chore: merge main into release/v1.0.0

Resolved merge conflicts:
- agent.py: kept main's docker validation, restored get_platform_tools method, fixed KnowledgeRetrievalStartedEvent position
- agent_utils.py: merged both LLM and LiteAgent imports
- test_cache_hitting.yaml: accepted main's test recordings
- Removed 4 deprecated test cassettes from old tests/ directory

Key changes from main:
- Enhanced knowledge event handling with from_agent/from_task parameters
- Updated LLMMessage typing in kickoff methods
- Added guardrail parameter to async kickoff
- Restored runtime validation in guardrail.py

lib/crewai/src/crewai/agent.py

@@ -5,7 +5,6 @@ from collections.abc import Callable, Sequence
 from typing import (
     Any,
     Literal,
-    cast,
 )
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
@@ -54,6 +53,7 @@ from crewai.utilities.converter import generate_model_description
 from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
+from crewai.utilities.types import LLMMessage
 
 
 class Agent(BaseAgent):
@@ -348,12 +348,13 @@ class Agent(BaseAgent):
             crewai_event_bus.emit(
                 self,
                 event=KnowledgeRetrievalStartedEvent(
-                    agent=self,
+                    from_task=task,
+                    from_agent=self,
                 ),
             )
             try:
                 self.knowledge_search_query = self._get_knowledge_search_query(
-                    task_prompt
+                    task_prompt, task
                 )
                 if self.knowledge_search_query:
                     # Quering agent specific knowledge
@@ -383,7 +384,8 @@ class Agent(BaseAgent):
                         self,
                         event=KnowledgeRetrievalCompletedEvent(
                             query=self.knowledge_search_query,
-                            agent=self,
+                            from_task=task,
+                            from_agent=self,
                             retrieved_knowledge=(
                                 (self.agent_knowledge_context or "")
                                 + (
@@ -401,8 +403,9 @@ class Agent(BaseAgent):
                     self,
                     event=KnowledgeSearchQueryFailedEvent(
                         query=self.knowledge_search_query or "",
-                        agent=self,
                         error=str(e),
+                        from_task=task,
+                        from_agent=self,
                     ),
                 )
 
@@ -577,7 +580,7 @@ class Agent(BaseAgent):
             agent=self,
             crew=self.crew,
             tools=parsed_tools,
-            prompt=cast(dict[str, str], prompt),
+            prompt=prompt,  # type: ignore[arg-type]
             original_tools=raw_tools,
             stop_words=stop_words,
             max_iter=self.max_iter,
@@ -744,13 +747,14 @@ class Agent(BaseAgent):
     def set_fingerprint(self, fingerprint: Fingerprint):
         self.security_config.fingerprint = fingerprint
 
-    def _get_knowledge_search_query(self, task_prompt: str) -> str | None:
+    def _get_knowledge_search_query(self, task_prompt: str, task: Task) -> str | None:
         """Generate a search query for the knowledge base based on the task description."""
         crewai_event_bus.emit(
             self,
             event=KnowledgeQueryStartedEvent(
                 task_prompt=task_prompt,
-                agent=self,
+                from_task=task,
+                from_agent=self,
             ),
         )
         query = self.i18n.slice("knowledge_search_query").format(
@@ -765,8 +769,9 @@ class Agent(BaseAgent):
             crewai_event_bus.emit(
                 self,
                 event=KnowledgeQueryFailedEvent(
-                    agent=self,
                     error="LLM is not compatible with knowledge search queries",
+                    from_task=task,
+                    from_agent=self,
                 ),
             )
             return None
@@ -785,7 +790,8 @@ class Agent(BaseAgent):
                 self,
                 event=KnowledgeQueryCompletedEvent(
                     query=query,
-                    agent=self,
+                    from_task=task,
+                    from_agent=self,
                 ),
             )
             return rewritten_query
@@ -793,15 +799,16 @@ class Agent(BaseAgent):
             crewai_event_bus.emit(
                 self,
                 event=KnowledgeQueryFailedEvent(
-                    agent=self,
                     error=str(e),
+                    from_task=task,
+                    from_agent=self,
                 ),
             )
             return None
 
     def kickoff(
         self,
-        messages: str | list[dict[str, str]],
+        messages: str | list[LLMMessage],
         response_format: type[Any] | None = None,
     ) -> LiteAgentOutput:
         """
@@ -841,7 +848,7 @@ class Agent(BaseAgent):
 
     async def kickoff_async(
         self,
-        messages: str | list[dict[str, str]],
+        messages: str | list[LLMMessage],
         response_format: type[Any] | None = None,
     ) -> LiteAgentOutput:
         """
@@ -871,6 +878,7 @@ class Agent(BaseAgent):
             response_format=response_format,
             i18n=self.i18n,
             original_agent=self,
+            guardrail=self.guardrail,
         )
 
         return await lite_agent.kickoff_async(messages)

lib/crewai/src/crewai/events/listeners/tracing/trace_listener.py

@@ -32,6 +32,13 @@ from crewai.events.types.flow_events import (
     MethodExecutionFinishedEvent,
     MethodExecutionStartedEvent,
 )
+from crewai.events.types.knowledge_events import (
+    KnowledgeQueryCompletedEvent,
+    KnowledgeQueryFailedEvent,
+    KnowledgeQueryStartedEvent,
+    KnowledgeRetrievalCompletedEvent,
+    KnowledgeRetrievalStartedEvent,
+)
 from crewai.events.types.llm_events import (
     LLMCallCompletedEvent,
     LLMCallFailedEvent,
@@ -310,6 +317,26 @@ class TraceCollectionListener(BaseEventListener):
         def on_agent_reasoning_failed(source, event):
             self._handle_action_event("agent_reasoning_failed", source, event)
 
+        @event_bus.on(KnowledgeRetrievalStartedEvent)
+        def on_knowledge_retrieval_started(source, event):
+            self._handle_action_event("knowledge_retrieval_started", source, event)
+
+        @event_bus.on(KnowledgeRetrievalCompletedEvent)
+        def on_knowledge_retrieval_completed(source, event):
+            self._handle_action_event("knowledge_retrieval_completed", source, event)
+
+        @event_bus.on(KnowledgeQueryStartedEvent)
+        def on_knowledge_query_started(source, event):
+            self._handle_action_event("knowledge_query_started", source, event)
+
+        @event_bus.on(KnowledgeQueryCompletedEvent)
+        def on_knowledge_query_completed(source, event):
+            self._handle_action_event("knowledge_query_completed", source, event)
+
+        @event_bus.on(KnowledgeQueryFailedEvent)
+        def on_knowledge_query_failed(source, event):
+            self._handle_action_event("knowledge_query_failed", source, event)
+
     def _initialize_crew_batch(self, source: Any, event: Any):
         """Initialize trace batch"""
         user_context = self._get_user_context()

lib/crewai/src/crewai/events/types/knowledge_events.py

@@ -1,51 +1,60 @@
-from crewai.agents.agent_builder.base_agent import BaseAgent
+from typing import Any
+
 from crewai.events.base_events import BaseEvent
 
