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Major rehaul of TaskOutput and CrewOutput. Updated all tests to work with new change. Need to add in a few final tricky async tests and add a few more to verify output types on TaskOutput and CrewOutput.

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This commit is contained in:
Brandon Hancock
2024-06-21 16:13:59 -04:00
parent 5c504f4087
commit ee4a996de3
13 changed files with 28954 additions and 5453 deletions
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161
src/crewai/crew.py
View File

@@ -6,15 +6,15 @@ from typing import Any, Dict, List, Optional, Tuple, Union
from langchain_core.callbacks import BaseCallbackHandler
from pydantic import (
UUID4,
BaseModel,
ConfigDict,
Field,
InstanceOf,
Json,
PrivateAttr,
field_validator,
model_validator,
UUID4,
BaseModel,
ConfigDict,
Field,
InstanceOf,
Json,
PrivateAttr,
field_validator,
model_validator,
)
from pydantic_core import PydanticCustomError
@@ -26,9 +26,11 @@ from crewai.memory.long_term.long_term_memory import LongTermMemory
from crewai.memory.short_term.short_term_memory import ShortTermMemory
from crewai.process import Process
from crewai.task import Task
from crewai.tasks.task_output import TaskOutput
from crewai.telemetry import Telemetry
from crewai.tools.agent_tools import AgentTools
from crewai.utilities import I18N, FileHandler, Logger, RPMController
from crewai.utilities.formatter import aggregate_raw_outputs_from_task_outputs
class Crew(BaseModel):
@@ -246,12 +248,12 @@ class Crew(BaseModel):
def kickoff(
self,
inputs: Optional[Dict[str, Any]] = {},
inputs: Optional[Dict[str, Any]] = None,
) -> CrewOutput:
"""Starts the crew to work on its assigned tasks."""
self._execution_span = self._telemetry.crew_execution_span(self)
# type: ignore # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
self._interpolate_inputs(inputs)
if inputs is not None:
self._interpolate_inputs(inputs)
self._set_tasks_callbacks()
i18n = I18N(prompt_file=self.prompt_file)
@@ -297,12 +299,7 @@ class Crew(BaseModel):
for input_data in inputs:
crew = self.copy()
for task in crew.tasks:
task.interpolate_inputs(input_data)
for agent in crew.agents:
agent.interpolate_inputs(input_data)
output = crew.kickoff()
output = crew.kickoff(inputs=input_data)
results.append(output)
return results
@@ -317,12 +314,7 @@ class Crew(BaseModel):
async def run_crew(input_data):
crew = self.copy()
for task in crew.tasks:
task.interpolate_inputs(input_data)
for agent in crew.agents:
agent.interpolate_inputs(input_data)
return await crew.kickoff_async()
return await crew.kickoff_async(inputs=input_data)
tasks = [asyncio.create_task(run_crew(input_data)) for input_data in inputs]
@@ -336,15 +328,8 @@ class Crew(BaseModel):
def _run_sequential_process(self) -> CrewOutput:
"""Executes tasks sequentially and returns the final output."""
# TODO: Check to see if we need to be clearing task output after each task
task_output = ""
futures: List[Tuple[Task, Future]] = []
def _process_task_result(task, output):
role = task.agent.role if task.agent is not None else "None"
self._logger.log("debug", f"== [{role}] Task output: {output}\n\n")
if self.output_log_file:
self._file_handler.log(agent=role, task=output, status="completed")
task_outputs: List[TaskOutput] = []
futures: List[Tuple[Task, Future[TaskOutput]]] = []
for task in self.tasks:
if task.agent.allow_delegation: # type: ignore # Item "None" of "Agent | None" has no attribute "allow_delegation"
@@ -366,47 +351,55 @@ class Crew(BaseModel):
)
if task.async_execution:
context = aggregate_raw_outputs_from_task_outputs(task_outputs)
future = task.execute_async(
agent=task.agent, context=task_output, tools=task.tools
agent=task.agent, context=context, tools=task.tools
)
futures.append(( task, future))
futures.append((task, future))
else:
# Before executing a synchronous task, wait for all async tasks to complete
if futures:
task_output = ""
# Clear task_outputs before processing async tasks
task_outputs = []
for future_task, future in futures:
output = future.result()
task_output += output + "\n\n\n"
_process_task_result(future_task, output)
task_output = future.result()
task_outputs.append(task_output)
self._process_task_result(future_task, task_output)
# Clear the futures list after processing all async results
futures.clear()
output = task.execute_sync(
agent=task.agent, context=task_output, tools=task.tools
context = aggregate_raw_outputs_from_task_outputs(task_outputs)
task_output = task.execute_sync(
agent=task.agent, context=context, tools=task.tools
)
task_output = output
_process_task_result(task, output)
# TODO: Check with Joao to see if we want to add or ignore outputs from async tasks
# Process any remaining async results
task_outputs = [task_output]
self._process_task_result(task, task_output)
if futures:
task_output = ""
# Clear task_outputs before processing async tasks
task_outputs = []
for future_task, future in futures:
output = future.result()
task_output += output + "\n\n\n"
_process_task_result(future_task, output)
task_output = future.result()
task_outputs.append(task_output)
self._process_task_result(future_task, task_output)
self._finish_execution(task_output)
final_string_output = aggregate_raw_outputs_from_task_outputs(task_outputs)
self._finish_execution(final_string_output)
# type: ignore # Item "None" of "Agent | None" has no attribute "_token_process"
token_usage = task.agent._token_process.get_summary()
return self._format_output(task_output, token_usage)
return self._format_output(task_outputs, token_usage)
# TODO: Updates this to mimic the async and sync exeuction of tasks in sequential process
def _process_task_result(self, task: Task, output: TaskOutput) -> None:
role = task.agent.role if task.agent is not None else "None"
self._logger.log("debug", f"== [{role}] Task output: {output}\n\n")
if self.output_log_file:
self._file_handler.log(agent=role, task=output, status="completed")
# TODO: Ask Joao if agent conntected to task can delegate to other agents like we
# can in sequential process. Or, can only the manager delegate to other agents in hierarchical process.
def _run_hierarchical_process(self) -> Tuple[CrewOutput, Dict[str, Any]]:
"""Creates and assigns a manager agent to make sure the crew completes the tasks."""
i18n = I18N(prompt_file=self.prompt_file)
if self.manager_agent is not None:
self.manager_agent.allow_delegation = True
@@ -424,16 +417,12 @@ class Crew(BaseModel):
verbose=True,
)
task_output = ""
futures: List[Tuple[Task, Future]] = []
def _process_task_result(task, output):
role = task.agent.role if task.agent is not None else "None"
self._logger.log("debug", f"== [{role}] Task output: {output}\n\n")
if self.output_log_file:
self._file_handler.log(agent=role, task=output, status="completed")
task_outputs: List[TaskOutput] = []
futures: List[Tuple[Task, Future[TaskOutput]]] = []
for task in self.tasks:
# TODO: In sequential, we allow delegation but we ignore it here. Confirm with Joao.
self._logger.log("debug", f"Working Agent: {manager.role}")
self._logger.log("info", f"Starting Task: {task.description}")
@@ -443,40 +432,45 @@ class Crew(BaseModel):
)
if task.async_execution:
context = aggregate_raw_outputs_from_task_outputs(task_outputs)
future = task.execute_async(
agent=manager, context=task_output, tools=manager.tools
agent=manager, context=context, tools=manager.tools
)
futures.append((task, future))
else:
# Before executing a synchronous task, wait for all async tasks to complete
if futures:
task_output = "" # Clear task_output before processing async tasks
# Clear task_outputs before processing async tasks
task_outputs = []
for future_task, future in futures:
output = future.result()
task_output += output + "\n\n\n"
_process_task_result(future_task, output)
task_output = future.result()
task_outputs.append(task_output)
self._process_task_result(future_task, task_output)
# Clear the futures list after processing all async results
futures.clear()
output = task.execute_sync(
agent=manager, context=task_output, tools=manager.tools
context = aggregate_raw_outputs_from_task_outputs(task_outputs)
task_output = task.execute_sync(
agent=manager, context=context, tools=manager.tools
)
task_output = output # Set task_output to the new result for sync tasks
_process_task_result(task, output)
task_outputs = [task_output]
self._process_task_result(task, task_output)
# Process any remaining async results
if futures:
task_output = "" # Clear task_output before processing async tasks
# Clear task_outputs before processing async tasks
task_outputs = []
for future_task, future in futures:
output = future.result()
task_output += output + "\n\n\n"
_process_task_result(future_task, output)
task_output = future.result()
task_outputs.append(task_output)
self._process_task_result(future_task, task_output)
self._finish_execution(task_output)
final_string_output = aggregate_raw_outputs_from_task_outputs(task_outputs)
self._finish_execution(final_string_output)
manager_token_usage = manager._token_process.get_summary()
return (
self._format_output(task_output, manager_token_usage),
self._format_output(task_outputs, manager_token_usage),
manager_token_usage,
)
@@ -529,25 +523,22 @@ class Crew(BaseModel):
[agent.interpolate_inputs(inputs) for agent in self.agents]
def _format_output(
self, output: str, token_usage: Optional[Dict[str, Any]]
self, output: List[TaskOutput], token_usage: Optional[Dict[str, Any]]
) -> CrewOutput:
"""
Formats the output of the crew execution.
"""
print("Crew Output: ", output)
print("Crew output type: ", type(output))
print("SELF TASKS: ", self.tasks)
print("Tasks Output: ", [task.output for task in self.tasks if task])
return CrewOutput(
final_output=output,
output=output,
tasks_output=[task.output for task in self.tasks if task],
token_output=token_usage,
)
def _finish_execution(self, output: str) -> None:
def _finish_execution(self, final_string_output: str) -> None:
if self.max_rpm:
self._rpm_controller.stop_rpm_counter()
self._telemetry.end_crew(self, output)
self._telemetry.end_crew(self, final_string_output)
def __repr__(self):
return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"

