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Lorenze/better tracing events (#3382)
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* feat: implement tool usage limit exception handling

- Introduced `ToolUsageLimitExceeded` exception to manage maximum usage limits for tools.
- Enhanced `CrewStructuredTool` to check and raise this exception when the usage limit is reached.
- Updated `_run` and `_execute` methods to include usage limit checks and handle exceptions appropriately, improving reliability and user feedback.

* feat: enhance PlusAPI and ToolUsage with task metadata

- Removed the `send_trace_batch` method from PlusAPI to streamline the API.
- Added timeout parameters to trace event methods in PlusAPI for improved reliability.
- Updated ToolUsage to include task metadata (task name and ID) in event emissions, enhancing traceability and context during tool usage.
- Refactored event handling in LLM and ToolUsage events to ensure task information is consistently captured.

* feat: enhance memory and event handling with task and agent metadata

- Added task and agent metadata to various memory and event classes, improving traceability and context during memory operations.
- Updated the `ContextualMemory` and `Memory` classes to associate tasks and agents, allowing for better context management.
- Enhanced event emissions in `LLM`, `ToolUsage`, and memory events to include task and agent information, facilitating improved debugging and monitoring.
- Refactored event handling to ensure consistent capture of task and agent details across the system.

* drop

* refactor: clean up unused imports in memory and event modules

- Removed unused TYPE_CHECKING imports from long_term_memory.py to streamline the code.
- Eliminated unnecessary import from memory_events.py, enhancing clarity and maintainability.

* fix memory tests

* fix task_completed payload

* fix: remove unused test agent variable in external memory tests

* refactor: remove unused agent parameter from Memory class save method

- Eliminated the agent parameter from the save method in the Memory class to streamline the code and improve clarity.
- Updated the TraceBatchManager class by moving initialization of attributes into the constructor for better organization and readability.

* refactor: enhance ExecutionState and ReasoningEvent classes with optional task and agent identifiers

- Added optional `current_agent_id` and `current_task_id` attributes to the `ExecutionState` class for better tracking of agent and task states.
- Updated the `from_task` attribute in the `ReasoningEvent` class to use `Optional[Any]` instead of a specific type, improving flexibility in event handling.

* refactor: update ExecutionState class by removing unused agent and task identifiers

- Removed the `current_agent_id` and `current_task_id` attributes from the `ExecutionState` class to simplify the code and enhance clarity.
- Adjusted the import statements to include `Optional` for better type handling.

* refactor: streamline LLM event handling in LiteAgent

- Removed unused LLM event emissions (LLMCallStartedEvent, LLMCallCompletedEvent, LLMCallFailedEvent) from the LiteAgent class to simplify the code and improve performance.
- Adjusted the flow of LLM response handling by eliminating unnecessary event bus interactions, enhancing clarity and maintainability.

* flow ownership and not emitting events when a crew is done

* refactor: remove unused agent parameter from ShortTermMemory save method

- Eliminated the agent parameter from the save method in the ShortTermMemory class to streamline the code and improve clarity.
- This change enhances the maintainability of the memory management system by reducing unnecessary complexity.

* runtype check fix

* fixing tests

* fix lints

* fix: update event assertions in test_llm_emits_event_with_lite_agent

- Adjusted the expected counts for completed and started events in the test to reflect the correct behavior of the LiteAgent.
- Updated assertions for agent roles and IDs to match the expected values after recent changes in event handling.

* fix: update task name assertions in event tests

- Modified assertions in `test_stream_llm_emits_event_with_task_and_agent_info` and `test_llm_emits_event_with_task_and_agent_info` to use `task.description` as a fallback for `task.name`. This ensures that the tests correctly validate the task name even when it is not explicitly set.

* fix: update test assertions for output values and improve readability

- Updated assertions in `test_output_json_dict_hierarchical` to reflect the correct expected score value.
- Enhanced readability of assertions in `test_output_pydantic_to_another_task` and `test_key` by formatting the error messages for clarity.
- These changes ensure that the tests accurately validate the expected outputs and improve overall code quality.

* test fixes

* fix crew_test

* added another fixture

* fix: ensure agent and task assignments in contextual memory are conditional

- Updated the ContextualMemory class to check for the existence of short-term, long-term, external, and extended memory before assigning agent and task attributes. This prevents potential attribute errors when memory types are not initialized.
This commit is contained in:
Lorenze Jay
2025-08-26 09:09:46 -07:00
committed by GitHub
parent 4b4a119a9f
commit 7addda9398
43 changed files with 5151 additions and 295 deletions
Download Patch File Download Diff File Expand all files Collapse all files

7
src/crewai/agent.py
View File

@@ -320,15 +320,20 @@ class Agent(BaseAgent):
event=MemoryRetrievalStartedEvent(
task_id=str(task.id) if task else None,
source_type="agent",
from_agent=self,
from_task=task,
),
)
start_time = time.time()
contextual_memory = ContextualMemory(
self.crew._short_term_memory,
self.crew._long_term_memory,
self.crew._entity_memory,
self.crew._external_memory,
agent=self,
task=task,
)
memory = contextual_memory.build_context_for_task(task, context)
if memory.strip() != "":
@@ -341,6 +346,8 @@ class Agent(BaseAgent):
memory_content=memory,
retrieval_time_ms=(time.time() - start_time) * 1000,
source_type="agent",
from_agent=self,
from_task=task,
),
)
knowledge_config = (

6
src/crewai/agents/agent_builder/base_agent_executor_mixin.py
View File

@@ -43,7 +43,6 @@ class CrewAgentExecutorMixin:
metadata={
"observation": self.task.description,
},
agent=self.agent.role,
)
except Exception as e:
print(f"Failed to add to short term memory: {e}")
@@ -65,7 +64,6 @@ class CrewAgentExecutorMixin:
"description": self.task.description,
"messages": self.messages,
},
agent=self.agent.role,
)
except Exception as e:
print(f"Failed to add to external memory: {e}")
@@ -158,7 +156,9 @@ class CrewAgentExecutorMixin:
self._printer.print(content=prompt, color="bold_yellow")
response = input()
if response.strip() != "":
self._printer.print(content="\nProcessing your feedback...", color="cyan")
self._printer.print(
content="\nProcessing your feedback...", color="cyan"
)
return response
finally:
event_listener.formatter.resume_live_updates()

5
src/crewai/cli/plus_api.py
View File

@@ -117,9 +117,6 @@ class PlusAPI:
def get_organizations(self) -> requests.Response:
return self._make_request("GET", self.ORGANIZATIONS_RESOURCE)
def send_trace_batch(self, payload) -> requests.Response:
return self._make_request("POST", self.TRACING_RESOURCE, json=payload)
def initialize_trace_batch(self, payload) -> requests.Response:
return self._make_request(
"POST", f"{self.TRACING_RESOURCE}/batches", json=payload
@@ -135,6 +132,7 @@ class PlusAPI:
"POST",
f"{self.TRACING_RESOURCE}/batches/{trace_batch_id}/events",
json=payload,
timeout=30,
)
def send_ephemeral_trace_events(
@@ -144,6 +142,7 @@ class PlusAPI:
"POST",
f"{self.EPHEMERAL_TRACING_RESOURCE}/batches/{trace_batch_id}/events",
json=payload,
timeout=30,
)
def finalize_trace_batch(self, trace_batch_id: str, payload) -> requests.Response:

7
src/crewai/experimental/evaluation/agent_evaluator.py
View File

@@ -1,5 +1,5 @@
import threading
from typing import Any
from typing import Any, Optional
from crewai.experimental.evaluation.base_evaluator import AgentEvaluationResult, AggregationStrategy
from crewai.agent import Agent
@@ -15,10 +15,11 @@ from crewai.utilities.events.agent_events import LiteAgentExecutionCompletedEven
from crewai.experimental.evaluation.base_evaluator import AgentAggregatedEvaluationResult, EvaluationScore, MetricCategory
class ExecutionState:
current_agent_id: Optional[str] = None
current_task_id: Optional[str] = None
def __init__(self):
self.traces = {}
self.current_agent_id: str | None = None
self.current_task_id: str | None = None
self.iteration = 1
self.iterations_results = {}
self.agent_evaluators = {}

36
src/crewai/lite_agent.py
View File

@@ -69,12 +69,7 @@ from crewai.utilities.events.agent_events import (
LiteAgentExecutionStartedEvent,
)
from crewai.utilities.events.crewai_event_bus import crewai_event_bus
from crewai.utilities.events.llm_events import (
LLMCallCompletedEvent,
LLMCallFailedEvent,
LLMCallStartedEvent,
LLMCallType,
)
from crewai.utilities.llm_utils import create_llm
from crewai.utilities.printer import Printer
from crewai.utilities.token_counter_callback import TokenCalcHandler
@@ -519,19 +514,6 @@ class LiteAgent(FlowTrackable, BaseModel):
enforce_rpm_limit(self.request_within_rpm_limit)
llm = cast(LLM, self.llm)
model = llm.model if hasattr(llm, "model") else "unknown"
crewai_event_bus.emit(
self,
event=LLMCallStartedEvent(
messages=self._messages,
tools=None,
callbacks=self._callbacks,
from_agent=self,
model=model,
),
)
try:
answer = get_llm_response(
llm=cast(LLM, self.llm),
@@ -541,23 +523,7 @@ class LiteAgent(FlowTrackable, BaseModel):
from_agent=self,
)
# Emit LLM call completed event
crewai_event_bus.emit(
self,
event=LLMCallCompletedEvent(
messages=self._messages,
response=answer,
call_type=LLMCallType.LLM_CALL,
from_agent=self,
model=model,
),
)
except Exception as e:
# Emit LLM call failed event
crewai_event_bus.emit(
self,
event=LLMCallFailedEvent(error=str(e), from_agent=self),
)
raise e
formatted_answer = process_llm_response(answer, self.use_stop_words)

