAgent(FlowTrackable, BaseModel):
 
         return self
 
+    @model_validator(mode="after")
+    def ensure_guardrail_is_callable(self) -> Self:
+        if callable(self.guardrail):
+            self._guardrail = self.guardrail
+        elif isinstance(self.guardrail, str):
+            from crewai.tasks.llm_guardrail import LLMGuardrail
+            assert isinstance(self.llm, LLM)
+
+            self._guardrail = LLMGuardrail(
+                description=self.guardrail, llm=self.llm
+            )
+
+        return self
+
+    @field_validator("guardrail", mode="before")
+    @classmethod
+    def validate_guardrail_function(cls, v: Optional[Union[Callable, str]]) -> Optional[Union[Callable, str]]:
+        """Validate that the guardrail function has the correct signature.
+
+        If v is a callable, validate that it has the correct signature.
+        If v is a string, return it as is.
+
+        Args:
+            v: The guardrail function to validate or a string describing the guardrail task
+
+        Returns:
+            The validated guardrail function or a string describing the guardrail task
+        """
+        if v is None or isinstance(v, str):
+            return v
+
+        # Check function signature
+        sig = inspect.signature(v)
+        if len(sig.parameters) != 1:
+            raise ValueError(
+                f"Guardrail function must accept exactly 1 parameter (LiteAgentOutput), "
+                f"but it accepts {len(sig.parameters)}"
+            )
+
+        # Check return annotation if present
+        if sig.return_annotation is not sig.empty:
+            if sig.return_annotation == Tuple[bool, Any]:
+                return v
+
+            origin = get_origin(sig.return_annotation)
+            args = get_args(sig.return_annotation)
+
+            if origin is not tuple or len(args) != 2 or args[0] is not bool:
+                raise ValueError(
+                    "If return type is annotated, it must be Tuple[bool, Any]"
+                )
+
+        return v
+
     @property
     def key(self) -> str:
         """Get the unique key for this agent instance."""
@@ -223,54 +295,7 @@ class LiteAgent(FlowTrackable, BaseModel):
             # Format messages for the LLM
             self._messages = self._format_messages(messages)
 
-            # Emit event for agent execution start
-            crewai_event_bus.emit(
-                self,
-                event=LiteAgentExecutionStartedEvent(
-                    agent_info=agent_info,
-                    tools=self._parsed_tools,
-                    messages=messages,
-                ),
-            )
-
-            # Execute the agent using invoke loop
-            agent_finish = self._invoke_loop()
-            formatted_result: Optional[BaseModel] = None
-            if self.response_format:
-                try:
-                    # Cast to BaseModel to ensure type safety
-                    result = self.response_format.model_validate_json(
-                        agent_finish.output
-                    )
-                    if isinstance(result, BaseModel):
-                        formatted_result = result
-                except Exception as e:
-                    self._printer.print(
-                        content=f"Failed to parse output into response format: {str(e)}",
-                        color="yellow",
-                    )
-
-            # Calculate token usage metrics
-            usage_metrics = self._token_process.get_summary()
-
-            # Create output
-            output = LiteAgentOutput(
-                raw=agent_finish.output,
-                pydantic=formatted_result,
-                agent_role=self.role,
-                usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
-            )
-
-            # Emit completion event
-            crewai_event_bus.emit(
-                self,
-                event=LiteAgentExecutionCompletedEvent(
-                    agent_info=agent_info,
-                    output=agent_finish.output,
-                ),
-            )
-
-            return output
+            return self._execute_core(agent_info=agent_info)
 
         except Exception as e:
             self._printer.print(
@@ -288,6 +313,94 @@ class LiteAgent(FlowTrackable, BaseModel):
             )
             raise e
 