24
src/crewai/crews/crew_output.py
View File

@@ -1,15 +1,13 @@
from typing import Any, Dict, Union
from typing import Any, Dict, List, Optional, Union
from pydantic import BaseModel, Field
from crewai.tasks.task_output import TaskOutput
from crewai.utilities.formatter import aggregate_raw_outputs_from_task_outputs
# TODO: Potentially add in JSON_OUTPUT, PYDANTIC_OUTPUT, etc.
class CrewOutput(BaseModel):
final_output: Union[str, Dict, BaseModel] = Field(
description="Final output of the crew"
)
output: List[TaskOutput] = Field(description="Result of the final task")
tasks_output: list[TaskOutput] = Field(
description="Output of each task", default=[]
)
@@ -17,5 +15,19 @@ class CrewOutput(BaseModel):
description="Processed token summary", default={}
)
def result(self) -> Union[str, BaseModel, Dict[str, Any]]:
"""Return the result of the task based on the available output."""
return self.output.result()
def raw_output(self) -> str:
"""Return the raw output of the task."""
return aggregate_raw_outputs_from_task_outputs(self.output)
def to_output_dict(self) -> Dict[str, Any]:
self.output.to_output_dict()
def __getitem__(self, key: str) -> Any:
self.output[key]
def __str__(self):
return self.final_output
return str(self.raw_output())