 
-class KnowledgeRetrievalStartedEvent(BaseEvent):
+class KnowledgeEventBase(BaseEvent):
+    task_id: str | None = None
+    task_name: str | None = None
+    from_task: Any | None = None
+    from_agent: Any | None = None
+    agent_role: str | None = None
+    agent_id: str | None = None
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self._set_agent_params(data)
+        self._set_task_params(data)
+
+
+class KnowledgeRetrievalStartedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge retrieval is started."""
 
     type: str = "knowledge_search_query_started"
-    agent: BaseAgent
 
 
-class KnowledgeRetrievalCompletedEvent(BaseEvent):
+class KnowledgeRetrievalCompletedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge retrieval is completed."""
 
     query: str
     type: str = "knowledge_search_query_completed"
-    agent: BaseAgent
     retrieved_knowledge: str
 
 
-class KnowledgeQueryStartedEvent(BaseEvent):
+class KnowledgeQueryStartedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge query is started."""
 
     task_prompt: str
     type: str = "knowledge_query_started"
-    agent: BaseAgent
 
 
-class KnowledgeQueryFailedEvent(BaseEvent):
+class KnowledgeQueryFailedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge query fails."""
 
     type: str = "knowledge_query_failed"
-    agent: BaseAgent
     error: str
 
 
-class KnowledgeQueryCompletedEvent(BaseEvent):
+class KnowledgeQueryCompletedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge query is completed."""
 
     query: str
     type: str = "knowledge_query_completed"
-    agent: BaseAgent
 
 
-class KnowledgeSearchQueryFailedEvent(BaseEvent):
+class KnowledgeSearchQueryFailedEvent(KnowledgeEventBase):
     """Event emitted when a knowledge search query fails."""
 
     query: str
     type: str = "knowledge_search_query_failed"
-    agent: BaseAgent
     error: str

lib/crewai/src/crewai/events/types/llm_guardrail_events.py

@@ -5,7 +5,21 @@ from typing import Any
 from crewai.events.base_events import BaseEvent
 
 
-class LLMGuardrailStartedEvent(BaseEvent):
+class LLMGuardrailBaseEvent(BaseEvent):
+    task_id: str | None = None
+    task_name: str | None = None
+    from_task: Any | None = None
+    from_agent: Any | None = None
+    agent_role: str | None = None
+    agent_id: str | None = None
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self._set_agent_params(data)
+        self._set_task_params(data)
+
+
+class LLMGuardrailStartedEvent(LLMGuardrailBaseEvent):
     """Event emitted when a guardrail task starts
 
     Attributes:
@@ -29,7 +43,7 @@ class LLMGuardrailStartedEvent(BaseEvent):
             self.guardrail = getsource(self.guardrail).strip()
 
 
-class LLMGuardrailCompletedEvent(BaseEvent):
+class LLMGuardrailCompletedEvent(LLMGuardrailBaseEvent):
     """Event emitted when a guardrail task completes
 
     Attributes:
@@ -44,3 +58,16 @@ class LLMGuardrailCompletedEvent(BaseEvent):
     result: Any
     error: str | None = None
     retry_count: int
+
+
+class LLMGuardrailFailedEvent(LLMGuardrailBaseEvent):
+    """Event emitted when a guardrail task fails
+
+    Attributes:
+        error: The error message
+        retry_count: The number of times the guardrail has been retried
+    """
+
+    type: str = "llm_guardrail_failed"
+    error: str
+    retry_count: int

lib/crewai/src/crewai/events/utils/console_formatter.py

@@ -1377,7 +1377,7 @@ class ConsoleFormatter:
         if isinstance(formatted_answer, AgentAction):
             thought = re.sub(r"\n+", "\n", formatted_answer.thought)
             formatted_json = json.dumps(
-                formatted_answer.tool_input,
+                json.loads(formatted_answer.tool_input),
                 indent=2,
                 ensure_ascii=False,
             )

lib/crewai/src/crewai/lite_agent.py

@@ -3,6 +3,7 @@ from collections.abc import Callable
 import inspect
 from typing import (
     Any,
+    Literal,
     cast,
     get_args,
     get_origin,
@@ -62,6 +63,7 @@ from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.printer import Printer
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.tool_utils import execute_tool_and_check_finality
+from crewai.utilities.types import LLMMessage
 
 
 class LiteAgentOutput(BaseModel):
@@ -180,7 +182,7 @@ class LiteAgent(FlowTrackable, BaseModel):
     _token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
     _cache_handler: CacheHandler = PrivateAttr(default_factory=CacheHandler)
     _key: str = PrivateAttr(default_factory=lambda: str(uuid.uuid4()))
-    _messages: list[dict[str, str]] = PrivateAttr(default_factory=list)
+    _messages: list[LLMMessage] = PrivateAttr(default_factory=list)
     _iterations: int = PrivateAttr(default=0)
     _printer: Printer = PrivateAttr(default_factory=Printer)
     _guardrail: Callable | None = PrivateAttr(default=None)
@@ -219,7 +221,6 @@ class LiteAgent(FlowTrackable, BaseModel):
                 raise TypeError(
                     f"Guardrail requires LLM instance of type BaseLLM, got {type(self.llm).__name__}"
                 )
-
             self._guardrail = LLMGuardrail(description=self.guardrail, llm=self.llm)
 
         return self
@@ -276,7 +277,7 @@ class LiteAgent(FlowTrackable, BaseModel):
         """Return the original role for compatibility with tool interfaces."""
         return self.role
 
-    def kickoff(self, messages: str | list[dict[str, str]]) -> LiteAgentOutput:
+    def kickoff(self, messages: str | list[LLMMessage]) -> LiteAgentOutput:
         """
         Execute the agent with the given messages.
 
@@ -371,6 +372,7 @@ class LiteAgent(FlowTrackable, BaseModel):
                 guardrail=self._guardrail,
                 retry_count=self._guardrail_retry_count,
                 event_source=self,
+                from_agent=self,
             )
 
             if not guardrail_result.success:
@@ -420,9 +422,7 @@ class LiteAgent(FlowTrackable, BaseModel):
 
         return output
 
-    async def kickoff_async(
-        self, messages: str | list[dict[str, str]]
-    ) -> LiteAgentOutput:
+    async def kickoff_async(self, messages: str | list[LLMMessage]) -> LiteAgentOutput:
         """
         Execute the agent asynchronously with the given messages.
 