+    def _execute_core(self, agent_info: Dict[str, Any]) -> LiteAgentOutput:
+        # Emit event for agent execution start
+        crewai_event_bus.emit(
+            self,
+            event=LiteAgentExecutionStartedEvent(
+                agent_info=agent_info,
+                tools=self._parsed_tools,
+                messages=self._messages,
+            ),
+        )
+
+        # Execute the agent using invoke loop
+        agent_finish = self._invoke_loop()
+        formatted_result: Optional[BaseModel] = None
+        if self.response_format:
+            try:
+                # Cast to BaseModel to ensure type safety
+                result = self.response_format.model_validate_json(
+                    agent_finish.output
+                )
+                if isinstance(result, BaseModel):
+                    formatted_result = result
+            except Exception as e:
+                self._printer.print(
+                    content=f"Failed to parse output into response format: {str(e)}",
+                    color="yellow",
+                )
+
+        # Calculate token usage metrics
+        usage_metrics = self._token_process.get_summary()
+
+        # Create output
+        output = LiteAgentOutput(
+            raw=agent_finish.output,
+            pydantic=formatted_result,
+            agent_role=self.role,
+            usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
+        )
+
+        # Process guardrail if set
+        if self._guardrail is not None:
+            guardrail_result = process_guardrail(
+                output=output,
+                guardrail=self._guardrail,
+                retry_count=self._guardrail_retry_count
+            )
+
+            if not guardrail_result.success:
+                if self._guardrail_retry_count >= self.guardrail_max_retries:
+                        raise Exception(
+                            f"Agent's guardrail failed validation after {self.guardrail_max_retries} retries. "
+                            f"Last error: {guardrail_result.error}"
+                        )
+                self._guardrail_retry_count += 1
+                if self.verbose:
+                    self._printer.print(
+                        f"Guardrail failed. Retrying ({self._guardrail_retry_count}/{self.guardrail_max_retries})..."
+                        f"\n{guardrail_result.error}"
+                    )
+
+                self._messages.append({
+                    "role": "user",
+                    "content": guardrail_result.error or "Guardrail validation failed"
+                })
+
+                return self._execute_core(agent_info=agent_info)
+
+            # Apply guardrail result if available
+            if guardrail_result.result is not None:
+                if isinstance(guardrail_result.result, str):
+                    output.raw = guardrail_result.result
+                elif isinstance(guardrail_result.result, BaseModel):
+                    output.pydantic = guardrail_result.result
+
+            usage_metrics = self._token_process.get_summary()
+            output.usage_metrics = usage_metrics.model_dump() if usage_metrics else None
+
+        # Emit completion event
+        crewai_event_bus.emit(
+            self,
+            event=LiteAgentExecutionCompletedEvent(
+                agent_info=agent_info,
+                output=agent_finish.output,
+            ),
+        )
+
+        return output
+
     async def kickoff_async(
         self, messages: Union[str, List[Dict[str, str]]]
     ) -> LiteAgentOutput:

src/crewai/task.py

@@ -35,12 +35,12 @@ from pydantic_core import PydanticCustomError
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.security import Fingerprint, SecurityConfig
-from crewai.tasks.guardrail_result import GuardrailResult
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.tools.base_tool import BaseTool
 from crewai.utilities.config import process_config
 from crewai.utilities.constants import NOT_SPECIFIED
+from crewai.utilities.guardrail import process_guardrail, GuardrailResult
 from crewai.utilities.converter import Converter, convert_to_model
 from crewai.utilities.events import (
     TaskCompletedEvent,
@@ -431,7 +431,11 @@ class Task(BaseModel):
             )
 
             if self._guardrail:
-                guardrail_result = self._process_guardrail(task_output)
+                guardrail_result = process_guardrail(
+                    output=task_output,
+                    guardrail=self._guardrail,
+                    retry_count=self.retry_count
+                )
                 if not guardrail_result.success:
                     if self.retry_count >= self.max_retries:
                         raise Exception(
@@ -527,10 +531,10 @@ class Task(BaseModel):
 
     def prompt(self) -> str:
         """Generates the task prompt with optional markdown formatting.
-        
+
         When the markdown attribute is True, instructions for formatting the
         response in Markdown syntax will be added to the prompt.
-        
+
         Returns:
             str: The formatted prompt string containing the task description,
                  expected output, and optional markdown formatting instructions.
@@ -541,7 +545,7 @@ class Task(BaseModel):
             expected_output=self.expected_output
         )
         tasks_slices = [self.description, output]
-        
+
         if self.markdown:
             markdown_instruction = """Your final answer MUST be formatted in Markdown syntax.
 Follow these guidelines:

src/crewai/tools/base_tool.py

@@ -64,7 +64,7 @@ class BaseTool(BaseModel, ABC):
                 },
             },
         )
-        
+
     @field_validator("max_usage_count", mode="before")
     @classmethod
     def validate_max_usage_count(cls, v: int | None) -> int | None:
@@ -88,11 +88,11 @@ class BaseTool(BaseModel, ABC):
         # If _run is async, we safely run it
         if asyncio.iscoroutine(result):
             result = asyncio.run(result)
-            
+
         self.current_usage_count += 1
-        
+
         return result
-        
+
     def reset_usage_count(self) -> None:
         """Reset the current usage count to zero."""
         self.current_usage_count = 0
@@ -279,7 +279,7 @@ def to_langchain(
 def tool(*args, result_as_answer: bool = False, max_usage_count: int | None = None) -> Callable:
     """
     Decorator to create a tool from a function.
-    
+
     Args:
         *args: Positional arguments, either the function to decorate or the tool name.
         result_as_answer: Flag to indicate if the tool result should be used as the final agent answer.