21
src/crewai/llm.py
View File

@@ -851,7 +851,9 @@ class LLM(BaseLLM):
return tool_calls
# --- 7) Handle tool calls if present
tool_result = self._handle_tool_call(tool_calls, available_functions)
tool_result = self._handle_tool_call(
tool_calls, available_functions, from_task, from_agent
)
if tool_result is not None:
return tool_result
# --- 8) If tool call handling didn't return a result, emit completion event and return text response
@@ -868,6 +870,8 @@ class LLM(BaseLLM):
self,
tool_calls: List[Any],
available_functions: Optional[Dict[str, Any]] = None,
from_task: Optional[Any] = None,
from_agent: Optional[Any] = None,
) -> Optional[str]:
"""Handle a tool call from the LLM.
@@ -902,6 +906,8 @@ class LLM(BaseLLM):
event=ToolUsageStartedEvent(
tool_name=function_name,
tool_args=function_args,
from_agent=from_agent,
from_task=from_task,
),
)
@@ -914,12 +920,17 @@ class LLM(BaseLLM):
tool_args=function_args,
started_at=started_at,
finished_at=datetime.now(),
from_task=from_task,
from_agent=from_agent,
),
)
# --- 3.3) Emit success event
self._handle_emit_call_events(
response=result, call_type=LLMCallType.TOOL_CALL
response=result,
call_type=LLMCallType.TOOL_CALL,
from_task=from_task,
from_agent=from_agent,
)
return result
except Exception as e:
@@ -1139,7 +1150,11 @@ class LLM(BaseLLM):
# TODO: Remove this code after merging PR https://github.com/BerriAI/litellm/pull/10917
# Ollama doesn't supports last message to be 'assistant'
if "ollama" in self.model.lower() and messages and messages[-1]["role"] == "assistant":
if (
"ollama" in self.model.lower()
and messages
and messages[-1]["role"] == "assistant"
):
return messages + [{"role": "user", "content": ""}]
# Handle Anthropic models

33
src/crewai/memory/contextual/contextual_memory.py
View File

@@ -1,4 +1,4 @@
from typing import Optional
from typing import Optional, TYPE_CHECKING
from crewai.memory import (
EntityMemory,
@@ -7,6 +7,10 @@ from crewai.memory import (
ShortTermMemory,
)
if TYPE_CHECKING:
from crewai.agent import Agent
from crewai.task import Task
class ContextualMemory:
def __init__(
@@ -15,11 +19,28 @@ class ContextualMemory:
ltm: LongTermMemory,
em: EntityMemory,
exm: ExternalMemory,
agent: Optional["Agent"] = None,
task: Optional["Task"] = None,
):
self.stm = stm
self.ltm = ltm
self.em = em
self.exm = exm
self.agent = agent
self.task = task
if self.stm is not None:
self.stm.agent = self.agent
self.stm.task = self.task
if self.ltm is not None:
self.ltm.agent = self.agent
self.ltm.task = self.task
if self.em is not None:
self.em.agent = self.agent
self.em.task = self.task
if self.exm is not None:
self.exm.agent = self.agent
self.exm.task = self.task
def build_context_for_task(self, task, context) -> str:
"""
@@ -49,10 +70,7 @@ class ContextualMemory:
stm_results = self.stm.search(query)
formatted_results = "\n".join(
[
f"- {result['context']}"
for result in stm_results
]
[f"- {result['context']}" for result in stm_results]
)
return f"Recent Insights:\n{formatted_results}" if stm_results else ""
@@ -89,10 +107,7 @@ class ContextualMemory:
em_results = self.em.search(query)
formatted_results = "\n".join(
[
f"- {result['context']}"
for result in em_results
] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
[f"- {result['context']}" for result in em_results] # type: ignore # Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
)
return f"Entities:\n{formatted_results}" if em_results else ""

12
src/crewai/memory/entity/entity_memory.py
View File

@@ -35,7 +35,7 @@ class EntityMemory(Memory):
raise ImportError(
"Mem0 is not installed. Please install it with `pip install mem0ai`."
)
config = embedder_config.get("config")
config = embedder_config.get("config") if embedder_config else None
storage = Mem0Storage(type="short_term", crew=crew, config=config)
else:
storage = (
@@ -60,6 +60,8 @@ class EntityMemory(Memory):
event=MemorySaveStartedEvent(
metadata=item.metadata,
source_type="entity_memory",
from_agent=self.agent,
from_task=self.task,
),
)
@@ -85,6 +87,8 @@ class EntityMemory(Memory):
metadata=item.metadata,
save_time_ms=(time.time() - start_time) * 1000,
source_type="entity_memory",
from_agent=self.agent,
from_task=self.task,
),
)
except Exception as e:
@@ -94,6 +98,8 @@ class EntityMemory(Memory):
metadata=item.metadata,
error=str(e),
source_type="entity_memory",
from_agent=self.agent,
from_task=self.task,
),
)
raise
@@ -111,6 +117,8 @@ class EntityMemory(Memory):
limit=limit,
score_threshold=score_threshold,
source_type="entity_memory",
from_agent=self.agent,
from_task=self.task,
),
)
@@ -129,6 +137,8 @@ class EntityMemory(Memory):
score_threshold=score_threshold,
query_time_ms=(time.time() - start_time) * 1000,
source_type="entity_memory",
from_agent=self.agent,
from_task=self.task,
),
)

22
src/crewai/memory/external/external_memory.py vendored
View File

@@ -53,7 +53,6 @@ class ExternalMemory(Memory):
self,
value: Any,
metadata: Optional[Dict[str, Any]] = None,
agent: Optional[str] = None,
) -> None:
"""Saves a value into the external storage."""
crewai_event_bus.emit(
@@ -61,24 +60,30 @@ class ExternalMemory(Memory):
event=MemorySaveStartedEvent(
value=value,
metadata=metadata,
agent_role=agent,
source_type="external_memory",
from_agent=self.agent,
from_task=self.task,
),
)
start_time = time.time()
try:
item = ExternalMemoryItem(value=value, metadata=metadata, agent=agent)
super().save(value=item.value, metadata=item.metadata, agent=item.agent)
item = ExternalMemoryItem(
value=value,
metadata=metadata,
agent=self.agent.role if self.agent else None,
)
super().save(value=item.value, metadata=item.metadata)
crewai_event_bus.emit(
self,
event=MemorySaveCompletedEvent(
value=value,
metadata=metadata,
agent_role=agent,
save_time_ms=(time.time() - start_time) * 1000,
source_type="external_memory",
from_agent=self.agent,
from_task=self.task,
),
)
except Exception as e:
@@ -87,9 +92,10 @@ class ExternalMemory(Memory):
event=MemorySaveFailedEvent(
value=value,
metadata=metadata,
agent_role=agent,
error=str(e),
source_type="external_memory",
from_agent=self.agent,
from_task=self.task,
),
)
raise
@@ -107,6 +113,8 @@ class ExternalMemory(Memory):
limit=limit,
score_threshold=score_threshold,
source_type="external_memory",
from_agent=self.agent,
from_task=self.task,
),
)
@@ -125,6 +133,8 @@ class ExternalMemory(Memory):
score_threshold=score_threshold,
query_time_ms=(time.time() - start_time) * 1000,
source_type="external_memory",
from_agent=self.agent,
from_task=self.task,
),
)

18
src/crewai/memory/long_term/long_term_memory.py
View File

@@ -37,13 +37,17 @@ class LongTermMemory(Memory):
metadata=item.metadata,
agent_role=item.agent,
source_type="long_term_memory",
from_agent=self.agent,
from_task=self.task,
),
)
start_time = time.time()
try:
metadata = item.metadata
metadata.update({"agent": item.agent, "expected_output": item.expected_output})
metadata.update(
{"agent": item.agent, "expected_output": item.expected_output}
)
self.storage.save( # type: ignore # BUG?: Unexpected keyword argument "task_description","score","datetime" for "save" of "Storage"
task_description=item.task,
score=metadata["quality"],
@@ -59,6 +63,8 @@ class LongTermMemory(Memory):
agent_role=item.agent,
save_time_ms=(time.time() - start_time) * 1000,
source_type="long_term_memory",
from_agent=self.agent,
from_task=self.task,
),
)
except Exception as e:
@@ -74,13 +80,19 @@ class LongTermMemory(Memory):
)
raise
def search(self, task: str, latest_n: int = 3) -> List[Dict[str, Any]]: # type: ignore # signature of "search" incompatible with supertype "Memory"
def search( # type: ignore # signature of "search" incompatible with supertype "Memory"
self,
task: str,
latest_n: int = 3,
) -> List[Dict[str, Any]]: # type: ignore # signature of "search" incompatible with supertype "Memory"
crewai_event_bus.emit(
self,
event=MemoryQueryStartedEvent(
query=task,
limit=latest_n,
source_type="long_term_memory",
from_agent=self.agent,
from_task=self.task,
),
)
@@ -96,6 +108,8 @@ class LongTermMemory(Memory):
limit=latest_n,
query_time_ms=(time.time() - start_time) * 1000,
source_type="long_term_memory",
from_agent=self.agent,
from_task=self.task,
),
)

31
src/crewai/memory/memory.py
View File

@@ -1,7 +1,11 @@
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List, Optional, TYPE_CHECKING
from pydantic import BaseModel
if TYPE_CHECKING:
from crewai.agent import Agent
from crewai.task import Task
class Memory(BaseModel):
"""
@@ -12,19 +16,38 @@ class Memory(BaseModel):
crew: Optional[Any] = None
storage: Any
_agent: Optional["Agent"] = None
_task: Optional["Task"] = None
def __init__(self, storage: Any, **data: Any):
super().__init__(storage=storage, **data)
@property
def task(self) -> Optional["Task"]:
"""Get the current task associated with this memory."""
return self._task
@task.setter
def task(self, task: Optional["Task"]) -> None:
"""Set the current task associated with this memory."""
self._task = task
@property
def agent(self) -> Optional["Agent"]:
"""Get the current agent associated with this memory."""
return self._agent
@agent.setter
def agent(self, agent: Optional["Agent"]) -> None:
"""Set the current agent associated with this memory."""
self._agent = agent
def save(
self,
value: Any,
metadata: Optional[Dict[str, Any]] = None,
agent: Optional[str] = None,
) -> None:
metadata = metadata or {}
if agent:
metadata["agent"] = agent
self.storage.save(value, metadata)

29
src/crewai/memory/short_term/short_term_memory.py
View File

@@ -37,7 +37,7 @@ class ShortTermMemory(Memory):
raise ImportError(
"Mem0 is not installed. Please install it with `pip install mem0ai`."
)
config = embedder_config.get("config")
config = embedder_config.get("config") if embedder_config else None
storage = Mem0Storage(type="short_term", crew=crew, config=config)
else:
storage = (
@@ -57,34 +57,42 @@ class ShortTermMemory(Memory):
self,
value: Any,
metadata: Optional[Dict[str, Any]] = None,
agent: Optional[str] = None,
) -> None:
crewai_event_bus.emit(
self,
event=MemorySaveStartedEvent(
value=value,
metadata=metadata,
agent_role=agent,
source_type="short_term_memory",
from_agent=self.agent,
from_task=self.task,
),
)
start_time = time.time()
try:
item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
item = ShortTermMemoryItem(
data=value,
metadata=metadata,
agent=self.agent.role if self.agent else None,
)
if self._memory_provider == "mem0":
item.data = f"Remember the following insights from Agent run: {item.data}"
item.data = (
f"Remember the following insights from Agent run: {item.data}"
)
super().save(value=item.data, metadata=item.metadata, agent=item.agent)
super().save(value=item.data, metadata=item.metadata)
crewai_event_bus.emit(
self,
event=MemorySaveCompletedEvent(
value=value,
metadata=metadata,
agent_role=agent,
# agent_role=agent,
save_time_ms=(time.time() - start_time) * 1000,
source_type="short_term_memory",
from_agent=self.agent,
from_task=self.task,
),
)
except Exception as e:
@@ -93,9 +101,10 @@ class ShortTermMemory(Memory):
event=MemorySaveFailedEvent(
value=value,
metadata=metadata,
agent_role=agent,
error=str(e),
source_type="short_term_memory",
from_agent=self.agent,
from_task=self.task,
),
)
raise
@@ -113,6 +122,8 @@ class ShortTermMemory(Memory):
limit=limit,
score_threshold=score_threshold,
source_type="short_term_memory",
from_agent=self.agent,
from_task=self.task,
),
)
@@ -131,6 +142,8 @@ class ShortTermMemory(Memory):
score_threshold=score_threshold,
query_time_ms=(time.time() - start_time) * 1000,
source_type="short_term_memory",
from_agent=self.agent,
from_task=self.task,
),
)