@@ -467,9 +467,7 @@ class LiteAgent(FlowTrackable, BaseModel):
 
         return base_prompt
 
-    def _format_messages(
-        self, messages: str | list[dict[str, str]]
-    ) -> list[dict[str, str]]:
+    def _format_messages(self, messages: str | list[LLMMessage]) -> list[LLMMessage]:
         """Format messages for the LLM."""
         if isinstance(messages, str):
             messages = [{"role": "user", "content": messages}]
@@ -477,7 +475,9 @@ class LiteAgent(FlowTrackable, BaseModel):
         system_prompt = self._get_default_system_prompt()
 
         # Add system message at the beginning
-        formatted_messages = [{"role": "system", "content": system_prompt}]
+        formatted_messages: list[LLMMessage] = [
+            {"role": "system", "content": system_prompt}
+        ]
 
         # Add the rest of the messages
         formatted_messages.extend(messages)
@@ -589,6 +589,8 @@ class LiteAgent(FlowTrackable, BaseModel):
             ),
         )
 
-    def _append_message(self, text: str, role: str = "assistant") -> None:
+    def _append_message(
+        self, text: str, role: Literal["user", "assistant", "system"] = "assistant"
+    ) -> None:
         """Append a message to the message list with the given role."""
-        self._messages.append(format_message_for_llm(text, role=role))
+        self._messages.append(cast(LLMMessage, format_message_for_llm(text, role=role)))

lib/crewai/src/crewai/task.py

@@ -462,6 +462,8 @@ class Task(BaseModel):
                     guardrail=self._guardrail,
                     retry_count=self.retry_count,
                     event_source=self,
+                    from_task=self,
+                    from_agent=agent,
                 )
                 if not guardrail_result.success:
                     if self.retry_count >= self.guardrail_max_retries:

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -31,6 +31,7 @@ from crewai.utilities.types import LLMMessage
 
 if TYPE_CHECKING:
     from crewai.agent import Agent
+    from crewai.lite_agent import LiteAgent
     from crewai.llm import LLM
     from crewai.task import Task
 
@@ -224,7 +225,7 @@ def get_llm_response(
     callbacks: list[Callable[..., Any]],
     printer: Printer,
     from_task: Task | None = None,
-    from_agent: Agent | None = None,
+    from_agent: Agent | LiteAgent | None = None,
 ) -> str:
     """Call the LLM and return the response, handling any invalid responses.
 

lib/crewai/src/crewai/utilities/converter.py

@@ -14,6 +14,7 @@ from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 
 if TYPE_CHECKING:
     from crewai.agent import Agent
+    from crewai.agents.agent_builder.base_agent import BaseAgent
     from crewai.llm import LLM
     from crewai.llms.base_llm import BaseLLM
 
@@ -143,7 +144,7 @@ def convert_to_model(
     result: str,
     output_pydantic: type[BaseModel] | None,
     output_json: type[BaseModel] | None,
-    agent: Agent | None = None,
+    agent: Agent | BaseAgent | None = None,
     converter_cls: type[Converter] | None = None,
 ) -> dict[str, Any] | BaseModel | str:
     """Convert a result string to a Pydantic model or JSON.
@@ -215,7 +216,7 @@ def handle_partial_json(
     result: str,
     model: type[BaseModel],
     is_json_output: bool,
-    agent: Agent | None,
+    agent: Agent | BaseAgent | None,
     converter_cls: type[Converter] | None = None,
 ) -> dict[str, Any] | BaseModel | str:
     """Handle partial JSON in a result string and convert to Pydantic model or dict.
@@ -260,7 +261,7 @@ def convert_with_instructions(
     result: str,
     model: type[BaseModel],
     is_json_output: bool,
-    agent: Agent | None,
+    agent: Agent | BaseAgent | None,
     converter_cls: type[Converter] | None = None,
 ) -> dict | BaseModel | str:
     """Convert a result string to a Pydantic model or JSON using instructions.
@@ -283,7 +284,12 @@ def convert_with_instructions(
     """
     if agent is None:
         raise TypeError("Agent must be provided if converter_cls is not specified.")
-    llm = agent.function_calling_llm or agent.llm
+
+    llm = getattr(agent, "function_calling_llm", None) or agent.llm
+
+    if llm is None:
+        raise ValueError("Agent must have a valid LLM instance for conversion")
+
     instructions = get_conversion_instructions(model=model, llm=llm)
     converter = create_converter(
         agent=agent,
@@ -299,7 +305,7 @@ def convert_with_instructions(
 
     if isinstance(exported_result, ConverterError):
         Printer().print(
-            content=f"{exported_result.message} Using raw output instead.",
+            content=f"Failed to convert result to model: {exported_result}",
             color="red",
         )
         return result
@@ -308,7 +314,7 @@ def convert_with_instructions(
 
 
 def get_conversion_instructions(
-    model: type[BaseModel], llm: BaseLLM | LLM | str
+    model: type[BaseModel], llm: BaseLLM | LLM | str | Any
 ) -> str:
     """Generate conversion instructions based on the model and LLM capabilities.
 
@@ -357,7 +363,7 @@ class CreateConverterKwargs(TypedDict, total=False):
 
 
 def create_converter(
-    agent: Agent | None = None,
+    agent: Agent | BaseAgent | None = None,
     converter_cls: type[Converter] | None = None,
     *args: Any,
     **kwargs: Unpack[CreateConverterKwargs],

lib/crewai/src/crewai/utilities/guardrail.py

@@ -7,7 +7,9 @@ from pydantic import BaseModel, Field, field_validator
 from typing_extensions import Self
 
 if TYPE_CHECKING:
-    from crewai.lite_agent import LiteAgentOutput
+    from crewai.agents.agent_builder.base_agent import BaseAgent
+    from crewai.lite_agent import LiteAgent, LiteAgentOutput
+    from crewai.task import Task
     from crewai.tasks.task_output import TaskOutput
 
 
@@ -79,6 +81,8 @@ def process_guardrail(
     guardrail: Callable[[Any], tuple[bool, Any | str]],
     retry_count: int,
     event_source: Any | None = None,
+    from_agent: BaseAgent | LiteAgent | None = None,
+    from_task: Task | None = None,
 ) -> GuardrailResult:
     """Process the guardrail for the agent output.
 