src/crewai/utilities/guardrail.py

@@ -1,15 +1,7 @@
-"""
-Module for handling task guardrail validation results.
-
-This module provides the GuardrailResult class which standardizes
-the way task guardrails return their validation results.
-"""
-
-from typing import Any, Optional, Tuple, Union
+from typing import Any, Callable, Optional, Tuple, Union
 
 from pydantic import BaseModel, field_validator
 
-
 class GuardrailResult(BaseModel):
     """Result from a task guardrail execution.
 
@@ -54,3 +46,48 @@ class GuardrailResult(BaseModel):
             result=data if success else None,
             error=data if not success else None
         )
+
+
+def process_guardrail(output: Any, guardrail: Callable, retry_count: int) -> GuardrailResult:
+    """Process the guardrail for the agent output.
+
+    Args:
+        output: The output to validate with the guardrail
+
+    Returns:
+        GuardrailResult: The result of the guardrail validation
+    """
+    from crewai.task import TaskOutput
+    from crewai.lite_agent import LiteAgentOutput
+
+    assert isinstance(output, TaskOutput) or isinstance(output, LiteAgentOutput), "Output must be a TaskOutput or LiteAgentOutput"
+
+    assert guardrail is not None
+
+    from crewai.utilities.events import (
+        LLMGuardrailCompletedEvent,
+        LLMGuardrailStartedEvent,
+    )
+    from crewai.utilities.events.crewai_event_bus import crewai_event_bus
+
+    crewai_event_bus.emit(
+        None,
+        LLMGuardrailStartedEvent(
+            guardrail=guardrail, retry_count=retry_count
+        ),
+    )
+
+    result = guardrail(output)
+    guardrail_result = GuardrailResult.from_tuple(result)
+
+    crewai_event_bus.emit(
+        None,
+        LLMGuardrailCompletedEvent(
+            success=guardrail_result.success,
+            result=guardrail_result.result,
+            error=guardrail_result.error,
+            retry_count=retry_count,
+        ),
+    )
+
+    return guardrail_result