10
src/crewai/task.py
View File

@@ -433,7 +433,7 @@ class Task(BaseModel):
pydantic_output, json_output = self._export_output(result)
task_output = TaskOutput(
name=self.name,
name=self.name or self.description,
description=self.description,
expected_output=self.expected_output,
raw=result,
@@ -561,8 +561,8 @@ class Task(BaseModel):
should_inject = self.allow_crewai_trigger_context
if should_inject and self.agent:
crew = getattr(self.agent, 'crew', None)
if crew and hasattr(crew, '_inputs') and crew._inputs:
crew = getattr(self.agent, "crew", None)
if crew and hasattr(crew, "_inputs") and crew._inputs:
trigger_payload = crew._inputs.get("crewai_trigger_payload")
if trigger_payload is not None:
description += f"\n\nTrigger Payload: {trigger_payload}"
@@ -780,7 +780,9 @@ Follow these guidelines:
if self.create_directory and not directory.exists():
directory.mkdir(parents=True, exist_ok=True)
elif not self.create_directory and not directory.exists():
raise RuntimeError(f"Directory {directory} does not exist and create_directory is False")
raise RuntimeError(
f"Directory {directory} does not exist and create_directory is False"
)
with resolved_path.open("w", encoding="utf-8") as file:
if isinstance(result, dict):

47
src/crewai/tools/structured_tool.py
View File

@@ -16,6 +16,12 @@ if TYPE_CHECKING:
from crewai.tools.base_tool import BaseTool
class ToolUsageLimitExceeded(Exception):
"""Exception raised when a tool has reached its maximum usage limit."""
pass
class CrewStructuredTool:
"""A structured tool that can operate on any number of inputs.
@@ -227,17 +233,25 @@ class CrewStructuredTool:
"""
parsed_args = self._parse_args(input)
if self.has_reached_max_usage_count():
raise ToolUsageLimitExceeded(
f"Tool '{self.name}' has reached its maximum usage limit of {self.max_usage_count}. You should not use the {self.name} tool again."
)
self._increment_usage_count()
if inspect.iscoroutinefunction(self.func):
return await self.func(**parsed_args, **kwargs)
else:
# Run sync functions in a thread pool
import asyncio
try:
if inspect.iscoroutinefunction(self.func):
return await self.func(**parsed_args, **kwargs)
else:
# Run sync functions in a thread pool
import asyncio
return await asyncio.get_event_loop().run_in_executor(
None, lambda: self.func(**parsed_args, **kwargs)
)
return await asyncio.get_event_loop().run_in_executor(
None, lambda: self.func(**parsed_args, **kwargs)
)
except Exception:
raise
def _run(self, *args, **kwargs) -> Any:
"""Legacy method for compatibility."""
@@ -252,12 +266,22 @@ class CrewStructuredTool:
"""Main method for tool execution."""
parsed_args = self._parse_args(input)
if self.has_reached_max_usage_count():
raise ToolUsageLimitExceeded(
f"Tool '{self.name}' has reached its maximum usage limit of {self.max_usage_count}. You should not use the {self.name} tool again."
)
self._increment_usage_count()
if inspect.iscoroutinefunction(self.func):
result = asyncio.run(self.func(**parsed_args, **kwargs))
return result
try:
result = self.func(**parsed_args, **kwargs)
except Exception:
raise
result = self.func(**parsed_args, **kwargs)
if asyncio.iscoroutine(result):
@@ -265,6 +289,13 @@ class CrewStructuredTool:
return result
def has_reached_max_usage_count(self) -> bool:
"""Check if the tool has reached its maximum usage count."""
return (
self.max_usage_count is not None
and self.current_usage_count >= self.max_usage_count
)
def _increment_usage_count(self) -> None:
"""Increment the usage count."""
self.current_usage_count += 1

20
src/crewai/tools/tool_usage.py
View File

@@ -178,8 +178,10 @@ class ToolUsage:
if self.agent.fingerprint:
event_data.update(self.agent.fingerprint)
crewai_event_bus.emit(self,ToolUsageStartedEvent(**event_data))
if self.task:
event_data["task_name"] = self.task.name or self.task.description
event_data["task_id"] = str(self.task.id)
crewai_event_bus.emit(self, ToolUsageStartedEvent(**event_data))
started_at = time.time()
from_cache = False
@@ -311,12 +313,15 @@ class ToolUsage:
if self.agent and hasattr(self.agent, "tools_results"):
self.agent.tools_results.append(data)
if available_tool and hasattr(available_tool, 'current_usage_count'):
if available_tool and hasattr(available_tool, "current_usage_count"):
available_tool.current_usage_count += 1
if hasattr(available_tool, 'max_usage_count') and available_tool.max_usage_count is not None:
if (
hasattr(available_tool, "max_usage_count")
and available_tool.max_usage_count is not None
):
self._printer.print(
content=f"Tool '{available_tool.name}' usage: {available_tool.current_usage_count}/{available_tool.max_usage_count}",
color="blue"
color="blue",
)
return result
@@ -362,7 +367,7 @@ class ToolUsage:
Error message if limit reached, None otherwise
"""
if (
hasattr(tool, 'max_usage_count')
hasattr(tool, "max_usage_count")
and tool.max_usage_count is not None
and tool.current_usage_count >= tool.max_usage_count
):
@@ -605,6 +610,9 @@ class ToolUsage:
"output": result,
}
)
if self.task:
event_data["task_id"] = str(self.task.id)
event_data["task_name"] = self.task.name or self.task.description
crewai_event_bus.emit(self, ToolUsageFinishedEvent(**event_data))
def _prepare_event_data(

21
src/crewai/utilities/events/base_events.py
View File

@@ -11,7 +11,9 @@ class BaseEvent(BaseModel):
timestamp: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
type: str
source_fingerprint: Optional[str] = None # UUID string of the source entity
source_type: Optional[str] = None # "agent", "task", "crew", "memory", "entity_memory", "short_term_memory", "long_term_memory", "external_memory"
source_type: Optional[str] = (
None # "agent", "task", "crew", "memory", "entity_memory", "short_term_memory", "long_term_memory", "external_memory"
)
fingerprint_metadata: Optional[Dict[str, Any]] = None # Any relevant metadata
def to_json(self, exclude: set[str] | None = None):
@@ -25,3 +27,20 @@ class BaseEvent(BaseModel):
dict: A JSON-serializable dictionary.
"""
return to_serializable(self, exclude=exclude)
def _set_task_params(self, data: Dict[str, Any]):
if "from_task" in data and (task := data["from_task"]):
self.task_id = task.id
self.task_name = task.name or task.description
self.from_task = None
def _set_agent_params(self, data: Dict[str, Any]):
task = data.get("from_task", None)
agent = task.agent if task else data.get("from_agent", None)
if not agent:
return
self.agent_id = agent.id
self.agent_role = agent.role
self.from_agent = None

17
src/crewai/utilities/events/listeners/tracing/trace_batch_manager.py
View File

@@ -41,6 +41,12 @@ class TraceBatchManager:
"""Single responsibility: Manage batches and event buffering"""
is_current_batch_ephemeral: bool = False
trace_batch_id: Optional[str] = None
current_batch: Optional[TraceBatch] = None
event_buffer: List[TraceEvent] = []
execution_start_times: Dict[str, datetime] = {}
batch_owner_type: Optional[str] = None
batch_owner_id: Optional[str] = None
def __init__(self):
try:
@@ -48,11 +54,6 @@ class TraceBatchManager:
except AuthError:
self.plus_api = PlusAPI(api_key="")
self.trace_batch_id: Optional[str] = None # Backend ID
self.current_batch: Optional[TraceBatch] = None
self.event_buffer: List[TraceEvent] = []
self.execution_start_times: Dict[str, datetime] = {}
def initialize_batch(
self,
user_context: Dict[str, str],
@@ -178,14 +179,16 @@ class TraceBatchManager:
if not self.current_batch:
return None
self.current_batch.events = self.event_buffer.copy()
if self.event_buffer:
self._send_events_to_backend()
self._finalize_backend_batch()
self.current_batch.events = self.event_buffer.copy()
finalized_batch = self.current_batch
self.batch_owner_type = None
self.batch_owner_id = None
self.current_batch = None
self.event_buffer.clear()
self.trace_batch_id = None