@@ -111,7 +115,12 @@ def process_guardrail(
 
     crewai_event_bus.emit(
         event_source,
-        LLMGuardrailStartedEvent(guardrail=guardrail, retry_count=retry_count),
+        LLMGuardrailStartedEvent(
+            guardrail=guardrail,
+            retry_count=retry_count,
+            from_agent=from_agent,
+            from_task=from_task,
+        ),
     )
 
     result = guardrail(output)
@@ -124,6 +133,8 @@ def process_guardrail(
             result=guardrail_result.result,
             error=guardrail_result.error,
             retry_count=retry_count,
+            from_agent=from_agent,
+            from_task=from_task,
         ),
     )
 

lib/crewai/src/crewai/utilities/tool_utils.py

@@ -12,6 +12,7 @@ from crewai.utilities.i18n import I18N
 
 if TYPE_CHECKING:
     from crewai.agent import Agent
+    from crewai.agents.agent_builder.base_agent import BaseAgent
     from crewai.llm import LLM
     from crewai.llms.base_llm import BaseLLM
     from crewai.task import Task
@@ -25,7 +26,7 @@ def execute_tool_and_check_finality(
     agent_role: str | None = None,
     tools_handler: ToolsHandler | None = None,
     task: Task | None = None,
-    agent: Agent | None = None,
+    agent: Agent | BaseAgent | None = None,
     function_calling_llm: BaseLLM | LLM | None = None,
     fingerprint_context: dict[str, str] | None = None,
 ) -> ToolResult:

lib/crewai/tests/agents/test_agent.py

@@ -2319,10 +2319,10 @@ def test_get_knowledge_search_query():
         crew = Crew(agents=[agent], tasks=[task])
         crew.kickoff()
 
-        mock_get_query.assert_called_once_with(task_prompt)
+        mock_get_query.assert_called_once_with(task_prompt, task)
 
     with patch.object(agent.llm, "call") as mock_llm_call:
-        agent._get_knowledge_search_query(task_prompt)
+        agent._get_knowledge_search_query(task_prompt, task)
 