tests/cassettes/test_agent_output_when_guardrail_returns_base_model.yaml

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+      information about the best soccer players\n\nTo give my best complete final
+      answer to the task respond using the exact following format:\n\nThought: I now
+      can give a great answer\nFinal Answer: Your final answer must be the great and
+      the most complete as possible, it must be outcome described.\n\nI MUST use these
+      formats, my job depends on it!"}, {"role": "user", "content": "Top 10 best players
+      in the world?"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    headers:
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+      in the world?"}, {"role": "assistant", "content": "Thought: I now can give a
+      great answer  \nFinal Answer: Here are the top 10 best soccer players in the
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+      Country: Argentina\n   - Highlights: Messi continues to showcase his exceptional
+      dribbling, vision, and playmaking ability, leading Argentina to victory in the
+      2022 FIFA World Cup and significantly contributing to his club''s successes.\n\n2.
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+      positions among the best in the world."}, {"role": "user", "content": "The task
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+      numerous awards and accolades throughout her legendary career.\n\n5. **Andressa
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+      team.\n\n6. **Alana Santos**  \n   - Position: Defender  \n   - Club: Benfica  \n   -
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+      players:\n\n1. **Lionel Messi (Inter Miami CF)**\n   - Position: Forward\n   -
+      Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements:
+      Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2.
+      **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key
+      Attributes: Speed, technique, finishing.\n   - Achievements: FIFA World Cup
+      champion (2018), Ligue 1 titles, multiple domestic cups.\n\n3. **Erling Haaland
+      (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed,
+      goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions
+      League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester
+      City)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, creativity.\n   -
+      Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League
+      winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   -
+      Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements:
+      2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6.
+      **Robert Lewandowski (FC Barcelona)**\n   - Position: Forward\n   - Key Attributes:
+      Finishing, positioning, aerial ability.\n   - Achievements: FIFA Best Men''s
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+      UEFA Champions League winner (2022), La Liga champion (2023).\n\n9. **Luka Modri\u0107
+      (Real Madrid)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision,
+      tactical intelligence.\n   - Achievements: Multiple Champions League titles,
+      Ballon d''Or winner (2018).\n\n10. **Harry Kane (Bayern Munich)**\n    - Position:
+      Forward\n    - Key Attributes: Goal-scoring, technique, playmaking.\n    - Achievements:
+      Golden Boot winner, Premier League titles, UEFA European Championship runner-up.\n\nThis
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+      are an expert at gathering and organizing information. You carefully collect
+      details and present them in a structured way.\nYour personal goal is: Gather
+      information about the best soccer players\n\nTo give my best complete final
+      answer to the task respond using the exact following format:\n\nThought: I now
+      can give a great answer\nFinal Answer: Your final answer must be the great and
+      the most complete as possible, it must be outcome described.\n\nI MUST use these
+      formats, my job depends on it!"}, {"role": "user", "content": "Top 10 best players
+      in the world?"}, {"role": "assistant", "content": "Thought: I now can give a
+      great answer  \nFinal Answer: The top 10 best soccer players in the world, as
+      of October 2023, can be identified based on their recent performances, skills,
+      impact on games, and overall contributions to their teams. Here is the structured
+      list:\n\n1. **Lionel Messi (Inter Miami CF)**\n   - Position: Forward\n   -
+      Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements:
+      Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2.
+      **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key
+      Attributes: Speed, technique, finishing.\n   - Achievements: FIFA World Cup
+      champion (2018), Ligue 1 titles, multiple domestic cups.\n\n3. **Erling Haaland
+      (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed,
+      goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions
+      League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester
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+      Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League
+      winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   -
+      Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements:
+      2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6.
+      **Neymar Jr. (Al Hilal)**\n   - Position: Forward\n   - Key Attributes: Flair,
+      dribbling, creativity.\n   - Achievements: Multiple domestic league titles,
+      Champions League runner-up.\n\n7. **Robert Lewandowski (FC Barcelona)**\n   -
+      Position: Forward\n   - Key Attributes: Finishing, positioning, aerial ability.\n   -
+      Achievements: FIFA Best Men''s Player, multiple Bundesliga titles, La Liga champion
+      (2023).\n\n8. **Mohamed Salah (Liverpool)**\n   - Position: Forward\n   - Key
+      Attributes: Speed, finishing, dribbling.\n   - Achievements: Premier League
+      champion, FA Cup, UEFA Champions League winner.\n\n9. **Vin\u00edcius J\u00fanior
+      (Real Madrid)**\n   - Position: Forward\n   - Key Attributes: Speed, dribbling,
+      creativity.\n   - Achievements: UEFA Champions League winner (2022), La Liga
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+      Midfielder\n    - Key Attributes: Passing, vision, tactical intelligence.\n    -
+      Achievements: Multiple Champions League titles, Ballon d''Or winner (2018).\n\nThis
+      list is compiled based on their current form, past performances, and contributions
+      to their respective teams in both domestic and international competitions. Player
+      rankings can vary based on personal opinion and specific criteria used for evaluation,
+      but these players have consistently been regarded as some of the best in the
+      world as of October 2023."}, {"role": "user", "content": "You are not allowed
+      to include Brazilian players"}, {"role": "assistant", "content": "Thought: I
+      now can give a great answer  \nFinal Answer: Here is an updated list of the
+      top 10 best soccer players in the world as of October 2023, excluding Brazilian
+      players:\n\n1. **Lionel Messi (Inter Miami CF)**\n   - Position: Forward\n   -
+      Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements:
+      Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2.
+      **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key
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+      champion (2018), Ligue 1 titles, multiple domestic cups.\n\n3. **Erling Haaland
+      (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed,
+      goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions
+      League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester
+      City)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, creativity.\n   -
+      Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League
+      winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   -
+      Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements:
+      2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6.
+      **Robert Lewandowski (FC Barcelona)**\n   - Position: Forward\n   - Key Attributes:
+      Finishing, positioning, aerial ability.\n   - Achievements: FIFA Best Men''s
+      Player, multiple Bundesliga titles, La Liga champion (2023).\n\n7. **Mohamed
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+      performances, and achievements."}, {"role": "user", "content": "You are not
+      allowed to include Brazilian players"}, {"role": "assistant", "content": "Thought:
+      I now can give a great answer  \nFinal Answer: Here is the revised list of the
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+      **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key
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+      demonstrated exceptional skills and remarkable performances, solidifying their
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tests/test_lite_agent.py