67
src/crewai/utilities/events/listeners/tracing/trace_listener.py
View File

@@ -75,10 +75,18 @@ class TraceCollectionListener(BaseEventListener):
Trace collection listener that orchestrates trace collection
"""
complex_events = ["task_started", "llm_call_started", "llm_call_completed"]
complex_events = [
"task_started",
"task_completed",
"llm_call_started",
"llm_call_completed",
"agent_execution_started",
"agent_execution_completed",
]
_instance = None
_initialized = False
_listeners_setup = False
def __new__(cls, batch_manager=None):
if cls._instance is None:
@@ -116,10 +124,15 @@ class TraceCollectionListener(BaseEventListener):
def setup_listeners(self, crewai_event_bus):
"""Setup event listeners - delegates to specific handlers"""
if self._listeners_setup:
return
self._register_flow_event_handlers(crewai_event_bus)
self._register_context_event_handlers(crewai_event_bus)
self._register_action_event_handlers(crewai_event_bus)
self._listeners_setup = True
def _register_flow_event_handlers(self, event_bus):
"""Register handlers for flow events"""
@@ -148,7 +161,8 @@ class TraceCollectionListener(BaseEventListener):
@event_bus.on(FlowFinishedEvent)
def on_flow_finished(source, event):
self._handle_trace_event("flow_finished", source, event)
self.batch_manager.finalize_batch()
if self.batch_manager.batch_owner_type == "flow":
self.batch_manager.finalize_batch()
@event_bus.on(FlowPlotEvent)
def on_flow_plot(source, event):
@@ -166,7 +180,8 @@ class TraceCollectionListener(BaseEventListener):
@event_bus.on(CrewKickoffCompletedEvent)
def on_crew_completed(source, event):
self._handle_trace_event("crew_kickoff_completed", source, event)
self.batch_manager.finalize_batch()
if self.batch_manager.batch_owner_type == "crew":
self.batch_manager.finalize_batch()
@event_bus.on(CrewKickoffFailedEvent)
def on_crew_failed(source, event):
@@ -218,7 +233,7 @@ class TraceCollectionListener(BaseEventListener):
self._handle_trace_event("llm_guardrail_completed", source, event)
def _register_action_event_handlers(self, event_bus):
"""Register handlers for action events (LLM calls, tool usage, memory)"""
"""Register handlers for action events (LLM calls, tool usage)"""
@event_bus.on(LLMCallStartedEvent)
def on_llm_call_started(source, event):
@@ -289,6 +304,9 @@ class TraceCollectionListener(BaseEventListener):
"crewai_version": get_crewai_version(),
}
self.batch_manager.batch_owner_type = "crew"
self.batch_manager.batch_owner_id = getattr(source, "id", str(uuid.uuid4()))
self._initialize_batch(user_context, execution_metadata)
def _initialize_flow_batch(self, source: Any, event: Any):
@@ -301,6 +319,9 @@ class TraceCollectionListener(BaseEventListener):
"execution_type": "flow",
}
self.batch_manager.batch_owner_type = "flow"
self.batch_manager.batch_owner_id = getattr(source, "id", str(uuid.uuid4()))
self._initialize_batch(user_context, execution_metadata)
def _initialize_batch(
@@ -358,12 +379,44 @@ class TraceCollectionListener(BaseEventListener):
return {
"task_description": event.task.description,
"expected_output": event.task.expected_output,
"task_name": event.task.name,
"task_name": event.task.name or event.task.description,
"context": event.context,
"agent": source.agent.role,
"agent_role": source.agent.role,
"task_id": str(event.task.id),
}
elif event_type == "task_completed":
return {
"task_description": event.task.description if event.task else None,
"task_name": event.task.name or event.task.description
if event.task
else None,
"task_id": str(event.task.id) if event.task else None,
"output_raw": event.output.raw if event.output else None,
"output_format": str(event.output.output_format)
if event.output
else None,
"agent_role": event.output.agent if event.output else None,
}
elif event_type == "agent_execution_started":
return {
"agent_role": event.agent.role,
"agent_goal": event.agent.goal,
"agent_backstory": event.agent.backstory,
}
elif event_type == "agent_execution_completed":
return {
"agent_role": event.agent.role,
"agent_goal": event.agent.goal,
"agent_backstory": event.agent.backstory,
}
elif event_type == "llm_call_started":
return self._safe_serialize_to_dict(event)
event_data = self._safe_serialize_to_dict(event)
event_data["task_name"] = (
event.task_name or event.task_description
if hasattr(event, "task_name") and event.task_name
else None
)
return event_data
elif event_type == "llm_call_completed":
return self._safe_serialize_to_dict(event)
else:

18
src/crewai/utilities/events/llm_events.py
View File

@@ -13,26 +13,14 @@ class LLMEventBase(BaseEvent):
agent_id: Optional[str] = None
agent_role: Optional[str] = None
from_task: Optional[Any] = None
from_agent: Optional[Any] = None
def __init__(self, **data):
super().__init__(**data)
self._set_agent_params(data)
self._set_task_params(data)
def _set_agent_params(self, data: Dict[str, Any]):
task = data.get("from_task", None)
agent = task.agent if task else data.get("from_agent", None)
if not agent:
return
self.agent_id = agent.id
self.agent_role = agent.role
def _set_task_params(self, data: Dict[str, Any]):
if "from_task" in data and (task := data["from_task"]):
self.task_id = task.id
self.task_name = task.name
class LLMCallType(Enum):
"""Type of LLM call being made"""

33
src/crewai/utilities/events/memory_events.py
View File

@@ -3,7 +3,24 @@ from typing import Any, Dict, Optional
from crewai.utilities.events.base_events import BaseEvent
class MemoryQueryStartedEvent(BaseEvent):
class MemoryBaseEvent(BaseEvent):
"""Base event for memory operations"""
type: str
task_id: Optional[str] = None
task_name: Optional[str] = None
from_task: Optional[Any] = None
from_agent: Optional[Any] = None
agent_role: Optional[str] = None
agent_id: Optional[str] = None
def __init__(self, **data):
super().__init__(**data)
self._set_agent_params(data)
self._set_task_params(data)
class MemoryQueryStartedEvent(MemoryBaseEvent):
"""Event emitted when a memory query is started"""
type: str = "memory_query_started"
@@ -12,7 +29,7 @@ class MemoryQueryStartedEvent(BaseEvent):
score_threshold: Optional[float] = None
class MemoryQueryCompletedEvent(BaseEvent):
class MemoryQueryCompletedEvent(MemoryBaseEvent):
"""Event emitted when a memory query is completed successfully"""
type: str = "memory_query_completed"
@@ -23,7 +40,7 @@ class MemoryQueryCompletedEvent(BaseEvent):
query_time_ms: float
class MemoryQueryFailedEvent(BaseEvent):
class MemoryQueryFailedEvent(MemoryBaseEvent):
"""Event emitted when a memory query fails"""
type: str = "memory_query_failed"
@@ -33,7 +50,7 @@ class MemoryQueryFailedEvent(BaseEvent):
error: str
class MemorySaveStartedEvent(BaseEvent):
class MemorySaveStartedEvent(MemoryBaseEvent):
"""Event emitted when a memory save operation is started"""
type: str = "memory_save_started"
@@ -42,7 +59,7 @@ class MemorySaveStartedEvent(BaseEvent):
agent_role: Optional[str] = None
class MemorySaveCompletedEvent(BaseEvent):
class MemorySaveCompletedEvent(MemoryBaseEvent):
"""Event emitted when a memory save operation is completed successfully"""
type: str = "memory_save_completed"
@@ -52,7 +69,7 @@ class MemorySaveCompletedEvent(BaseEvent):
save_time_ms: float
class MemorySaveFailedEvent(BaseEvent):
class MemorySaveFailedEvent(MemoryBaseEvent):
"""Event emitted when a memory save operation fails"""
type: str = "memory_save_failed"
@@ -62,14 +79,14 @@ class MemorySaveFailedEvent(BaseEvent):
error: str
class MemoryRetrievalStartedEvent(BaseEvent):
class MemoryRetrievalStartedEvent(MemoryBaseEvent):
"""Event emitted when memory retrieval for a task prompt starts"""
type: str = "memory_retrieval_started"
task_id: Optional[str] = None
class MemoryRetrievalCompletedEvent(BaseEvent):
class MemoryRetrievalCompletedEvent(MemoryBaseEvent):
"""Event emitted when memory retrieval for a task prompt completes successfully"""
type: str = "memory_retrieval_completed"

28
src/crewai/utilities/events/reasoning_events.py
View File

@@ -1,16 +1,34 @@
from crewai.utilities.events.base_events import BaseEvent
from typing import Any, Optional
class AgentReasoningStartedEvent(BaseEvent):
class ReasoningEvent(BaseEvent):
"""Base event for reasoning events."""
type: str
attempt: int = 1
agent_role: str
task_id: str
task_name: Optional[str] = None
from_task: Optional[Any] = None
agent_id: Optional[str] = None
from_agent: Optional[Any] = None
def __init__(self, **data):
super().__init__(**data)
self._set_task_params(data)
self._set_agent_params(data)
class AgentReasoningStartedEvent(ReasoningEvent):
"""Event emitted when an agent starts reasoning about a task."""
type: str = "agent_reasoning_started"
agent_role: str
task_id: str
attempt: int = 1 # The current reasoning/refinement attempt
class AgentReasoningCompletedEvent(BaseEvent):
class AgentReasoningCompletedEvent(ReasoningEvent):
"""Event emitted when an agent finishes its reasoning process."""
type: str = "agent_reasoning_completed"
@@ -18,14 +36,12 @@ class AgentReasoningCompletedEvent(BaseEvent):
task_id: str
plan: str
ready: bool
attempt: int = 1
class AgentReasoningFailedEvent(BaseEvent):
class AgentReasoningFailedEvent(ReasoningEvent):
"""Event emitted when the reasoning process fails."""
type: str = "agent_reasoning_failed"
agent_role: str
task_id: str
error: str
attempt: int = 1

7
src/crewai/utilities/events/tool_usage_events.py
View File

@@ -9,17 +9,24 @@ class ToolUsageEvent(BaseEvent):
agent_key: Optional[str] = None
agent_role: Optional[str] = None
agent_id: Optional[str] = None
tool_name: str
tool_args: Dict[str, Any] | str
tool_class: Optional[str] = None
run_attempts: int | None = None
delegations: int | None = None
agent: Optional[Any] = None
task_name: Optional[str] = None
task_id: Optional[str] = None
from_task: Optional[Any] = None
from_agent: Optional[Any] = None
model_config = {"arbitrary_types_allowed": True}
def __init__(self, **data):
super().__init__(**data)
self._set_agent_params(data)
self._set_task_params(data)
# Set fingerprint data from the agent
if self.agent and hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
self.source_fingerprint = self.agent.fingerprint.uuid_str