         mock_llm_call.assert_called_once_with(
             [

lib/crewai/tests/cassettes/test_agent_execute_task_with_tool.yaml

@@ -237,4 +237,94 @@ interactions:
       - req_cdc7b25a3877bb9a7cb7c6d2645ff447
     http_version: HTTP/1.1
     status_code: 200
+- request:
+    body: '{"trace_id": "1581aff1-2567-43f4-a1f2-a2816533eb7d", "execution_type":
+      "crew", "user_identifier": null, "execution_context": {"crew_fingerprint": null,
+      "crew_name": "Unknown Crew", "flow_name": null, "crewai_version": "0.201.1",
+      "privacy_level": "standard"}, "execution_metadata": {"expected_duration_estimate":
+      300, "agent_count": 0, "task_count": 0, "flow_method_count": 0, "execution_started_at":
+      "2025-10-08T18:11:28.008595+00:00"}}'
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate, zstd
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '436'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - CrewAI-CLI/0.201.1
+      X-Crewai-Organization-Id:
+      - d3a3d10c-35db-423f-a7a4-c026030ba64d
+      X-Crewai-Version:
+      - 0.201.1
+    method: POST
+    uri: http://localhost:3000/crewai_plus/api/v1/tracing/batches
+  response:
+    body:
+      string: '{"id":"30844ebe-8ac6-4f67-939a-7a072d792654","trace_id":"1581aff1-2567-43f4-a1f2-a2816533eb7d","execution_type":"crew","crew_name":"Unknown
+        Crew","flow_name":null,"status":"running","duration_ms":null,"crewai_version":"0.201.1","privacy_level":"standard","total_events":0,"execution_context":{"crew_fingerprint":null,"crew_name":"Unknown
+        Crew","flow_name":null,"crewai_version":"0.201.1","privacy_level":"standard"},"created_at":"2025-10-08T18:11:28.353Z","updated_at":"2025-10-08T18:11:28.353Z"}'
+    headers:
+      Content-Length:
+      - '496'
+      cache-control:
+      - no-store
+      content-security-policy:
+      - 'default-src ''self'' *.crewai.com crewai.com; script-src ''self'' ''unsafe-inline''
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+        https://js-na1.hs-scripts.com https://share.descript.com/; style-src ''self''
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+      I now can give a great answer\nFinal Answer: Your final answer must be the great
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+      not a summary.\n\nThis is the context you''re working with:\nYour task is to
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+      clarity, and intrigue.\n\nBegin! This is VERY important to you, use the tools
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+      "started_at": "2025-10-08T11:18:09.887804", "finished_at": "2025-10-08T11:18:09.893985",
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+      ''The task to delegate'', ''type'': ''str''}, ''context'': {''description'':
+      ''The context for the task'', ''type'': ''str''}, ''coworker'': {''description'':
+      ''The role/name of the coworker to delegate to'', ''type'': ''str''}}\nTool
+      Description: Delegate a specific task to one of the following coworkers: Scorer\nThe
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+      ALL necessary context to execute the task, they know nothing about the task,
+      so share absolutely everything you know, don''t reference things but instead
+      explain them.\nTool Name: Ask question to coworker\nTool Arguments: {''question'':
+      {''description'': ''The question to ask'', ''type'': ''str''}, ''context'':
+      {''description'': ''The context for the question'', ''type'': ''str''}, ''coworker'':
+      {''description'': ''The role/name of the coworker to ask'', ''type'': ''str''}}\nTool
+      Description: Ask a specific question to one of the following coworkers: Scorer\nThe
+      input to this tool should be the coworker, the question you have for them, and
+      ALL necessary context to ask the question properly, they know nothing about
+      the question, so share absolutely everything you know, don''t reference things
+      but instead explain them.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought:
+      you should always think about what to do\nAction: the action to take, only one
+      name of [Delegate work to coworker, Ask question to coworker], just the name,
+      exactly as it''s written.\nAction Input: the input to the action, just a simple
+      JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation:
+      the result of the action\n```\n\nOnce all necessary information is gathered,
+      return the following format:\n\n```\nThought: I now know the final answer\nFinal
+      Answer: the final answer to the original input question\n```"}, {"role": "user",
+      "content": "\nCurrent Task: Give me an integer score between 1-5 for the following
+      title: ''The impact of AI in the future of work''\n\nThis is the expected criteria
+      for your final answer: The score of the title.\nyou MUST return the actual complete
+      content as the final answer, not a summary.\n\nBegin! This is VERY important
+      to you, use the tools available and give your best Final Answer, your job depends
+      on it!\n\nThought:"}, {"role": "assistant", "content": "Thought: To provide
+      an accurate score for the title \"The impact of AI in the future of work,\"
+      I need to delegate the task of scoring it to the Scorer, who is specialized
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+      \"Scorer\", \"task\": \"Give me an integer score between 1-5 for the following
+      title: ''The impact of AI in the future of work''\", \"context\": \"Your task
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+      4. The title ''The impact of AI in the future of work'' is clear and directly
+      addresses a highly relevant and intriguing topic. It captures the essence of
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+      "agent_id": "7aaa26da-9c22-494d-95cd-bd0a8b650e43", "agent_role": "Manager",
+      "from_task": null, "from_agent": null, "messages": [{"role": "system", "content":
+      "You are Manager. You''re great at delegating work about scoring.\nYour personal
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+      tools, and should NEVER make up tools that are not listed here:\n\nTool Name:
+      Delegate work to coworker\nTool Arguments: {''task'': {''description'': ''The
+      task to delegate'', ''type'': ''str''}, ''context'': {''description'': ''The
+      context for the task'', ''type'': ''str''}, ''coworker'': {''description'':
+      ''The role/name of the coworker to delegate to'', ''type'': ''str''}}\nTool
+      Description: Delegate a specific task to one of the following coworkers: Scorer\nThe
+      input to this tool should be the coworker, the task you want them to do, and
+      ALL necessary context to execute the task, they know nothing about the task,
+      so share absolutely everything you know, don''t reference things but instead
+      explain them.\nTool Name: Ask question to coworker\nTool Arguments: {''question'':
+      {''description'': ''The question to ask'', ''type'': ''str''}, ''context'':
+      {''description'': ''The context for the question'', ''type'': ''str''}, ''coworker'':
+      {''description'': ''The role/name of the coworker to ask'', ''type'': ''str''}}\nTool
+      Description: Ask a specific question to one of the following coworkers: Scorer\nThe
+      input to this tool should be the coworker, the question you have for them, and
+      ALL necessary context to ask the question properly, they know nothing about
+      the question, so share absolutely everything you know, don''t reference things
+      but instead explain them.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought:
+      you should always think about what to do\nAction: the action to take, only one
+      name of [Delegate work to coworker, Ask question to coworker], just the name,
+      exactly as it''s written.\nAction Input: the input to the action, just a simple
+      JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation:
+      the result of the action\n```\n\nOnce all necessary information is gathered,
+      return the following format:\n\n```\nThought: I now know the final answer\nFinal
+      Answer: the final answer to the original input question\n```"}, {"role": "user",
+      "content": "\nCurrent Task: Give me an integer score between 1-5 for the following
+      title: ''The impact of AI in the future of work''\n\nThis is the expected criteria