147
src/crewai/task.py
View File

@@ -151,16 +151,16 @@ class Task(BaseModel):
agent: Optional[Agent] = None,
context: Optional[str] = None,
tools: Optional[List[Any]] = None,
) -> str:
) -> TaskOutput:
"""Execute the task synchronously."""
return self._execute_task_sync(agent, context, tools)
return self._execute_core(agent, context, tools)
def execute_async(
self,
agent: Optional[Agent] = None,
context: Optional[str] = None,
tools: Optional[List[Any]] = None,
) -> Future:
) -> Future[TaskOutput]:
"""Execute the task asynchronously."""
future = Future()
threading.Thread(
@@ -168,21 +168,12 @@ class Task(BaseModel):
).start()
return future
def _execute_task_sync(
self,
agent: Optional[Agent],
context: Optional[str],
tools: Optional[List[Any]],
) -> str:
"""Execute the task synchronously with context handling."""
return self._execute_core(agent, context, tools)
def _execute_task_async(
self,
agent: Optional[Agent],
context: Optional[str],
tools: Optional[List[Any]],
future: Future,
future: Future[TaskOutput],
) -> None:
"""Execute the task asynchronously with context handling."""
result = self._execute_core(agent, context, tools)
@@ -193,7 +184,7 @@ class Task(BaseModel):
agent: Optional[Agent],
context: Optional[str],
tools: Optional[List[Any]],
) -> str:
) -> TaskOutput:
"""Run the core execution logic of the task."""
agent = agent or self.agent
if not agent:
@@ -221,17 +212,19 @@ class Task(BaseModel):
exported_output = self._export_output(result)
self.output = TaskOutput(
task_output = TaskOutput(
description=self.description,
exported_output=exported_output,
raw_output=result,
pydantic_output=exported_output["pydantic"],
json_output=exported_output["json"],
agent=agent.role,
)
self.output = task_output
if self.callback:
self.callback(self.output)
return exported_output
return task_output
def prompt(self) -> str:
"""Prompt the task.
@@ -292,70 +285,92 @@ class Task(BaseModel):
)
return copied_task
def _export_output(self, result: str) -> Union[str, dict, BaseModel]:
exported_result = result
instructions = "I'm gonna convert this raw text into valid JSON."
def _export_output(
self, result: str
) -> Dict[str, Union[BaseModel, Dict[str, Any]]]:
output = {
"pydantic": None,
"json": None,
}
if self.output_pydantic or self.output_json:
model = self.output_pydantic or self.output_json
model_output = self._convert_to_model(result)
output["pydantic"] = (
model_output if isinstance(model_output, BaseModel) else None
)
output["json"] = model_output if isinstance(model_output, dict) else None
# try to convert task_output directly to pydantic/json
if self.output_file:
self._save_output(output["raw"])
return output
def _convert_to_model(self, result: str) -> Union[dict, BaseModel, str]:
model = self.output_pydantic or self.output_json
try:
return self._validate_model(result, model)
except Exception:
return self._handle_partial_json(result, model)
def _validate_model(
self, result: str, model: Type[BaseModel]
) -> Union[dict, BaseModel]:
exported_result = model.model_validate_json(result)
if self.output_json:
return exported_result.model_dump()
return exported_result
def _handle_partial_json(
self, result: str, model: Type[BaseModel]
) -> Union[dict, BaseModel, str]:
match = re.search(r"({.*})", result, re.DOTALL)
if match:
try:
# type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
exported_result = model.model_validate_json(result)
exported_result = model.model_validate_json(match.group(0))
if self.output_json:
# type: ignore # "str" has no attribute "model_dump"
return exported_result.model_dump()
return exported_result
except Exception:
# sometimes the response contains valid JSON in the middle of text
match = re.search(r"({.*})", result, re.DOTALL)
if match:
try:
# type: ignore # Item "None" of "type[BaseModel] | None" has no attribute "model_validate_json"
exported_result = model.model_validate_json(match.group(0))
if self.output_json:
# type: ignore # "str" has no attribute "model_dump"
return exported_result.model_dump()
return exported_result
except Exception:
pass
pass
# type: ignore # Item "None" of "Agent | None" has no attribute "function_calling_llm"
llm = self.agent.function_calling_llm or self.agent.llm
return self._convert_with_instructions(result, model)
if not self._is_gpt(llm):
# type: ignore # Argument "model" to "PydanticSchemaParser" has incompatible type "type[BaseModel] | None"; expected "type[BaseModel]"
model_schema = PydanticSchemaParser(model=model).get_schema()
instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
def _convert_with_instructions(
self, result: str, model: Type[BaseModel]
) -> Union[dict, BaseModel, str]:
llm = self.agent.function_calling_llm or self.agent.llm
instructions = self._get_conversion_instructions(model, llm)
converter = Converter(
llm=llm, text=result, model=model, instructions=instructions
converter = Converter(
llm=llm, text=result, model=model, instructions=instructions
)
exported_result = (
converter.to_pydantic() if self.output_pydantic else converter.to_json()
)
if isinstance(exported_result, ConverterError):
Printer().print(
content=f"{exported_result.message} Using raw output instead.",
color="red",
)
if self.output_pydantic:
exported_result = converter.to_pydantic()
elif self.output_json:
exported_result = converter.to_json()
if isinstance(exported_result, ConverterError):
Printer().print(
content=f"{exported_result.message} Using raw output instead.",
color="red",
)
exported_result = result
if self.output_file:
content = (
# type: ignore # "str" has no attribute "json"
exported_result
if not self.output_pydantic
else exported_result.json()
)
self._save_file(content)
return result
return exported_result
def _get_conversion_instructions(self, model: Type[BaseModel], llm: Any) -> str:
instructions = "I'm gonna convert this raw text into valid JSON."
if not self._is_gpt(llm):
model_schema = PydanticSchemaParser(model=model).get_schema()
instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
return instructions
def _save_output(self, content: str) -> None:
directory = os.path.dirname(self.output_file)
if directory and not os.path.exists(directory):
os.makedirs(directory)
with open(self.output_file, "w", encoding="utf-8") as file:
file.write(content)
def _is_gpt(self, llm) -> bool:
return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None