@@ -1,4 +1,4 @@
-import asyncio
+from collections import defaultdict
 from typing import cast
 from unittest.mock import Mock
 
@@ -313,5 +313,108 @@ def test_sets_parent_flow_when_inside_flow():
             nonlocal captured_agent
             captured_agent = source
 
-        result = flow.kickoff()
+        flow.kickoff()
         assert captured_agent.parent_flow is flow
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_guardrail_is_called_using_string():
+    guardrail_events = defaultdict(list)
+    from crewai.utilities.events import LLMGuardrailCompletedEvent, LLMGuardrailStartedEvent
+    with crewai_event_bus.scoped_handlers():
+        @crewai_event_bus.on(LLMGuardrailStartedEvent)
+        def capture_guardrail_started(source, event):
+            guardrail_events["started"].append(event)
+
+        @crewai_event_bus.on(LLMGuardrailCompletedEvent)
+        def capture_guardrail_completed(source, event):
+            guardrail_events["completed"].append(event)
+
+        agent = Agent(
+            role="Sports Analyst",
+            goal="Gather information about the best soccer players",
+            backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
+            guardrail="""Only include Brazilian players, both women and men""",
+        )
+
+        result = agent.kickoff(messages="Top 10 best players in the world?")
+
+        assert len(guardrail_events['started']) == 2
+        assert len(guardrail_events['completed']) == 2
+        assert not guardrail_events['completed'][0].success
+        assert guardrail_events['completed'][1].success
+        assert "Here are the top 10 best soccer players in the world, focusing exclusively on Brazilian players" in result.raw
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_guardrail_is_called_using_callable():
+    guardrail_events = defaultdict(list)
+    from crewai.utilities.events import LLMGuardrailCompletedEvent, LLMGuardrailStartedEvent
+    with crewai_event_bus.scoped_handlers():
+        @crewai_event_bus.on(LLMGuardrailStartedEvent)
+        def capture_guardrail_started(source, event):
+            guardrail_events["started"].append(event)
+
+        @crewai_event_bus.on(LLMGuardrailCompletedEvent)
+        def capture_guardrail_completed(source, event):
+            guardrail_events["completed"].append(event)
+
+        agent = Agent(
+            role="Sports Analyst",
+            goal="Gather information about the best soccer players",
+            backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
+            guardrail=lambda output: (True, "Pelé - Santos, 1958"),
+        )
+
+        result = agent.kickoff(messages="Top 1 best players in the world?")
+
+        assert len(guardrail_events['started']) == 1
+        assert len(guardrail_events['completed']) == 1
+        assert guardrail_events['completed'][0].success
+        assert "Pelé - Santos, 1958" in result.raw
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_guardrail_reached_attempt_limit():
+    guardrail_events = defaultdict(list)
+    from crewai.utilities.events import LLMGuardrailCompletedEvent, LLMGuardrailStartedEvent
+    with crewai_event_bus.scoped_handlers():
+        @crewai_event_bus.on(LLMGuardrailStartedEvent)
+        def capture_guardrail_started(source, event):
+            guardrail_events["started"].append(event)
+
+        @crewai_event_bus.on(LLMGuardrailCompletedEvent)
+        def capture_guardrail_completed(source, event):
+            guardrail_events["completed"].append(event)
+
+        agent = Agent(
+            role="Sports Analyst",
+            goal="Gather information about the best soccer players",
+            backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
+            guardrail=lambda output: (False, "You are not allowed to include Brazilian players"),
+            guardrail_max_retries=2,
+        )
+
+        with pytest.raises(Exception, match="Agent's guardrail failed validation after 2 retries"):
+            agent.kickoff(messages="Top 10 best players in the world?")
+
+        assert len(guardrail_events['started']) == 3 # 2 retries + 1 initial call
+        assert len(guardrail_events['completed']) == 3 # 2 retries + 1 initial call
+        assert not guardrail_events['completed'][0].success
+        assert not guardrail_events['completed'][1].success
+        assert not guardrail_events['completed'][2].success
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_output_when_guardrail_returns_base_model():
+    class Player(BaseModel):
+        name: str
+        country: str
+
+    agent = Agent(
+        role="Sports Analyst",
+        goal="Gather information about the best soccer players",
+        backstory="""You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.""",
+        guardrail=lambda output: (True, Player(name="Lionel Messi", country="Argentina")),
+    )
+
+    result = agent.kickoff(messages="Top 10 best players in the world?")
+
+    assert result.pydantic == Player(name="Lionel Messi", country="Argentina")