+      for your final answer: The score of the title.\nyou MUST return the actual complete
+      content as the final answer, not a summary.\n\nBegin! This is VERY important
+      to you, use the tools available and give your best Final Answer, your job depends
+      on it!\n\nThought:"}, {"role": "assistant", "content": "Thought: To provide
+      an accurate score for the title \"The impact of AI in the future of work,\"
+      I need to delegate the task of scoring it to the Scorer, who is specialized
+      in this activity.\n\nAction: Delegate work to coworker\nAction Input: {\"coworker\":
+      \"Scorer\", \"task\": \"Give me an integer score between 1-5 for the following
+      title: ''The impact of AI in the future of work''\", \"context\": \"Your task
+      is to evaluate the provided title on a scale from 1 to 5 based on criteria such
+      as relevance, clarity, and intrigue.\"}\n\nObservation: The scorer has received
+      the task to score the title ''The impact of AI in the future of work''.\nObservation:
+      4. The title ''The impact of AI in the future of work'' is clear and directly
+      addresses a highly relevant and intriguing topic. It captures the essence of
+      how AI could shape the job market, which is a subject of significant interest.
+      However, it could be more specific or detailed to achieve a perfect score."}],
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+      ''The impact of AI in the future of work''", "task_id": "5c23750c-affd-43fc-ad97-920bd15342e3",
+      "agent_id": "2fee0a63-ccf0-408a-9368-1b95eb6d628e", "agent_role": "Crew Manager",
+      "from_task": null, "from_agent": null, "messages": [{"role": "system", "content":
+      "You are Crew Manager. You are a seasoned manager with a knack for getting the
+      best out of your team.\nYou are also known for your ability to delegate work
+      to the right people, and to ask the right questions to get the best out of your
+      team.\nEven though you don''t perform tasks by yourself, you have a lot of experience
+      in the field, which allows you to properly evaluate the work of your team members.\nYour
+      personal goal is: Manage the team to complete the task in the best way possible.\nYou
+      ONLY have access to the following tools, and should NEVER make up tools that
+      are not listed here:\n\nTool Name: Delegate work to coworker\nTool Arguments:
+      {''task'': {''description'': ''The task to delegate'', ''type'': ''str''}, ''context'':
+      {''description'': ''The context for the task'', ''type'': ''str''}, ''coworker'':
+      {''description'': ''The role/name of the coworker to delegate to'', ''type'':
+      ''str''}}\nTool Description: Delegate a specific task to one of the following
+      coworkers: Scorer\nThe input to this tool should be the coworker, the task you
+      want them to do, and ALL necessary context to execute the task, they know nothing
+      about the task, so share absolutely everything you know, don''t reference things
+      but instead explain them.\nTool Name: Ask question to coworker\nTool Arguments:
+      {''question'': {''description'': ''The question to ask'', ''type'': ''str''},
+      ''context'': {''description'': ''The context for the question'', ''type'': ''str''},
+      ''coworker'': {''description'': ''The role/name of the coworker to ask'', ''type'':
+      ''str''}}\nTool Description: Ask a specific question to one of the following
+      coworkers: Scorer\nThe input to this tool should be the coworker, the question
+      you have for them, and ALL necessary context to ask the question properly, they
+      know nothing about the question, so share absolutely everything you know, don''t
+      reference things but instead explain them.\n\nIMPORTANT: Use the following format
+      in your response:\n\n```\nThought: you should always think about what to do\nAction:
+      the action to take, only one name of [Delegate work to coworker, Ask question
+      to coworker], just the name, exactly as it''s written.\nAction Input: the input
+      to the action, just a simple JSON object, enclosed in curly braces, using \"
+      to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce
+      all necessary information is gathered, return the following format:\n\n```\nThought:
+      I now know the final answer\nFinal Answer: the final answer to the original
+      input question\n```"}, {"role": "user", "content": "\nCurrent Task: Give me
+      an integer score between 1-5 for the following title: ''The impact of AI in
+      the future of work''\n\nThis is the expected criteria for your final answer:
+      The score of the title.\nyou MUST return the actual complete content as the
+      final answer, not a summary.\nEnsure your final answer contains only the content
+      in the following format: {\n  \"score\": int\n}\n\nEnsure the final output does
+      not include any code block markers like ```json or ```python.\n\nBegin! This
+      is VERY important to you, use the tools available and give your best Final Answer,
+      your job depends on it!\n\nThought:"}], "response": "To provide the score for
+      the title \"The impact of AI in the future of work,\" I need to ask Scorer to
+      evaluate this based on the criteria they use. \n\nAction: Ask question to coworker\nAction
+      Input: {\"question\": \"Can you provide an integer score between 1-5 for the
+      title ''The impact of AI in the future of work''?\", \"context\": \"We need
+      to evaluate this title based on your criteria for scoring titles. The context
+      for the scoring revolves entirely around the perceived impact of AI on the future
+      of work.\", \"coworker\": \"Scorer\"}", "call_type": "<LLMCallType.LLM_CALL:
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+      "timestamp": "2025-10-08T18:18:08.558720+00:00", "type": "tool_usage_started",
+      "event_data": {"timestamp": "2025-10-08T18:18:08.558636+00:00", "type": "tool_usage_started",
+      "source_fingerprint": "7ef5b626-3760-4b79-900f-65432b536f7c", "source_type":
+      "agent", "fingerprint_metadata": null, "agent_key": "6b5becc64d7e3c705a7d3784a5fab1d3",
+      "agent_role": "Crew Manager", "agent_id": null, "tool_name": "Ask question to
+      coworker", "tool_args": "{\"question\": \"Can you provide an integer score between
+      1-5 for the title ''The impact of AI in the future of work''?\", \"context\":
+      \"We need to evaluate this title based on your criteria for scoring titles.
+      The context for the scoring revolves entirely around the perceived impact of
+      AI on the future of work.\", \"coworker\": \"Scorer\"}", "tool_class": "Ask
+      question to coworker", "run_attempts": null, "delegations": null, "agent": {"id":
+      "2fee0a63-ccf0-408a-9368-1b95eb6d628e", "role": "Crew Manager", "goal": "Manage
+      the team to complete the task in the best way possible.", "backstory": "You
+      are a seasoned manager with a knack for getting the best out of your team.\nYou
+      are also known for your ability to delegate work to the right people, and to
+      ask the right questions to get the best out of your team.\nEven though you don''t
+      perform tasks by yourself, you have a lot of experience in the field, which
+      allows you to properly evaluate the work of your team members.", "cache": true,
+      "verbose": false, "max_rpm": null, "allow_delegation": true, "tools": [{"name":
+      "''Delegate work to coworker''", "description": "\"Tool Name: Delegate work
+      to coworker\\nTool Arguments: {''task'': {''description'': ''The task to delegate'',
+      ''type'': ''str''}, ''context'': {''description'': ''The context for the task'',
+      ''type'': ''str''}, ''coworker'': {''description'': ''The role/name of the coworker
+      to delegate to'', ''type'': ''str''}}\\nTool Description: Delegate a specific
+      task to one of the following coworkers: Scorer\\nThe input to this tool should
+      be the coworker, the task you want them to do, and ALL necessary context to
+      execute the task, they know nothing about the task, so share absolutely everything
+      you know, don''t reference things but instead explain them.\"", "env_vars":
+      "[]", "args_schema": "<class ''crewai.tools.agent_tools.delegate_work_tool.DelegateWorkToolSchema''>",