44
src/crewai/tasks/task_output.py
View File

@@ -1,24 +1,56 @@
from typing import Optional, Union
from typing import Any, Dict, Optional, Union
from pydantic import BaseModel, Field, model_validator
# TODO: This is a breaking change. Confirm with @joao
class TaskOutput(BaseModel):
"""Class that represents the result of a task."""
description: str = Field(description="Description of the task")
summary: Optional[str] = Field(description="Summary of the task", default=None)
exported_output: Union[str, BaseModel] = Field(
description="Output of the task", default=None
raw_output: str = Field(description="Result of the task")
pydantic_output: Optional[BaseModel] = Field(
description="Pydantic model output", default=None
)
json_output: Optional[Dict[str, Any]] = Field(
description="JSON output", default=None
)
agent: str = Field(description="Agent that executed the task")
raw_output: str = Field(description="Result of the task")
@model_validator(mode="after")
def set_summary(self):
"""Set the summary field based on the description."""
excerpt = " ".join(self.description.split(" ")[:10])
self.summary = f"{excerpt}..."
return self
def result(self):
return self.exported_output
# TODO: Ask @joao what is the desired behavior here
def result(self) -> Union[str, BaseModel, Dict[str, Any]]:
"""Return the result of the task based on the available output."""
if self.pydantic_output:
return self.pydantic_output
elif self.json_output:
return self.json_output
else:
return self.raw_output
def __getitem__(self, key: str) -> Any:
"""Retrieve a value from the pydantic_output or json_output based on the key."""
if self.pydantic_output and hasattr(self.pydantic_output, key):
return getattr(self.pydantic_output, key)
if self.json_output and key in self.json_output:
return self.json_output[key]
raise KeyError(f"Key '{key}' not found in pydantic_output or json_output")
def to_output_dict(self) -> Dict[str, Any]:
"""Convert json_output and pydantic_output to a dictionary."""
output_dict = {}
if self.json_output:
output_dict.update(self.json_output)
if self.pydantic_output:
output_dict.update(self.pydantic_output.dict())
return output_dict
def __str__(self) -> str:
return self.raw_output

6
src/crewai/telemetry/telemetry.py
View File

@@ -273,7 +273,7 @@ class Telemetry:
except Exception:
pass
def end_crew(self, crew, output):
def end_crew(self, crew, final_string_output):
if (self.ready) and (crew.share_crew):
try:
self._add_attribute(
@@ -281,7 +281,9 @@ class Telemetry:
"crewai_version",
pkg_resources.get_distribution("crewai").version,
)
self._add_attribute(crew._execution_span, "crew_output", output)
self._add_attribute(
crew._execution_span, "crew_output", final_string_output
)
self._add_attribute(
crew._execution_span,
"crew_tasks_output",

4
src/crewai/utilities/__init__.py
View File

@@ -1,9 +1,9 @@
from .converter import Converter, ConverterError
from .fileHandler import FileHandler
from .i18n import I18N
from .instructor import Instructor
from .logger import Logger
from .parser import YamlParser
from .printer import Printer
from .prompts import Prompts
from .rpm_controller import RPMController
from .fileHandler import FileHandler
from .parser import YamlParser

12
src/crewai/utilities/formatter.py Normal file
View File

@@ -0,0 +1,12 @@
from typing import List
from crewai.tasks.task_output import TaskOutput
def aggregate_raw_outputs_from_task_outputs(task_outputs: List[TaskOutput]) -> str:
"""Generate string context from the task outputs."""
dividers = "\n\n----------\n\n"
# Join task outputs with dividers
context = dividers.join(output.raw_output for output in task_outputs)
return context