75
src/crewai/utilities/reasoning_handler.py
View File

@@ -18,17 +18,20 @@ from crewai.utilities.events.reasoning_events import (
class ReasoningPlan(BaseModel):
"""Model representing a reasoning plan for a task."""
plan: str = Field(description="The detailed reasoning plan for the task.")
ready: bool = Field(description="Whether the agent is ready to execute the task.")
class AgentReasoningOutput(BaseModel):
"""Model representing the output of the agent reasoning process."""
plan: ReasoningPlan = Field(description="The reasoning plan for the task.")
class ReasoningFunction(BaseModel):
"""Model for function calling with reasoning."""
plan: str = Field(description="The detailed reasoning plan for the task.")
ready: bool = Field(description="Whether the agent is ready to execute the task.")
@@ -38,6 +41,7 @@ class AgentReasoning:
Handles the agent reasoning process, enabling an agent to reflect and create a plan
before executing a task.
"""
def __init__(self, task: Task, agent: Agent):
if not task or not agent:
raise ValueError("Both task and agent must be provided.")
@@ -63,6 +67,7 @@ class AgentReasoning:
agent_role=self.agent.role,
task_id=str(self.task.id),
attempt=1,
from_task=self.task,
),
)
except Exception:
@@ -82,6 +87,7 @@ class AgentReasoning:
plan=output.plan.plan,
ready=output.plan.ready,
attempt=1,
from_task=self.task,
),
)
except Exception:
@@ -98,6 +104,7 @@ class AgentReasoning:
task_id=str(self.task.id),
error=str(e),
attempt=1,
from_task=self.task,
),
)
except Exception:
@@ -135,14 +142,16 @@ class AgentReasoning:
system_prompt = self.i18n.retrieve("reasoning", "initial_plan").format(
role=self.agent.role,
goal=self.agent.goal,
backstory=self.__get_agent_backstory()
backstory=self.__get_agent_backstory(),
)
response = self.llm.call(
[
{"role": "system", "content": system_prompt},
{"role": "user", "content": reasoning_prompt}
]
{"role": "user", "content": reasoning_prompt},
],
from_task=self.task,
from_agent=self.agent,
)
return self.__parse_reasoning_response(str(response))
@@ -170,6 +179,7 @@ class AgentReasoning:
agent_role=self.agent.role,
task_id=str(self.task.id),
attempt=attempt + 1,
from_task=self.task,
),
)
except Exception:
@@ -183,14 +193,16 @@ class AgentReasoning:
system_prompt = self.i18n.retrieve("reasoning", "refine_plan").format(
role=self.agent.role,
goal=self.agent.goal,
backstory=self.__get_agent_backstory()
backstory=self.__get_agent_backstory(),
)
response = self.llm.call(
[
{"role": "system", "content": system_prompt},
{"role": "user", "content": refine_prompt}
]
{"role": "user", "content": refine_prompt},
],
from_task=self.task,
from_agent=self.agent,
)
plan, ready = self.__parse_reasoning_response(str(response))
@@ -227,23 +239,23 @@ class AgentReasoning:
"properties": {
"plan": {
"type": "string",
"description": "The detailed reasoning plan for the task."
"description": "The detailed reasoning plan for the task.",
},
"ready": {
"type": "boolean",
"description": "Whether the agent is ready to execute the task."
}
"description": "Whether the agent is ready to execute the task.",
},
},
"required": ["plan", "ready"]
}
}
"required": ["plan", "ready"],
},
},
}
try:
system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
role=self.agent.role,
goal=self.agent.goal,
backstory=self.__get_agent_backstory()
backstory=self.__get_agent_backstory(),
)
# Prepare a simple callable that just returns the tool arguments as JSON
@@ -254,10 +266,12 @@ class AgentReasoning:
response = self.llm.call(
[
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt}
{"role": "user", "content": prompt},
],
tools=[function_schema],
available_functions={"create_reasoning_plan": _create_reasoning_plan},
from_task=self.task,
from_agent=self.agent,
)
self.logger.debug(f"Function calling response: {response[:100]}...")
@@ -270,30 +284,43 @@ class AgentReasoning:
pass
response_str = str(response)
return response_str, "READY: I am ready to execute the task." in response_str
return (
response_str,
"READY: I am ready to execute the task." in response_str,
)
except Exception as e:
self.logger.warning(f"Error during function calling: {str(e)}. Falling back to text parsing.")
self.logger.warning(
f"Error during function calling: {str(e)}. Falling back to text parsing."
)
try:
system_prompt = self.i18n.retrieve("reasoning", prompt_type).format(
role=self.agent.role,
goal=self.agent.goal,
backstory=self.__get_agent_backstory()
backstory=self.__get_agent_backstory(),
)
fallback_response = self.llm.call(
[
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt}
]
{"role": "user", "content": prompt},
],
from_task=self.task,
from_agent=self.agent,
)
fallback_str = str(fallback_response)
return fallback_str, "READY: I am ready to execute the task." in fallback_str
return (
fallback_str,
"READY: I am ready to execute the task." in fallback_str,
)
except Exception as inner_e:
self.logger.error(f"Error during fallback text parsing: {str(inner_e)}")
return "Failed to generate a plan due to an error.", True # Default to ready to avoid getting stuck
return (
"Failed to generate a plan due to an error.",
True,
) # Default to ready to avoid getting stuck
def __get_agent_backstory(self) -> str:
"""
@@ -319,7 +346,7 @@ class AgentReasoning:
backstory=self.__get_agent_backstory(),
description=self.task.description,
expected_output=self.task.expected_output,
tools=available_tools
tools=available_tools,
)
def __format_available_tools(self) -> str:
@@ -330,7 +357,7 @@ class AgentReasoning:
str: Comma-separated list of tool names.
"""
try:
return ', '.join([tool.name for tool in (self.task.tools or [])])
return ", ".join([tool.name for tool in (self.task.tools or [])])
except (AttributeError, TypeError):
return "No tools available"
@@ -348,7 +375,7 @@ class AgentReasoning:
role=self.agent.role,
goal=self.agent.goal,
backstory=self.__get_agent_backstory(),
current_plan=current_plan
current_plan=current_plan,
)
def __parse_reasoning_response(self, response: str) -> Tuple[str, bool]:

158
tests/cassettes/test_agent_usage_metrics_are_captured_for_hierarchical_process.yaml
View File

@@ -508,4 +508,162 @@ interactions:
- req_80eed127ea0361c637657470cf9b647e
http_version: HTTP/1.1
status_code: 200
- request:
body: '{"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
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an article.\", \"context\": \"We need a list of 5 interesting ideas that would
make compelling articles. The topics should be relevant, timely, and engaging
for a broad audience.\", \"coworker\": \"Researcher\"}\nObservation: 1. **The
Rise of AI in Healthcare: Transforming Patient Care and Diagnostics**\n \"Imagine
a world where routine check-ups and complex diagnostics are seamlessly conducted
by intelligent systems. AI in healthcare is rapidly transforming this vision
into reality, enhancing patient care through early and accurate disease detection,
personalized treatment plans, and continuous health monitoring. This revolution
is not just a futuristic concept; it''s already happening with AI-driven technologies
enabling doctors to make more informed decisions and offering patients a more
proactive approach to their health. The ethical and privacy concerns, however,
pose new challenges that the industry must navigate to ensure responsible advancement.\"\n\n2.
**AI Agents in the Workplace: Boosting Productivity and Shaping the Future of
Jobs**\n \"The modern workplace is being redefined by the integration of AI
agents, intelligent systems designed to handle repetitive tasks, analyze vast
amounts of data, and assist in complex decision-making processes. These innovations
are boosting productivity, allowing employees to focus on creativity and strategic
thinking. The rise of AI in the workspace heralds a future where human and machine
collaboration leads to unparalleled efficiencies and novel job opportunities.
However, this shift also brings with it challenges related to job displacement
and the urgent need for reskilling the workforce to thrive in an AI-augmented
environment.\"\n\n3. **Ethical AI: Ensuring Fairness, Accountability, and Transparency**\n \"As
AI technology becomes more pervasive, the quest for ethical AI has become critical.
Ensuring fairness, accountability, and transparency in AI systems is essential
to prevent biases and promote trust. This involves creating robust frameworks
and guidelines that govern AI development and deployment. Real-world case studies
highlight both the triumphs and the tribulations of tech companies as they navigate
the complex moral landscape that AI presents. As society grapples with these
challenges, the push for ethical AI continues to drive innovation and policy-making
in the tech industry.\"\n\n4. **AI-Powered Startups: Innovators Changing the
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are emerging as the vanguards of innovation, disrupting traditional business
models and reshaping entire industries. From revolutionizing financial services
to pioneering advances in healthcare and beyond, these startups are at the forefront
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complex problems and bringing novel solutions to market at an unprecedented
pace. Investors are taking notice, pouring capital into these ventures, and
fueling a new wave of growth and competition in the AI ecosystem.\"\n\n5. **The
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industry is undergoing a seismic shift driven by AI technology. In movies, AI
is revolutionizing special effects, enabling filmmakers to create stunning,
hyper-realistic worlds. In video games, AI is enhancing player experiences by
creating adaptive, intelligent characters and dynamic storylines that respond
to player choices. This transformative power of AI promises to push the boundaries
of creativity, offering audiences more immersive and interactive entertainment
experiences. As the technology continues to evolve, the future of entertainment
looks set to be more exciting and unpredictable than ever before.\""}, {"role":
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amazing paragraph highlight for each of the identified ideas to the Senior Writer,
ensuring each topic is adequately detailed to showcase its potential.\n\nAction:
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highlight for each of these 5 ideas that showcases how good an article about
these topics could be.\", \"context\": \"Here are the 5 ideas:\\n1. The Rise
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AI is revolutionizing healthcare through enhanced diagnostic tools, personalized
treatment plans, and post-operative care. Discuss current achievements, potential
future applications, and ethical considerations.\\n2. AI Agents in the Workplace:
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improving efficiency, automating mundane tasks, and assisting in decision-making
processes. Address potential job displacement and the need for upskilling the
workforce.\\n3. Ethical AI: Ensuring Fairness, Accountability, and Transparency
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models, and the unique challenges they face. Discuss the role of venture capital
in fostering AI innovation.\\n5. The Future of AI in Entertainment: From Movies
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from scriptwriting and special effects in movies to creating more immersive
and intelligent experiences in video games. Offer predictions about future trends
and possible impacts on content creation and consumption.\", \"coworker\": \"Senior
Writer\"}\nObservation:\nObservation: 1. **The Rise of AI in Healthcare: Transforming
Patient Care and Diagnostics**\n AI is undeniably revolutionizing the healthcare
sector, introducing transformative changes in patient care and diagnostics.
With the advent of advanced diagnostic tools powered by AI, healthcare professionals
can now detect and monitor diseases with unprecedented accuracy and speed. Personalized
treatment plans, which were once a distant dream, are becoming a reality as
AI algorithms analyze vast amounts of patient data, tailoring treatments that
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improvements, with AI-driven monitoring systems ensuring that patients receive
timely and effective care. Current achievements like IBM Watson''s oncology
platform or Google''s DeepMind in eye care are just the beginning, with future
applications promising to further enhance surgical robotics, predictive analytics,
and remote patient monitoring. However, alongside these advancements, ethical
considerations such as patient privacy, data security, and decision-making transparency
must be rigorously addressed to fully harness AI\u2019s potential in healthcare.\n\n2.
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Jobs**\n The integration of AI agents in the workplace marks a pivotal shift
in how businesses operate and compete. These intelligent agents are revolutionizing
various industries by automating mundane tasks, optimizing workflows, and providing
actionable insights from data analysis. For instance, AI chatbots in customer
service can handle routine inquiries, freeing human agents to tackle more complex
issues. In manufacturing, AI systems predict equipment failures, reducing downtime
and maintaining peak productivity. By enhancing operational efficiency, AI agents
not only boost productivity but also reshape the future of jobs. While the risk
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for upskilling and creating new roles that harness human creativity and strategic
thinking. Preparing the workforce for these changes is crucial, fostering a
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and even exacerbate inequality. Case studies, such as the controversy surrounding
facial recognition systems and their racial biases, highlight the pressing need
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with accountability measures for their outcomes, establishes trust and integrity
in AI applications. Legislative frameworks from the EU\u2019s GDPR to the IEEE\u2019s
global initiatives for ethical AI provide the scaffolding for these principles.
Companies are now faced with the challenge of integrating these ethical considerations
into their development processes, ensuring that AI advancements benefit all
of society equitably.\n\n4. **AI-Powered Startups: Innovators Changing the Tech
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renaissance, disrupting traditional business models and reshaping industries.
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learning models analyze vast datasets to create compelling narratives and optimize
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learning algorithms personalizing gaming experiences. Future trends indicate
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blur. These advancements are not only transforming content creation but also
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This article traces the portrayal of AI agents in movies, books, and video games,
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providing a fascinating journey through the evolution of AI in popular culture.\n\n5.
**The Future of AI in Personalization: Creating Tailored Experiences in Everyday
Life**\n - Imagine a world where every experience, from shopping to entertainment,
is uniquely tailored to your preferences by intelligent systems. This article
explores the cutting-edge of AI-driven personalization, discussing current technologies
and future potentials. Featuring insights from industry leaders and technologists,
readers will discover how AI is revolutionizing the way we live, offering bespoke
experiences that were once the realm of fantasy but are swiftly becoming a part
of our daily lives."}, {"role": "assistant", "content": "Thought: Now that I
have the list of 5 interesting article ideas from the Researcher, the next step
is to ask the Senior Writer to create a compelling paragraph for each idea.
This will ensure that each topic is highlighted effectively.\n\nAction: Delegate
work to coworker\nAction Input: {\"task\": \"Write one amazing paragraph highlight
for each of these 5 article ideas: \\n\\n1. **The Rise of AI in Healthcare:
Revolutionizing Diagnosis and Treatment**\\n- This article could explore how
AI is making strides in the healthcare industry. From AI-driven diagnostic tools
to personalized treatment plans, the piece could highlight real-world applications
and the potential future impact. Interviews with healthcare professionals and
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surrounding AI development and deployment. The article could examine issues
such as bias in AI algorithms, privacy concerns, and the ethical responsibility
of AI developers. Expert opinions from ethicists, technologists, and policymakers
can provide a well-rounded perspective.\\n\\n3. **AI Agents in the Workplace:
Enhancing Productivity or Eroding Jobs?**\\n- This piece can analyze the dual
impact of AI agents in the professional environment. While some argue that AI
can significantly boost productivity by automating mundane tasks, others fear
job displacement. The article can present both sides, backed by data and forecasts
about labor market trends.\\n\\n4. **From Science Fiction to Reality: The Evolution
of AI Agents in Popular Culture**\\n- An engaging piece that traces the depiction
of AI agents in movies, books, and video games, and compares these fictional
portrayals to the current state of AI technology. This can include interviews
with creators from the entertainment industry and AI experts to discuss how
close we are to realizing these sci-fi visions.\\n\\n5. **The Future of AI in
Personalization: Creating Tailored Experiences in Everyday Life**\\n- Discuss
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education, and personal finance. The focus can be on current technologies and
future possibilities, featuring insights from industry leaders and technologists
who are driving these innovations.\", \"context\": \"Create compelling paragraphs
that highlight the potential and intrigue of each article idea. These should
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compelling paragraphs for each article idea:\n\n1. **The Rise of AI in Healthcare:
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intelligence. AI is already transforming healthcare with advanced diagnostic
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dilemmas. This article will explore the complex moral landscape AI inhabits,
questioning the bias that may be programmed into algorithms, the privacy concerns
surrounding data usage, and the ethical responsibilities of those who develop
AI technologies. Insightful perspectives from ethicists, technologists, and
policymakers will highlight how society can navigate these challenges to ensure
that AI benefits humanity as a whole.\n\n3. **AI Agents in the Workplace: Enhancing
Productivity or Eroding Jobs?**\n - As AI agents become more integrated into
the workforce, the debate over their impact intensifies. This article will examine
how AI can boost productivity by automating routine tasks, thereby freeing up
human workers for more creative and strategic roles. However, it will also address
the fear of job displacement and present data and forecasts about the future
labor market. Balanced viewpoints from industry analysts and workers affected
by AI implementation will offer a comprehensive understanding of the pros and
cons of AI in the workplace.\n\n4. **From Science Fiction to Reality: The Evolution
of AI Agents in Popular Culture**\n - From HAL 9000 to Jarvis, AI agents have
long captivated our imaginations in books, movies, and video games. This article
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36
tests/memory/test_external_memory.py
View File