+      "description_updated": "False", "cache_function": "<function BaseTool.<lambda>
+      at 0x1127be8e0>", "result_as_answer": "False", "max_usage_count": "None", "current_usage_count":
+      "0"}, {"name": "''Ask question to coworker''", "description": "\"Tool Name:
+      Ask question to coworker\\nTool Arguments: {''question'': {''description'':
+      ''The question to ask'', ''type'': ''str''}, ''context'': {''description'':
+      ''The context for the question'', ''type'': ''str''}, ''coworker'': {''description'':
+      ''The role/name of the coworker to ask'', ''type'': ''str''}}\\nTool Description:
+      Ask a specific question to one of the following coworkers: Scorer\\nThe input
+      to this tool should be the coworker, the question you have for them, and ALL
+      necessary context to ask the question properly, they know nothing about the
+      question, so share absolutely everything you know, don''t reference things but
+      instead explain them.\"", "env_vars": "[]", "args_schema": "<class ''crewai.tools.agent_tools.ask_question_tool.AskQuestionToolSchema''>",
+      "description_updated": "False", "cache_function": "<function BaseTool.<lambda>
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+      "0"}], "max_iter": 25, "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor
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+      0, ''tools_errors'': 0, ''delegations'': 0, ''i18n'': {''prompt_file'': None},
+      ''name'': None, ''prompt_context'': '''', ''description'': \"Give me an integer
+      score between 1-5 for the following title: ''The impact of AI in the future
+      of work''\", ''expected_output'': ''The score of the title.'', ''config'': None,
+      ''callback'': None, ''agent'': {''id'': UUID(''2fee0a63-ccf0-408a-9368-1b95eb6d628e''),
+      ''role'': ''Crew Manager'', ''goal'': ''Manage the team to complete the task
+      in the best way possible.'', ''backstory'': \"You are a seasoned manager with
+      a knack for getting the best out of your team.\\nYou are also known for your
+      ability to delegate work to the right people, and to ask the right questions
+      to get the best out of your team.\\nEven though you don''t perform tasks by
+      yourself, you have a lot of experience in the field, which allows you to properly
+      evaluate the work of your team members.\", ''cache'': True, ''verbose'': False,
+      ''max_rpm'': None, ''allow_delegation'': True, ''tools'': [{''name'': ''Delegate
+      work to coworker'', ''description'': \"Tool Name: Delegate work to coworker\\nTool
+      Arguments: {''task'': {''description'': ''The task to delegate'', ''type'':
+      ''str''}, ''context'': {''description'': ''The context for the task'', ''type'':
+      ''str''}, ''coworker'': {''description'': ''The role/name of the coworker to
+      delegate to'', ''type'': ''str''}}\\nTool Description: Delegate a specific task
+      to one of the following coworkers: Scorer\\nThe input to this tool should be
+      the coworker, the task you want them to do, and ALL necessary context to execute
+      the task, they know nothing about the task, so share absolutely everything you
+      know, don''t reference things but instead explain them.\", ''env_vars'': [],
+      ''args_schema'': <class ''crewai.tools.agent_tools.delegate_work_tool.DelegateWorkToolSchema''>,
+      ''description_updated'': False, ''cache_function'': <function BaseTool.<lambda>
+      at 0x1127be8e0>, ''result_as_answer'': False, ''max_usage_count'': None, ''current_usage_count'':
+      0}, {''name'': ''Ask question to coworker'', ''description'': \"Tool Name: Ask
+      question to coworker\\nTool Arguments: {''question'': {''description'': ''The
+      question to ask'', ''type'': ''str''}, ''context'': {''description'': ''The
+      context for the question'', ''type'': ''str''}, ''coworker'': {''description'':
+      ''The role/name of the coworker to ask'', ''type'': ''str''}}\\nTool Description:
+      Ask a specific question to one of the following coworkers: Scorer\\nThe input
+      to this tool should be the coworker, the question you have for them, and ALL
+      necessary context to ask the question properly, they know nothing about the
+      question, so share absolutely everything you know, don''t reference things but
+      instead explain them.\", ''env_vars'': [], ''args_schema'': <class ''crewai.tools.agent_tools.ask_question_tool.AskQuestionToolSchema''>,
+      ''description_updated'': False, ''cache_function'': <function BaseTool.<lambda>
+      at 0x1127be8e0>, ''result_as_answer'': False, ''max_usage_count'': None, ''current_usage_count'':
+      0}], ''max_iter'': 25, ''agent_executor'': <crewai.agents.crew_agent_executor.CrewAgentExecutor
+      object at 0x136ce03b0>, ''llm'': <crewai.llm.LLM object at 0x136d04050>, ''crew'':
+      Crew(id=7237e268-30a9-4eca-b754-a6dea72b2dfc, process=Process.hierarchical,
+      number_of_agents=1, number_of_tasks=1), ''i18n'': {''prompt_file'': None}, ''cache_handler'':
+      {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler object at 0x136aef8b0>,
+      ''tools_results'': [], ''max_tokens'': None, ''knowledge'': None, ''knowledge_sources'':
+      None, ''knowledge_storage'': None, ''security_config'': {''fingerprint'': {''metadata'':
+      {}}}, ''callbacks'': [], ''adapted_agent'': False, ''knowledge_config'': None},
+      ''context'': NOT_SPECIFIED, ''async_execution'': False, ''output_json'': <class
+      ''tests.test_task.test_output_json_hierarchical.<locals>.ScoreOutput''>, ''output_pydantic'':
+      None, ''output_file'': None, ''create_directory'': True, ''output'': None, ''tools'':
+      [], ''security_config'': {''fingerprint'': {''metadata'': {}}}, ''id'': UUID(''5c23750c-affd-43fc-ad97-920bd15342e3''),
+      ''human_input'': False, ''markdown'': False, ''converter_cls'': None, ''processed_by_agents'':
+      {''Crew Manager''}, ''guardrail'': None, ''max_retries'': None, ''guardrail_max_retries'':
+      3, ''retry_count'': 0, ''start_time'': datetime.datetime(2025, 10, 8, 11, 18,
+      8, 554345), ''end_time'': None, ''allow_crewai_trigger_context'': None}"], "agents":
+      ["{''id'': UUID(''16778b49-13e6-4cfc-bc0c-c95ebc698cb5''), ''role'': ''Scorer'',
+      ''goal'': ''Score the title'', ''backstory'': \"You''re an expert scorer, specialized
+      in scoring titles.\", ''cache'': True, ''verbose'': False, ''max_rpm'': None,
+      ''allow_delegation'': False, ''tools'': [], ''max_iter'': 25, ''agent_executor'':
+      <crewai.agents.crew_agent_executor.CrewAgentExecutor object at 0x136ce0290>,
+      ''llm'': <crewai.llm.LLM object at 0x136a97950>, ''crew'': Crew(id=7237e268-30a9-4eca-b754-a6dea72b2dfc,
+      process=Process.hierarchical, number_of_agents=1, number_of_tasks=1), ''i18n'':
+      {''prompt_file'': None}, ''cache_handler'': {}, ''tools_handler'': <crewai.agents.tools_handler.ToolsHandler
+      object at 0x133fd0710>, ''tools_results'': [], ''max_tokens'': None, ''knowledge'':
+      None, ''knowledge_sources'': None, ''knowledge_storage'': None, ''security_config'':
+      {''fingerprint'': {''metadata'': {}}}, ''callbacks'': [], ''adapted_agent'':
+      False, ''knowledge_config'': None}"], "process": "hierarchical", "verbose":
+      false, "memory": false, "short_term_memory": null, "long_term_memory": null,
+      "entity_memory": null, "external_memory": null, "embedder": null, "usage_metrics":
+      null, "manager_llm": "<crewai.llm.LLM object at 0x136d04050>", "manager_agent":
+      {"id": "UUID(''2fee0a63-ccf0-408a-9368-1b95eb6d628e'')", "role": "''Crew Manager''",
+      "goal": "''Manage the team to complete the task in the best way possible.''",
+      "backstory": "\"You are a seasoned manager with a knack for getting the best
+      out of your team.\\nYou are also known for your ability to delegate work to
+      the right people, and to ask the right questions to get the best out of your
+      team.\\nEven though you don''t perform tasks by yourself, you have a lot of
+      experience in the field, which allows you to properly evaluate the work of your
+      team members.\"", "cache": "True", "verbose": "False", "max_rpm": "None", "allow_delegation":
+      "True", "tools": "[{''name'': ''Delegate work to coworker'', ''description'':
+      \"Tool Name: Delegate work to coworker\\nTool Arguments: {''task'': {''description'':