5212
tests/cassettes/test_async_task_execution_completion.yaml
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20833
tests/cassettes/test_hierarchical_async_task_execution_completion.yaml Normal file
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tests/cassettes/test_sequential_async_task_execution_completion.yaml Normal file
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333
tests/cassettes/test_single_task_with_async_execution.yaml Normal file
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@@ -0,0 +1,333 @@
interactions:
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personal goal is: Make the best research and analysis on content about AI and
AI agentsTo give my best complete final answer to the task use the exact following
format:\n\nThought: I now can give a great answer\nFinal Answer: my best complete
final answer to the task.\nYour 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!\nCurrent Task: Generate a list of 5 interesting ideas
to explore for an article, where each bulletpoint is under 15 words.\n\nThis
is the expect criteria for your final answer: Bullet point list of 5 important
events. No additional commentary. \n you 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:\n", "role": "user"}], "model": "gpt-4o", "n": 1, "stop": ["\nObservation"],
"stream": true, "temperature": 0.7}'
headers:
accept:
- application/json
accept-encoding:
- gzip, deflate, br
connection:
- keep-alive
content-length:
- '1237'
content-type:
- application/json
host:
- api.openai.com
user-agent:
- OpenAI/Python 1.34.0
x-stainless-arch:
- arm64
x-stainless-async:
- 'false'
x-stainless-lang:
- python
x-stainless-os:
- MacOS
x-stainless-package-version:
- 1.34.0
x-stainless-runtime:
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x-stainless-runtime-version:
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1097
tests/cassettes/test_three_task_with_async_execution.yaml Normal file
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tests/crew_test.py
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@@ -139,12 +139,12 @@ def test_crew_creation():
result = crew.kickoff()
assert (
result.final_output
== "1. **The Rise of AI in Healthcare**: The convergence of AI and healthcare is a promising frontier, offering unprecedented opportunities for disease diagnosis and patient outcome prediction. AI's potential to revolutionize healthcare lies in its capacity to synthesize vast amounts of data, generating precise and efficient results. This technological breakthrough, however, is not just about improving accuracy and efficiency; it's about saving lives. As we stand on the precipice of this transformative era, we must prepare for the complex challenges and ethical questions it poses, while embracing its ability to reshape healthcare as we know it.\n\n2. **Ethical Implications of AI**: As AI intertwines with our daily lives, it presents a complex web of ethical dilemmas. This fusion of technology, philosophy, and ethics is not merely academically intriguing but profoundly impacts the fabric of our society. The questions raised range from decision-making transparency to accountability, and from privacy to potential biases. As we navigate this ethical labyrinth, it is crucial to establish robust frameworks and regulations to ensure that AI serves humanity, and not the other way around.\n\n3. **AI and Data Privacy**: The rise of AI brings with it an insatiable appetite for data, spawning new debates around privacy rights. Balancing the potential benefits of AI with the right to privacy is a unique challenge that intersects technology, law, and human rights. In an increasingly digital world, where personal information forms the backbone of many services, we must grapple with these issues. It's time to redefine the concept of privacy and devise innovative solutions that ensure our digital footprints are not abused.\n\n4. **AI in Job Market**: The discourse around AI's impact on employment is a narrative of contrast, a tale of displacement and creation. On one hand, AI threatens to automate a multitude of jobs, on the other, it promises to create new roles that we cannot yet imagine. This intersection of technology, economics, and labor rights is a critical dialogue that will shape our future. As we stand at this crossroads, we must not only brace ourselves for the changes but also seize the opportunities that this technological wave brings.\n\n5. **Future of AI Agents**: The evolution of AI agents signifies a leap towards a future where AI is not just a tool, but a partner. These sophisticated AI agents, employed in customer service to personal assistants, are redefining our interactions with technology. As we gaze into the future of AI agents, we see a landscape of possibilities and challenges. This journey will be about harnessing the potential of AI agents while navigating the issues of trust, dependence, and ethical use."
)
expected_string_output = "1. **The Rise of AI in Healthcare**: The convergence of AI and healthcare is a promising frontier, offering unprecedented opportunities for disease diagnosis and patient outcome prediction. AI's potential to revolutionize healthcare lies in its capacity to synthesize vast amounts of data, generating precise and efficient results. This technological breakthrough, however, is not just about improving accuracy and efficiency; it's about saving lives. As we stand on the precipice of this transformative era, we must prepare for the complex challenges and ethical questions it poses, while embracing its ability to reshape healthcare as we know it.\n\n2. **Ethical Implications of AI**: As AI intertwines with our daily lives, it presents a complex web of ethical dilemmas. This fusion of technology, philosophy, and ethics is not merely academically intriguing but profoundly impacts the fabric of our society. The questions raised range from decision-making transparency to accountability, and from privacy to potential biases. As we navigate this ethical labyrinth, it is crucial to establish robust frameworks and regulations to ensure that AI serves humanity, and not the other way around.\n\n3. **AI and Data Privacy**: The rise of AI brings with it an insatiable appetite for data, spawning new debates around privacy rights. Balancing the potential benefits of AI with the right to privacy is a unique challenge that intersects technology, law, and human rights. In an increasingly digital world, where personal information forms the backbone of many services, we must grapple with these issues. It's time to redefine the concept of privacy and devise innovative solutions that ensure our digital footprints are not abused.