@@ -222,13 +222,12 @@ def test_external_memory_custom_storage(custom_storage, crew_with_external_memor
test_value = "test value"
test_metadata = {"source": "test"}
test_agent = "test_agent"
external_memory.save(value=test_value, metadata=test_metadata, agent=test_agent)
external_memory.save(value=test_value, metadata=test_metadata)
results = external_memory.search("test")
assert len(results) == 1
assert results[0]["value"] == test_value
assert results[0]["metadata"] == test_metadata | {"agent": test_agent}
assert results[0]["metadata"] == test_metadata
external_memory.reset()
results = external_memory.search("test")
@@ -259,7 +258,6 @@ def test_external_memory_search_events(
assert len(events["MemoryQueryStartedEvent"]) == 1
assert len(events["MemoryQueryCompletedEvent"]) == 1
assert len(events["MemoryQueryFailedEvent"]) == 0
assert dict(events["MemoryQueryStartedEvent"][0]) == {
"timestamp": ANY,
@@ -267,6 +265,12 @@ def test_external_memory_search_events(
"source_fingerprint": None,
"source_type": "external_memory",
"fingerprint_metadata": None,
"task_id": None,
"task_name": None,
"from_task": None,
"from_agent": None,
"agent_role": None,
"agent_id": None,
"query": "test value",
"limit": 3,
"score_threshold": 0.35,
@@ -278,6 +282,12 @@ def test_external_memory_search_events(
"source_fingerprint": None,
"source_type": "external_memory",
"fingerprint_metadata": None,
"task_id": None,
"task_name": None,
"from_task": None,
"from_agent": None,
"agent_role": None,
"agent_id": None,
"query": "test value",
"results": [],
"limit": 3,
@@ -306,12 +316,10 @@ def test_external_memory_save_events(
external_memory_with_mocked_config.save(
value="saving value",
metadata={"task": "test_task"},
agent="test_agent",
)
assert len(events["MemorySaveStartedEvent"]) == 1
assert len(events["MemorySaveCompletedEvent"]) == 1
assert len(events["MemorySaveFailedEvent"]) == 0
assert dict(events["MemorySaveStartedEvent"][0]) == {
"timestamp": ANY,
@@ -319,9 +327,14 @@ def test_external_memory_save_events(
"source_fingerprint": None,
"source_type": "external_memory",
"fingerprint_metadata": None,
"task_id": None,
"task_name": None,
"from_task": None,
"from_agent": None,
"agent_role": None,
"agent_id": None,
"value": "saving value",
"metadata": {"task": "test_task"},
"agent_role": "test_agent",
}
assert dict(events["MemorySaveCompletedEvent"][0]) == {
@@ -330,8 +343,13 @@ def test_external_memory_save_events(
"source_fingerprint": None,
"source_type": "external_memory",
"fingerprint_metadata": None,
"task_id": None,
"task_name": None,
"from_task": None,
"from_agent": None,
"agent_role": None,
"agent_id": None,
"value": "saving value",
"metadata": {"task": "test_task", "agent": "test_agent"},
"agent_role": "test_agent",
"metadata": {"task": "test_task"},
"save_time_ms": ANY,
}

26
tests/memory/test_long_term_memory.py
View File

@@ -51,9 +51,14 @@ def test_long_term_memory_save_events(long_term_memory):
"source_fingerprint": None,
"source_type": "long_term_memory",
"fingerprint_metadata": None,
"task_id": None,
"task_name": None,
"from_task": None,
"from_agent": None,
"agent_role": "test_agent",
"agent_id": None,
"value": "test_task",
"metadata": {"task": "test_task", "quality": 0.5},
"agent_role": "test_agent",
}
assert dict(events["MemorySaveCompletedEvent"][0]) == {
"timestamp": ANY,
@@ -61,6 +66,12 @@ def test_long_term_memory_save_events(long_term_memory):
"source_fingerprint": None,
"source_type": "long_term_memory",
"fingerprint_metadata": None,
"task_id": None,
"task_name": None,
"from_task": None,
"from_agent": None,
"agent_role": "test_agent",
"agent_id": None,
"value": "test_task",
"metadata": {
"task": "test_task",
@@ -68,7 +79,6 @@ def test_long_term_memory_save_events(long_term_memory):
"agent": "test_agent",
"expected_output": "test_output",
},
"agent_role": "test_agent",
"save_time_ms": ANY,
}
@@ -100,6 +110,12 @@ def test_long_term_memory_search_events(long_term_memory):
"source_fingerprint": None,
"source_type": "long_term_memory",
"fingerprint_metadata": None,
"task_id": None,
"task_name": None,
"from_task": None,
"from_agent": None,
"agent_role": None,
"agent_id": None,
"query": "test query",
"limit": 5,
"score_threshold": None,
@@ -111,6 +127,12 @@ def test_long_term_memory_search_events(long_term_memory):
"source_fingerprint": None,
"source_type": "long_term_memory",
"fingerprint_metadata": None,
"task_id": None,
"task_name": None,
"from_task": None,
"from_agent": None,
"agent_role": None,
"agent_id": None,
"query": "test query",
"results": None,
"limit": 5,