+      ''The task to delegate'', ''type'': ''str''}, ''context'': {''description'':
+      ''The context for the task'', ''type'': ''str''}, ''coworker'': {''description'':
+      ''The role/name of the coworker to delegate to'', ''type'': ''str''}}\\nTool
+      Description: Delegate a specific task to one of the following coworkers: Scorer\\nThe
+      input to this tool should be the coworker, the task you want them to do, and
+      ALL necessary context to execute the task, they know nothing about the task,
+      so share absolutely everything you know, don''t reference things but instead
+      explain them.\", ''env_vars'': [], ''args_schema'': <class ''crewai.tools.agent_tools.delegate_work_tool.DelegateWorkToolSchema''>,
+      ''description_updated'': False, ''cache_function'': <function BaseTool.<lambda>
+      at 0x1127be8e0>, ''result_as_answer'': False, ''max_usage_count'': None, ''current_usage_count'':
+      0}, {''name'': ''Ask question to coworker'', ''description'': \"Tool Name: Ask
+      question to coworker\\nTool Arguments: {''question'': {''description'': ''The
+      question to ask'', ''type'': ''str''}, ''context'': {''description'': ''The
+      context for the question'', ''type'': ''str''}, ''coworker'': {''description'':
+      ''The role/name of the coworker to ask'', ''type'': ''str''}}\\nTool Description:
+      Ask a specific question to one of the following coworkers: Scorer\\nThe input
+      to this tool should be the coworker, the question you have for them, and ALL
+      necessary context to ask the question properly, they know nothing about the
+      question, so share absolutely everything you know, don''t reference things but
+      instead explain them.\", ''env_vars'': [], ''args_schema'': <class ''crewai.tools.agent_tools.ask_question_tool.AskQuestionToolSchema''>,
+      ''description_updated'': False, ''cache_function'': <function BaseTool.<lambda>
+      at 0x1127be8e0>, ''result_as_answer'': False, ''max_usage_count'': None, ''current_usage_count'':
+      0}]", "max_iter": "25", "agent_executor": "<crewai.agents.crew_agent_executor.CrewAgentExecutor
+      object at 0x136ce03b0>", "llm": "<crewai.llm.LLM object at 0x136d04050>", "crew":
+      "Crew(id=7237e268-30a9-4eca-b754-a6dea72b2dfc, process=Process.hierarchical,
+      number_of_agents=1, number_of_tasks=1)", "i18n": "{''prompt_file'': None}",
+      "cache_handler": "{}", "tools_handler": "<crewai.agents.tools_handler.ToolsHandler
+      object at 0x136aef8b0>", "tools_results": "[]", "max_tokens": "None", "knowledge":
+      "None", "knowledge_sources": "None", "knowledge_storage": "None", "security_config":
+      "{''fingerprint'': {''metadata'': {}}}", "callbacks": "[]", "adapted_agent":
+      "False", "knowledge_config": "None"}, "function_calling_llm": null, "config":
+      null, "id": "7237e268-30a9-4eca-b754-a6dea72b2dfc", "share_crew": false, "step_callback":
+      null, "task_callback": null, "before_kickoff_callbacks": [], "after_kickoff_callbacks":
+      [], "max_rpm": null, "prompt_file": null, "output_log_file": null, "planning":
+      false, "planning_llm": null, "task_execution_output_json_files": null, "execution_logs":
+      [], "knowledge_sources": null, "chat_llm": null, "knowledge": null, "security_config":
+      {"fingerprint": "{''metadata'': {}}"}, "token_usage": null, "tracing": false},
+      "i18n": {"prompt_file": null}, "cache_handler": {}, "tools_handler": "<crewai.agents.tools_handler.ToolsHandler
+      object at 0x136aef8b0>", "tools_results": [], "max_tokens": null, "knowledge":
+      null, "knowledge_sources": null, "knowledge_storage": null, "security_config":
+      {"fingerprint": {"metadata": "{}"}}, "callbacks": [], "adapted_agent": false,
+      "knowledge_config": null, "max_execution_time": null, "agent_ops_agent_name":
+      "Crew Manager", "agent_ops_agent_id": null, "step_callback": null, "use_system_prompt":
+      true, "function_calling_llm": null, "system_template": null, "prompt_template":
+      null, "response_template": null, "allow_code_execution": false, "respect_context_window":
+      true, "max_retry_limit": 2, "multimodal": false, "inject_date": false, "date_format":
+      "%Y-%m-%d", "code_execution_mode": "safe", "reasoning": false, "max_reasoning_attempts":
+      null, "embedder": null, "agent_knowledge_context": null, "crew_knowledge_context":
+      null, "knowledge_search_query": null, "from_repository": null, "guardrail":
+      null, "guardrail_max_retries": 3}, "task_name": "Give me an integer score between
+      1-5 for the following title: ''The impact of AI in the future of work''", "task_id":
+      "5c23750c-affd-43fc-ad97-920bd15342e3", "from_task": null, "from_agent": null}},
+      {"event_id": "b666ec5d-63b6-4d97-86f9-7ce85babdadd", "timestamp": "2025-10-08T18:18:08.560511+00:00",
+      "type": "agent_execution_started", "event_data": {"agent_role": "Scorer", "agent_goal":
+      "Score the title", "agent_backstory": "You''re an expert scorer, specialized
+      in scoring titles."}}, {"event_id": "ab280ea3-e649-4ed5-87f1-00363f28a5c9",
+      "timestamp": "2025-10-08T18:18:08.560588+00:00", "type": "llm_call_started",
+      "event_data": {"timestamp": "2025-10-08T18:18:08.560564+00:00", "type": "llm_call_started",
+      "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null,
+      "task_name": "Can you provide an integer score between 1-5 for the title ''The
+      impact of AI in the future of work''?", "task_id": "d249cc86-962d-45ff-848d-46ebf36fb14f",
+      "agent_id": "16778b49-13e6-4cfc-bc0c-c95ebc698cb5", "agent_role": "Scorer",
+      "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": [{"role":
+      "system", "content": "You are Scorer. You''re an expert scorer, specialized
+      in scoring titles.\nYour personal goal is: Score the title\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": "\nCurrent
+      Task: Can you provide an integer score between 1-5 for the title ''The impact
+      of AI in the future of work''?\n\nThis is the expected criteria for your final
+      answer: Your best answer to your coworker asking you this, accounting for the
+      context shared.\nyou MUST return the actual complete content as the final answer,
+      not a summary.\n\nThis is the context you''re working with:\nWe need to evaluate
+      this title based on your criteria for scoring titles. The context for the scoring
+      revolves entirely around the perceived impact of AI on the future of work.\n\nBegin!
+      This is VERY important to you, use the tools available and give your best Final
+      Answer, your job depends on it!\n\nThought:"}], "tools": null, "callbacks":
+      ["<crewai.utilities.token_counter_callback.TokenCalcHandler object at 0x136cad8b0>"],
+      "available_functions": null}}, {"event_id": "4c9b120f-bed5-4398-8a53-7e581b552a04",
+      "timestamp": "2025-10-08T18:18:08.564442+00:00", "type": "llm_call_completed",
+      "event_data": {"timestamp": "2025-10-08T18:18:08.564412+00:00", "type": "llm_call_completed",
+      "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null,
+      "task_name": "Can you provide an integer score between 1-5 for the title ''The
+      impact of AI in the future of work''?", "task_id": "d249cc86-962d-45ff-848d-46ebf36fb14f",
+      "agent_id": "16778b49-13e6-4cfc-bc0c-c95ebc698cb5", "agent_role": "Scorer",
+      "from_task": null, "from_agent": null, "messages": [{"role": "system", "content":
+      "You are Scorer. You''re an expert scorer, specialized in scoring titles.\nYour
+      personal goal is: Score the title\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": "\nCurrent Task: Can you
+      provide an integer score between 1-5 for the title ''The impact of AI in the
+      future of work''?\n\nThis is the expected criteria for your final answer: Your
+      best answer to your coworker asking you this, accounting for the context shared.\nyou
+      MUST return the actual complete content as the final answer, not a summary.\n\nThis
+      is the context you''re working with:\nWe need to evaluate this title based on
+      your criteria for scoring titles. The context for the scoring revolves entirely
+      around the perceived impact of AI on the future of work.\n\nBegin! This is VERY
+      important to you, use the tools available and give your best Final Answer, your
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