\n\n4. **AI in Job Market**: The discourse around AI's impact on employment is a narrative of contrast, a tale of displacement and creation. On one hand, AI threatens to automate a multitude of jobs, on the other, it promises to create new roles that we cannot yet imagine. This intersection of technology, economics, and labor rights is a critical dialogue that will shape our future. As we stand at this crossroads, we must not only brace ourselves for the changes but also seize the opportunities that this technological wave brings.\n\n5. **Future of AI Agents**: The evolution of AI agents signifies a leap towards a future where AI is not just a tool, but a partner. These sophisticated AI agents, employed in customer service to personal assistants, are redefining our interactions with technology. As we gaze into the future of AI agents, we see a landscape of possibilities and challenges. This journey will be about harnessing the potential of AI agents while navigating the issues of trust, dependence, and ethical use."
assert str(result) == expected_string_output
assert result.raw_output() == expected_string_output
assert isinstance(result, CrewOutput)
assert len(result.tasks_output) == len(tasks)
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -170,8 +170,17 @@ def test_sync_task_execution():
tasks=tasks,
)
mock_task_output = TaskOutput(
description="Mock description", raw_output="mocked output", agent="mocked agent"
)
# Because we are mocking execute_sync, we never hit the underlying _execute_core
# which sets the output attribute of the task
for task in tasks:
task.output = mock_task_output
with patch.object(
Task, "execute_sync", return_value="mocked output"
Task, "execute_sync", return_value=mock_task_output
) as mock_execute_sync:
crew.kickoff()
@@ -198,7 +207,7 @@ def test_hierarchical_process():
result = crew.kickoff()
assert (
result.final_output
result.raw_output()
== "1. 'Demystifying AI: An in-depth exploration of Artificial Intelligence for the layperson' - In this piece, we will unravel the enigma of AI, simplifying its complexities into digestible information for the everyday individual. By using relatable examples and analogies, we will journey through the neural networks and machine learning algorithms that define AI, without the jargon and convoluted explanations that often accompany such topics.\n\n2. 'The Role of AI in Startups: A Game Changer?' - Startups today are harnessing the power of AI to revolutionize their businesses. This article will delve into how AI, as an innovative force, is shaping the startup ecosystem, transforming everything from customer service to product development. We'll explore real-life case studies of startups that have leveraged AI to accelerate their growth and disrupt their respective industries.\n\n3. 'AI and Ethics: Navigating the Complex Landscape' - AI brings with it not just technological advancements, but ethical dilemmas as well. This article will engage readers in a thought-provoking discussion on the ethical implications of AI, exploring issues like bias in algorithms, privacy concerns, job displacement, and the moral responsibility of AI developers. We will also discuss potential solutions and frameworks to address these challenges.\n\n4. 'Unveiling the AI Agents: The Future of Customer Service' - AI agents are poised to reshape the customer service landscape, offering businesses the ability to provide round-the-clock support and personalized experiences. In this article, we'll dive deep into the world of AI agents, examining how they work, their benefits and limitations, and how they're set to redefine customer interactions in the digital age.\n\n5. 'From Science Fiction to Reality: AI in Everyday Life' - AI, once a concept limited to the realm of sci-fi, has now permeated our daily lives. This article will highlight the ubiquitous presence of AI, from voice assistants and recommendation algorithms, to autonomous vehicles and smart homes. We'll explore how AI, in its various forms, is transforming our everyday experiences, making the future seem a lot closer than we imagined."
)
@@ -236,7 +245,7 @@ def test_crew_with_delegating_agents():
result = crew.kickoff()
assert (
result.final_output
result.raw_output()
== "AI Agents, simply put, are intelligent systems that can perceive their environment and take actions to reach specific goals. Imagine them as digital assistants that can learn, adapt and make decisions. They operate in the realms of software or hardware, like a chatbot on a website or a self-driving car. The key to their intelligence is their ability to learn from their experiences, making them better at their tasks over time. In today's interconnected world, AI agents are transforming our lives. They enhance customer service experiences, streamline business processes, and even predict trends in data. Vehicles equipped with AI agents are making transportation safer. In healthcare, AI agents are helping to diagnose diseases, personalizing treatment plans, and monitoring patient health. As we embrace the digital era, these AI agents are not just important, they're becoming indispensable, shaping a future where technology works intuitively and intelligently to meet our needs."
)
@@ -422,24 +431,22 @@ def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
"""
Future tests:
TODO: Make sure 1 async task return results and waits for async to fnish before returning result
TODO: Make sure 3 async tasks return result from final task. Make sure Joao approves of this.
TODO: 1 async task, 1 sync task. Make sure sync task waits for async to finish before starting.
TODO: 3 async tasks, 1 sync task. Make sure sync task waits for async to finish before starting.
TODO: 1 sync task, 1 async task. Make sure we wait for result from async before finishing crew.
TODO: 3 async tasks, 1 sync task. Make sure context from all 3 async tasks is passed to sync task.
TODO: 3 async tasks, 1 sync task. Pass in context from only 1 async task to sync task.
TODO: Test pydantic output of CrewOutput and test type in CrewOutput result
TODO: Test json output of CrewOutput and test type in CrewOutput result
TODO: TEST THE SAME THING BUT WITH HIERARCHICAL PROCESS
"""
@pytest.mark.vcr(filter_headers=["authorization"])
def test_async_task_execution_completion():