31
tests/memory/test_short_term_memory.py
View File

@@ -57,7 +57,6 @@ def test_short_term_memory_search_events(short_term_memory):
assert len(events["MemoryQueryStartedEvent"]) == 1
assert len(events["MemoryQueryCompletedEvent"]) == 1
assert len(events["MemoryQueryFailedEvent"]) == 0
assert dict(events["MemoryQueryStartedEvent"][0]) == {
"timestamp": ANY,
@@ -65,6 +64,12 @@ def test_short_term_memory_search_events(short_term_memory):
"source_fingerprint": None,
"source_type": "short_term_memory",
"fingerprint_metadata": None,
"task_id": None,
"task_name": None,
"from_task": None,
"from_agent": None,
"agent_role": None,
"agent_id": None,
"query": "test value",
"limit": 3,
"score_threshold": 0.35,
@@ -76,6 +81,12 @@ def test_short_term_memory_search_events(short_term_memory):
"source_fingerprint": None,
"source_type": "short_term_memory",
"fingerprint_metadata": None,
"task_id": None,
"task_name": None,
"from_task": None,
"from_agent": None,
"agent_role": None,
"agent_id": None,
"query": "test value",
"results": [],
"limit": 3,
@@ -99,12 +110,10 @@ def test_short_term_memory_save_events(short_term_memory):
short_term_memory.save(
value="test value",
metadata={"task": "test_task"},
agent="test_agent",
)
assert len(events["MemorySaveStartedEvent"]) == 1
assert len(events["MemorySaveCompletedEvent"]) == 1
assert len(events["MemorySaveFailedEvent"]) == 0
assert dict(events["MemorySaveStartedEvent"][0]) == {
"timestamp": ANY,
@@ -112,9 +121,14 @@ def test_short_term_memory_save_events(short_term_memory):
"source_fingerprint": None,
"source_type": "short_term_memory",
"fingerprint_metadata": None,
"task_id": None,
"task_name": None,
"from_task": None,
"from_agent": None,
"agent_role": None,
"agent_id": None,
"value": "test value",
"metadata": {"task": "test_task"},
"agent_role": "test_agent",
}
assert dict(events["MemorySaveCompletedEvent"][0]) == {
@@ -123,9 +137,14 @@ def test_short_term_memory_save_events(short_term_memory):
"source_fingerprint": None,
"source_type": "short_term_memory",
"fingerprint_metadata": None,
"task_id": None,
"task_name": None,
"from_task": None,
"from_agent": None,
"agent_role": None,
"agent_id": None,
"value": "test value",
"metadata": {"task": "test_task", "agent": "test_agent"},
"agent_role": "test_agent",
"metadata": {"task": "test_task"},
"save_time_ms": ANY,
}

179
tests/test_crew.py
View File

@@ -366,7 +366,7 @@ def test_hierarchical_process(researcher, writer):
assert (
result.raw
== "**1. The Rise of Autonomous AI Agents in Daily Life** \nAs artificial intelligence technology progresses, the integration of autonomous AI agents into everyday life becomes increasingly prominent. These agents, capable of making decisions without human intervention, are reshaping industries from healthcare to finance. Exploring case studies where autonomous AI has successfully decreased operational costs or improved efficiency can reveal not only the benefits but also the ethical implications of delegating decision-making to machines. This topic offers an exciting opportunity to dive into the AI landscape, showcasing current developments such as AI assistants and autonomous vehicles.\n\n**2. Ethical Implications of Generative AI in Creative Industries** \nThe surge of generative AI tools in creative fields, such as art, music, and writing, has sparked a heated debate about authorship and originality. This article could investigate how these tools are being used by artists and creators, examining both the potential for innovation and the risk of devaluing traditional art forms. Highlighting perspectives from creators, legal experts, and ethicists could provide a comprehensive overview of the challenges faced, including copyright concerns and the emotional impact on human artists. This discussion is vital as the creative landscape evolves alongside technological advancements, making it ripe for exploration.\n\n**3. AI in Climate Change Mitigation: Current Solutions and Future Potential** \nAs the world grapples with climate change, AI technology is increasingly being harnessed to develop innovative solutions for sustainability. From predictive analytics that optimize energy consumption to machine learning algorithms that improve carbon capture methods, AI's potential in environmental science is vast. This topic invites an exploration of existing AI applications in climate initiatives, with a focus on groundbreaking research and initiatives aimed at reducing humanity's carbon footprint. Highlighting successful projects and technology partnerships can illustrate the positive impact AI can have on global climate efforts, inspiring further exploration and investment in this area.\n\n**4. The Future of Work: How AI is Reshaping Employment Landscapes** \nThe discussions around AI's impact on the workforce are both urgent and complex, as advances in automation and machine learning continue to transform the job market. This article could delve into the current trends of AI-driven job displacement alongside opportunities for upskilling and the creation of new job roles. By examining case studies of companies that integrate AI effectively and the resulting workforce adaptations, readers can gain valuable insights into preparing for a future where humans and AI collaborate. This exploration highlights the importance of policies that promote workforce resilience in the face of change.\n\n**5. Decentralized AI: Exploring the Role of Blockchain in AI Development** \nAs blockchain technology sweeps through various sectors, its application in AI development presents a fascinating topic worth examining. Decentralized AI could address issues of data privacy, security, and democratization in AI models by allowing users to retain ownership of data while benefiting from AI's capabilities. This article could analyze how decentralized networks are disrupting traditional AI development models, featuring innovative projects that harness the synergy between blockchain and AI. Highlighting potential pitfalls and the future landscape of decentralized AI could stimulate discussion among technologists, entrepreneurs, and policymakers alike.\n\nThese topics not only reflect current trends but also probe deeper into ethical and practical considerations, making them timely and relevant for contemporary audiences."
== "1. **The Rise of Autonomous AI Agents in Daily Life** \n As artificial intelligence technology progresses, the integration of autonomous AI agents into everyday life becomes increasingly prominent. These agents, capable of making decisions without human intervention, are reshaping industries from healthcare to finance. Exploring case studies where autonomous AI has successfully decreased operational costs or improved efficiency can reveal not only the benefits but also the ethical implications of delegating decision-making to machines. This topic offers an exciting opportunity to dive into the AI landscape, showcasing current developments such as AI assistants and autonomous vehicles.\n\n2. **Ethical Implications of Generative AI in Creative Industries** \n The surge of generative AI tools in creative fields, such as art, music, and writing, has sparked a heated debate about authorship and originality. This article could investigate how these tools are being used by artists and creators, examining both the potential for innovation and the risk of devaluing traditional art forms. Highlighting perspectives from creators, legal experts, and ethicists could provide a comprehensive overview of the challenges faced, including copyright concerns and the emotional impact on human artists. This discussion is vital as the creative landscape evolves alongside technological advancements, making it ripe for exploration.\n\n3. **AI in Climate Change Mitigation: Current Solutions and Future Potential** \n As the world grapples with climate change, AI technology is increasingly being harnessed to develop innovative solutions for sustainability. From predictive analytics that optimize energy consumption to machine learning algorithms that improve carbon capture methods, AI's potential in environmental science is vast. This topic invites an exploration of existing AI applications in climate initiatives, with a focus on groundbreaking research and initiatives aimed at reducing humanity's carbon footprint. Highlighting successful projects and technology partnerships can illustrate the positive impact AI can have on global climate efforts, inspiring further exploration and investment in this area.\n\n4. **The Future of Work: How AI is Reshaping Employment Landscapes** \n The discussions around AI's impact on the workforce are both urgent and complex, as advances in automation and machine learning continue to transform the job market. This article could delve into the current trends of AI-driven job displacement alongside opportunities for upskilling and the creation of new job roles. By examining case studies of companies that integrate AI effectively and the resulting workforce adaptations, readers can gain valuable insights into preparing for a future where humans and AI collaborate. This exploration highlights the importance of policies that promote workforce resilience in the face of change.\n\n5. **Decentralized AI: Exploring the Role of Blockchain in AI Development** \n As blockchain technology sweeps through various sectors, its application in AI development presents a fascinating topic worth examining. Decentralized AI could address issues of data privacy, security, and democratization in AI models by allowing users to retain ownership of data while benefiting from AI's capabilities. This article could analyze how decentralized networks are disrupting traditional AI development models, featuring innovative projects that harness the synergy between blockchain and AI. Highlighting potential pitfalls and the future landscape of decentralized AI could stimulate discussion among technologists, entrepreneurs, and policymakers alike."
)
@@ -624,12 +624,12 @@ def test_crew_with_delegating_agents_should_not_override_task_tools(ceo, writer)
_, kwargs = mock_execute_sync.call_args
tools = kwargs["tools"]
assert any(
isinstance(tool, TestTool) for tool in tools
), "TestTool should be present"
assert any(
"delegate" in tool.name.lower() for tool in tools
), "Delegation tool should be present"
assert any(isinstance(tool, TestTool) for tool in tools), (
"TestTool should be present"
)
assert any("delegate" in tool.name.lower() for tool in tools), (
"Delegation tool should be present"
)
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -688,12 +688,12 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools(ceo, writer
_, kwargs = mock_execute_sync.call_args
tools = kwargs["tools"]
assert any(
isinstance(tool, TestTool) for tool in new_ceo.tools
), "TestTool should be present"
assert any(
"delegate" in tool.name.lower() for tool in tools
), "Delegation tool should be present"
assert any(isinstance(tool, TestTool) for tool in new_ceo.tools), (
"TestTool should be present"
)
assert any("delegate" in tool.name.lower() for tool in tools), (
"Delegation tool should be present"
)
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -817,17 +817,17 @@ def test_task_tools_override_agent_tools_with_allow_delegation(researcher, write
used_tools = kwargs["tools"]
# Confirm AnotherTestTool is present but TestTool is not
assert any(
isinstance(tool, AnotherTestTool) for tool in used_tools
), "AnotherTestTool should be present"
assert not any(
isinstance(tool, TestTool) for tool in used_tools
), "TestTool should not be present among used tools"
assert any(isinstance(tool, AnotherTestTool) for tool in used_tools), (
"AnotherTestTool should be present"
)
assert not any(isinstance(tool, TestTool) for tool in used_tools), (
"TestTool should not be present among used tools"
)
# Confirm delegation tool(s) are present
assert any(
"delegate" in tool.name.lower() for tool in used_tools
), "Delegation tool should be present"
assert any("delegate" in tool.name.lower() for tool in used_tools), (
"Delegation tool should be present"
)
# Finally, make sure the agent's original tools remain unchanged
assert len(researcher_with_delegation.tools) == 1
@@ -931,9 +931,9 @@ def test_cache_hitting_between_agents(researcher, writer, ceo):
tool="multiplier", input={"first_number": 2, "second_number": 6}
)
assert cache_calls[0] == expected_call, f"First call mismatch: {cache_calls[0]}"
assert (
cache_calls[1] == expected_call
), f"Second call mismatch: {cache_calls[1]}"
assert cache_calls[1] == expected_call, (
f"Second call mismatch: {cache_calls[1]}"
)
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -1676,9 +1676,9 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():
# Verify that exactly one tool was used and it was a CodeInterpreterTool
assert len(used_tools) == 1, "Should have exactly one tool"
assert isinstance(
used_tools[0], CodeInterpreterTool
), "Tool should be CodeInterpreterTool"
assert isinstance(used_tools[0], CodeInterpreterTool), (
"Tool should be CodeInterpreterTool"
)
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -1762,10 +1762,10 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
assert result.raw == "Howdy!"
assert result.token_usage == UsageMetrics(
total_tokens=1673,
prompt_tokens=1562,
completion_tokens=111,
successful_requests=3,
total_tokens=2390,
prompt_tokens=2264,
completion_tokens=126,
successful_requests=4,
cached_prompt_tokens=0,
)
@@ -3817,9 +3817,9 @@ def test_fetch_inputs():
expected_placeholders = {"role_detail", "topic", "field"}
actual_placeholders = crew.fetch_inputs()
assert (
actual_placeholders == expected_placeholders
), f"Expected {expected_placeholders}, but got {actual_placeholders}"
assert actual_placeholders == expected_placeholders, (
f"Expected {expected_placeholders}, but got {actual_placeholders}"
)
def test_task_tools_preserve_code_execution_tools():
@@ -3894,20 +3894,20 @@ def test_task_tools_preserve_code_execution_tools():
used_tools = kwargs["tools"]
# Verify all expected tools are present
assert any(
isinstance(tool, TestTool) for tool in used_tools
), "Task's TestTool should be present"
assert any(
isinstance(tool, CodeInterpreterTool) for tool in used_tools
), "CodeInterpreterTool should be present"
assert any(
"delegate" in tool.name.lower() for tool in used_tools
), "Delegation tool should be present"
assert any(isinstance(tool, TestTool) for tool in used_tools), (
"Task's TestTool should be present"
)
assert any(isinstance(tool, CodeInterpreterTool) for tool in used_tools), (
"CodeInterpreterTool should be present"
)
assert any("delegate" in tool.name.lower() for tool in used_tools), (
"Delegation tool should be present"
)
# Verify the total number of tools (TestTool + CodeInterpreter + 2 delegation tools)
assert (
len(used_tools) == 4
), "Should have TestTool, CodeInterpreter, and 2 delegation tools"
assert len(used_tools) == 4, (
"Should have TestTool, CodeInterpreter, and 2 delegation tools"
)
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -3951,9 +3951,9 @@ def test_multimodal_flag_adds_multimodal_tools():
used_tools = kwargs["tools"]
# Check that the multimodal tool was added
assert any(
isinstance(tool, AddImageTool) for tool in used_tools
), "AddImageTool should be present when agent is multimodal"
assert any(isinstance(tool, AddImageTool) for tool in used_tools), (
"AddImageTool should be present when agent is multimodal"
)
# Verify we have exactly one tool (just the AddImageTool)
assert len(used_tools) == 1, "Should only have the AddImageTool"
@@ -4217,9 +4217,9 @@ def test_crew_guardrail_feedback_in_context():
assert len(execution_contexts) > 1, "Task should have been executed multiple times"
# Verify that the second execution included the guardrail feedback
assert (
"Output must contain the keyword 'IMPORTANT'" in execution_contexts[1]
), "Guardrail feedback should be included in retry context"
assert "Output must contain the keyword 'IMPORTANT'" in execution_contexts[1], (
"Guardrail feedback should be included in retry context"
)
# Verify final output meets guardrail requirements
assert "IMPORTANT" in result.raw, "Final output should contain required keyword"
@@ -4435,46 +4435,46 @@ def test_crew_copy_with_memory():
try:
crew_copy = crew.copy()
assert hasattr(
crew_copy, "_short_term_memory"
), "Copied crew should have _short_term_memory"
assert (
crew_copy._short_term_memory is not None
), "Copied _short_term_memory should not be None"
assert (
id(crew_copy._short_term_memory) != original_short_term_id
), "Copied _short_term_memory should be a new object"
assert hasattr(crew_copy, "_short_term_memory"), (
"Copied crew should have _short_term_memory"
)
assert crew_copy._short_term_memory is not None, (
"Copied _short_term_memory should not be None"
)
assert id(crew_copy._short_term_memory) != original_short_term_id, (
"Copied _short_term_memory should be a new object"
)
assert hasattr(
crew_copy, "_long_term_memory"
), "Copied crew should have _long_term_memory"
assert (
crew_copy._long_term_memory is not None
), "Copied _long_term_memory should not be None"
assert (
id(crew_copy._long_term_memory) != original_long_term_id
), "Copied _long_term_memory should be a new object"
assert hasattr(crew_copy, "_long_term_memory"), (
"Copied crew should have _long_term_memory"
)
assert crew_copy._long_term_memory is not None, (
"Copied _long_term_memory should not be None"
)
assert id(crew_copy._long_term_memory) != original_long_term_id, (
"Copied _long_term_memory should be a new object"
)
assert hasattr(
crew_copy, "_entity_memory"
), "Copied crew should have _entity_memory"
assert (
crew_copy._entity_memory is not None
), "Copied _entity_memory should not be None"
assert (
id(crew_copy._entity_memory) != original_entity_id
), "Copied _entity_memory should be a new object"
assert hasattr(crew_copy, "_entity_memory"), (
"Copied crew should have _entity_memory"
)
assert crew_copy._entity_memory is not None, (
"Copied _entity_memory should not be None"
)
assert id(crew_copy._entity_memory) != original_entity_id, (
"Copied _entity_memory should be a new object"
)
if original_external_id:
assert hasattr(
crew_copy, "_external_memory"
), "Copied crew should have _external_memory"
assert (
crew_copy._external_memory is not None
), "Copied _external_memory should not be None"
assert (
id(crew_copy._external_memory) != original_external_id
), "Copied _external_memory should be a new object"
assert hasattr(crew_copy, "_external_memory"), (
"Copied crew should have _external_memory"
)
assert crew_copy._external_memory is not None, (
"Copied _external_memory should not be None"
)
assert id(crew_copy._external_memory) != original_external_id, (
"Copied _external_memory should be a new object"
)
else:
assert (
not hasattr(crew_copy, "_external_memory")
@@ -4754,5 +4754,4 @@ def test_ensure_exchanged_messages_are_propagated_to_external_memory():
external_memory_save.assert_called_once_with(
value=ANY,
metadata={"description": ANY, "messages": expected_messages},
agent=ANY,
)