import pdb
import threading
from unittest.mock import patch
from crewai.tasks.task_output import TaskOutput
def test_sequential_async_task_execution_completion():
list_ideas = Task(
description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
expected_output="Bullet point list of 5 important events.",
@@ -459,45 +466,21 @@ def test_async_task_execution_completion():
context=[list_ideas, list_important_history],
)
crew = Crew(
sequential_crew = Crew(
agents=[researcher, writer],
process=Process.sequential,
tasks=[list_ideas, list_important_history, write_article],
)
result = crew.kickoff()
assert result.final_output.startswith(
"Artificial Intelligence (AI) has a rich and storied history, marked by significant milestones that have shaped its development and societal impact."
sequential_result = sequential_crew.kickoff()
assert sequential_result.raw_output().startswith(
"**The Evolution of Artificial Intelligence: A Journey Through Milestones**"
)
# with patch.object(Agent, "execute_task") as execute:
# execute.return_value = "ok"
# with patch.object(threading.Thread, "start") as start:
# thread = threading.Thread(target=lambda: None, args=()).start()
# start.return_value = thread
# with patch.object(threading.Thread, "join", wraps=thread.join()) as join:
# list_ideas.output = TaskOutput(
# description="A 4 paragraph article about AI.",
# raw_output="ok",
# agent="writer",
# )
# list_important_history.output = TaskOutput(
# description="A 4 paragraph article about AI.",
# raw_output="ok",
# agent="writer",
# )
# crew.kickoff()
# start.assert_called()
# join.assert_called()
@pytest.mark.vcr(filter_headers=["authorization"])
def test_async_task_execution_completion():
import pdb
import threading
from unittest.mock import patch
from crewai.tasks.task_output import TaskOutput
def test_hierarchical_async_task_execution_completion():
from langchain_openai import ChatOpenAI
list_ideas = Task(
description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
@@ -518,21 +501,21 @@ def test_async_task_execution_completion():
context=[list_ideas, list_important_history],
)
crew = Crew(
hierarchical_crew = Crew(
agents=[researcher, writer],
process=Process.sequential,
process=Process.hierarchical,
tasks=[list_ideas, list_important_history, write_article],
manager_llm=ChatOpenAI(temperature=0, model="gpt-4"),
)
result = crew.kickoff()
assert result.final_output.startswith(
"Artificial Intelligence (AI) has a rich and storied history, marked by significant milestones that have shaped its development and societal impact."
hierarchical_result = hierarchical_crew.kickoff()
assert hierarchical_result.raw_output().startswith(
"The history of Artificial Intelligence (AI) is a fascinating journey that charts the evolution of human ingenuity and technological advancement."
)
@pytest.mark.vcr(filter_headers=["authorization"])
def test_single_task_with_async_execution():
from unittest.mock import patch
researcher_agent = Agent(
role="Researcher",
@@ -542,10 +525,10 @@ def test_single_task_with_async_execution():
)
list_ideas = Task(
description="Give me a short list of 5 interesting ideas to explore for an article, what makes them unique and interesting.",
expected_output="Bullet point list of 5 important events.",
description="Generate a list of 5 interesting ideas to explore for an article, where each bulletpoint is under 15 words.",
expected_output="Bullet point list of 5 important events. No additional commentary.",
agent=researcher_agent,
# async_execution=True,
async_execution=True,
)
crew = Crew(
@@ -555,11 +538,90 @@ def test_single_task_with_async_execution():
)
result = crew.kickoff()
print(result)
assert result.final_output == ""
print(result.raw_output())
assert result.raw_output().startswith(
"- The impact of AI agents on remote work productivity."
)
# TODO: Make sure sync and async task execution keeps right order of expected outputs
@pytest.mark.vcr(filter_headers=["authorization"])
def test_three_task_with_async_execution():
researcher_agent = Agent(
role="Researcher",
goal="Make the best research and analysis on content about AI and AI agents",
backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
allow_delegation=False,
)
bullet_list = Task(
description="Generate a list of 5 interesting ideas to explore for an article, where each bulletpoint is under 15 words.",
expected_output="Bullet point list of 5 important events. No additional commentary.",
agent=researcher_agent,
async_execution=True,
)
numbered_list = Task(
description="Generate a list of 5 interesting ideas to explore for an article, where each bulletpoint is under 15 words.",
expected_output="Numbered list of 5 important events. No additional commentary.",
agent=researcher_agent,
async_execution=True,
)
letter_list = Task(
description="Generate a list of 5 interesting ideas to explore for an article, where each bulletpoint is under 15 words.",
expected_output="Numbered list using [A), B), C)] list of 5 important events. No additional commentary.",
agent=researcher_agent,
async_execution=True,
)
crew = Crew(
agents=[researcher_agent],
process=Process.sequential,
tasks=[bullet_list, numbered_list, letter_list],
)
# Expected result is that we are going to concatenate the output from each async task.
# Because we add a buffer between each task, we should see a "----------" string
# after the first and second task in the final output.
result = crew.kickoff()
assert result.raw_output().count("\n\n----------\n\n") == 2
def test_wait_for_async_execution_before_sync_execution():
researcher_agent = Agent(
role="Researcher",
goal="Make the best research and analysis on content about AI and AI agents",
backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
allow_delegation=False,
)
writer_agent = Agent(
role="Writer",
goal="Write the best content about AI and AI agents.",
backstory="You're a writer, specialized in technology, software engineering, AI and startups. You work as a freelancer and are now working on writing content for a new customer.",
allow_delegation=False,
)
bullet_list = Task(
description="Generate a list of 5 interesting ideas to explore for an article, where each bulletpoint is under 15 words.",
expected_output="Bullet point list of 5 important events. No additional commentary.",
agent=researcher_agent,
async_execution=True,
)
article_writer = Task(
description="Convert a bulleted list into a 1 paragraph article.",
expected_output="A paragraph article with 5 sentences.",
agent=writer_agent,