24
tests/test_task.py
View File

@@ -159,7 +159,7 @@ def test_task_callback_returns_task_output():
"json_dict": None,
"agent": researcher.role,
"summary": "Give me a list of 5 interesting ideas to explore...",
"name": None,
"name": task.name or task.description,
"expected_output": "Bullet point list of 5 interesting ideas.",
"output_format": OutputFormat.RAW,
}
@@ -340,7 +340,7 @@ def test_output_pydantic_hierarchical():
)
result = crew.kickoff()
assert isinstance(result.pydantic, ScoreOutput)
assert result.to_dict() == {"score": 4}
assert result.to_dict() == {"score": 5}
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -401,8 +401,8 @@ def test_output_json_hierarchical():
manager_llm="gpt-4o",
)
result = crew.kickoff()
assert result.json == '{"score": 4}'
assert result.to_dict() == {"score": 4}
assert result.json == '{"score": 5}'
assert result.to_dict() == {"score": 5}
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -560,8 +560,8 @@ def test_output_json_dict_hierarchical():
manager_llm="gpt-4o",
)
result = crew.kickoff()
assert {"score": 4} == result.json_dict
assert result.to_dict() == {"score": 4}
assert {"score": 5} == result.json_dict
assert result.to_dict() == {"score": 5}
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -596,9 +596,9 @@ def test_output_pydantic_to_another_task():
crew = Crew(agents=[scorer], tasks=[task1, task2], verbose=True)
result = crew.kickoff()
pydantic_result = result.pydantic
assert isinstance(
pydantic_result, ScoreOutput
), "Expected pydantic result to be of type ScoreOutput"
assert isinstance(pydantic_result, ScoreOutput), (
"Expected pydantic result to be of type ScoreOutput"
)
assert pydantic_result.score == 5
@@ -1081,9 +1081,9 @@ def test_key():
assert task.key == hash, "The key should be the hash of the description."
task.interpolate_inputs_and_add_conversation_history(inputs={"topic": "AI"})
assert (
task.key == hash
), "The key should be the hash of the non-interpolated description."
assert task.key == hash, (
"The key should be the hash of the non-interpolated description."
)
def test_output_file_validation():

12
tests/tools/test_tool_usage.py
View File

@@ -476,7 +476,7 @@ def test_tool_selection_error_event_direct():
def event_handler(source, event):
received_events.append(event)
with pytest.raises(Exception) as exc_info:
with pytest.raises(Exception):
tool_usage._select_tool("Non Existent Tool")
assert len(received_events) == 1
event = received_events[0]
@@ -490,7 +490,7 @@ def test_tool_selection_error_event_direct():
assert "don't exist" in event.error
received_events.clear()
with pytest.raises(Exception) as exc_info:
with pytest.raises(Exception):
tool_usage._select_tool("")
assert len(received_events) == 1
@@ -563,7 +563,7 @@ def test_tool_validate_input_error_event():
# Test invalid input
invalid_input = "invalid json {[}"
with pytest.raises(Exception) as exc_info:
with pytest.raises(Exception):
tool_usage._validate_tool_input(invalid_input)
# Verify event was emitted
@@ -589,6 +589,9 @@ def test_tool_usage_finished_event_with_result():
# Create mock task
mock_task = MagicMock()
mock_task.delegations = 0
mock_task.name = "Test Task"
mock_task.description = "A test task for tool usage"
mock_task.id = "test-task-id"
# Create mock tool
class TestTool(BaseTool):
@@ -665,6 +668,9 @@ def test_tool_usage_finished_event_with_cached_result():
# Create mock task
mock_task = MagicMock()
mock_task.delegations = 0
mock_task.name = "Test Task"
mock_task.description = "A test task for tool usage"
mock_task.id = "test-task-id"
# Create mock tool
class TestTool(BaseTool):

12
tests/utilities/test_events.py
View File

@@ -891,7 +891,7 @@ def test_stream_llm_emits_event_with_task_and_agent_info():
assert set(all_agent_roles) == {agent.role}
assert set(all_agent_id) == {agent.id}
assert set(all_task_id) == {task.id}
assert set(all_task_name) == {task.name}
assert set(all_task_name) == {task.name or task.description}
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -942,7 +942,7 @@ def test_llm_emits_event_with_task_and_agent_info(base_agent, base_task):
assert set(all_agent_roles) == {base_agent.role}
assert set(all_agent_id) == {base_agent.id}
assert set(all_task_id) == {base_task.id}
assert set(all_task_name) == {base_task.name}
assert set(all_task_name) == {base_task.name or base_task.description}
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -978,9 +978,9 @@ def test_llm_emits_event_with_lite_agent():
)
agent.kickoff(messages=[{"role": "user", "content": "say hi!"}])
assert len(completed_event) == 2
assert len(completed_event) == 1
assert len(failed_event) == 0
assert len(started_event) == 2
assert len(started_event) == 1
assert len(stream_event) == 15
all_events = completed_event + failed_event + started_event + stream_event
@@ -990,8 +990,8 @@ def test_llm_emits_event_with_lite_agent():
all_task_name = [event.task_name for event in all_events if event.task_name]
# ensure all events have the agent + task props set
assert len(all_agent_roles) == 19
assert len(all_agent_id) == 19
assert len(all_agent_roles) == 17
assert len(all_agent_id) == 17
assert len(all_task_id) == 0
assert len(all_task_name) == 0