async_execution=False,
)
crew = Crew(
agents=[researcher_agent, writer_agent],
process=Process.sequential,
tasks=[bullet_list, article_writer],
)
# Expected output is that the sync task will wait for the async task to finish before starting.
# TODO: Mock the output of the async task
# TODO: Mocke the `execute_sync` Task to check that it was passed the context from the async task
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -590,25 +652,22 @@ def test_async_task_execution_call_count():
tasks=[list_ideas, list_important_history, write_article],
)
# Create a MagicMock Future instance
mock_future = MagicMock(spec=Future)
mock_future.result.return_value = "ok"
# Create a valid TaskOutput instance to mock the return value
mock_task_output = TaskOutput(
description="Mocked Task Output",
exported_output="mocked output",
raw_output="mocked raw output",
agent="mocked agent",
description="Mock description", raw_output="mocked output", agent="mocked agent"
)
# Create a MagicMock Future instance
mock_future = MagicMock(spec=Future)
mock_future.result.return_value = mock_task_output
# Directly set the output attribute for each task
list_ideas.output = mock_task_output
list_important_history.output = mock_task_output
write_article.output = mock_task_output
with patch.object(
Task, "execute_sync", return_value="ok"
Task, "execute_sync", return_value=mock_task_output
) as mock_execute_sync, patch.object(
Task, "execute_async", return_value=mock_future
) as mock_execute_async:
@@ -619,6 +678,9 @@ def test_async_task_execution_call_count():
assert mock_execute_sync.call_count == 1
# ---- TEST FOR HIERARCHICAL --- #
# TODO: Add back in
# def test_set_agents_step_callback():
# from unittest.mock import patch
@@ -750,7 +812,7 @@ def test_task_with_no_arguments():
crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
assert result.final_output == "75"
assert result.raw_output() == "75"
def test_delegation_is_not_enabled_if_there_are_only_one_agent():
@@ -790,7 +852,7 @@ def test_agents_do_not_get_delegation_tools_with_there_is_only_one_agent():
result = crew.kickoff()
assert (
result.final_output
result.raw_output()
== "Howdy! I hope this message finds you well and brings a smile to your face. Have a fantastic day!"
)
assert len(agent.tools) == 0
@@ -810,7 +872,7 @@ def test_agent_usage_metrics_are_captured_for_sequential_process():
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
assert result.final_output == "Howdy!"
assert result.raw_output() == "Howdy!"
required_keys = [
"total_tokens",
@@ -844,7 +906,7 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
)
result = crew.kickoff()
assert result.final_output == '"Howdy!"'
assert result.raw_output() == '"Howdy!"'
required_keys = [
"total_tokens",
@@ -910,6 +972,78 @@ def test_crew_inputs_interpolate_both_agents_and_tasks_diff():
interpolate_task_inputs.assert_called()
def test_crew_does_not_interpolate_without_inputs():
from unittest.mock import patch
agent = Agent(
role="{topic} Researcher",
goal="Express hot takes on {topic}.",
backstory="You have a lot of experience with {topic}.",
)
task = Task(
description="Give me an analysis around {topic}.",
expected_output="{points} bullet points about {topic}.",
agent=agent,
)
crew = Crew(agents=[agent], tasks=[task])
with patch.object(Agent, "interpolate_inputs") as interpolate_agent_inputs:
with patch.object(Task, "interpolate_inputs") as interpolate_task_inputs:
crew.kickoff()
interpolate_agent_inputs.assert_not_called()
interpolate_task_inputs.assert_not_called()
# TODO: Ask @joao if we want to start throwing errors if inputs are not provided
# def test_crew_partial_inputs():
# agent = Agent(
# role="{topic} Researcher",
# goal="Express hot takes on {topic}.",
# backstory="You have a lot of experience with {topic}.",
# )
# task = Task(
# description="Give me an analysis around {topic}.",
# expected_output="{points} bullet points about {topic}.",
# )
# crew = Crew(agents=[agent], tasks=[task], inputs={"topic": "AI"})
# inputs = {"topic": "AI"}
# crew._interpolate_inputs(inputs=inputs) # Manual call for now
# assert crew.tasks[0].description == "Give me an analysis around AI."
# assert crew.tasks[0].expected_output == "{points} bullet points about AI."
# assert crew.agents[0].role == "AI Researcher"
# assert crew.agents[0].goal == "Express hot takes on AI."
# assert crew.agents[0].backstory == "You have a lot of experience with AI."
# TODO: If we do want ot throw errors if we are missing inputs. Add in this test.
# def test_crew_invalid_inputs():
# agent = Agent(
# role="{topic} Researcher",
# goal="Express hot takes on {topic}.",
# backstory="You have a lot of experience with {topic}.",
# )
# task = Task(
# description="Give me an analysis around {topic}.",
# expected_output="{points} bullet points about {topic}.",
# )
# crew = Crew(agents=[agent], tasks=[task], inputs={"subject": "AI"})
# inputs = {"subject": "AI"}
# crew._interpolate_inputs(inputs=inputs) # Manual call for now
# assert crew.tasks[0].description == "Give me an analysis around {topic}."
# assert crew.tasks[0].expected_output == "{points} bullet points about {topic}."
# assert crew.agents[0].role == "{topic} Researcher"
# assert crew.agents[0].goal == "Express hot takes on {topic}."
# assert crew.agents[0].backstory == "You have a lot of experience with {topic}."
# TODO: Add back in
# def test_task_callback_on_crew():
# from unittest.mock import patch
@@ -1010,7 +1144,7 @@ def test_tools_with_custom_caching():
input={"first_number": 2, "second_number": 6},
output=12,
)
assert result.final_output == "3"
assert result.raw_output() == "3"
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -1087,7 +1221,7 @@ def test_crew_log_file_output(tmp_path):
@pytest.mark.vcr(filter_headers=["authorization"])
def test_manager_agent():
from unittest.mock import MagicMock, patch
from unittest.mock import patch
task = Task(
description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
@@ -1108,8 +1242,16 @@ def test_manager_agent():
tasks=[task],
)
mock_task_output = TaskOutput(
description="Mock description", raw_output="mocked output", agent="mocked agent"
)
# Because we are mocking execute_sync, we never hit the underlying _execute_core
# which sets the output attribute of the task
task.output = mock_task_output
with patch.object(
Task, "execute_sync", return_value="Example output for a task."
Task, "execute_sync", return_value=mock_task_output
) as mock_execute_sync:
crew.kickoff()
assert manager.allow_delegation is True