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Merge branch 'main' into main

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fzowl
2025-01-15 19:50:56 +01:00
committed by GitHub
parent e8b71fcfa1 4185ea688f
commit 0b19f2fafa
16 changed files with 3221 additions and 157 deletions
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2
pyproject.toml
View File

@@ -11,7 +11,7 @@ dependencies = [
# Core Dependencies
"pydantic>=2.4.2",
"openai>=1.13.3",
"litellm>=1.44.22",
"litellm==1.57.4",
"instructor>=1.3.3",
# Text Processing

3
src/crewai/crew.py
View File

@@ -513,6 +513,8 @@ class Crew(BaseModel):
inputs: Optional[Dict[str, Any]] = None,
) -> CrewOutput:
for before_callback in self.before_kickoff_callbacks:
if inputs is None:
inputs = {}
inputs = before_callback(inputs)
"""Starts the crew to work on its assigned tasks."""
@@ -674,6 +676,7 @@ class Crew(BaseModel):
else:
self.manager_llm = (
getattr(self.manager_llm, "model_name", None)
or getattr(self.manager_llm, "model", None)
or getattr(self.manager_llm, "deployment_name", None)
or self.manager_llm
)

53
src/crewai/project/crew_base.py
View File

@@ -1,4 +1,5 @@
import inspect
import logging
from pathlib import Path
from typing import Any, Callable, Dict, TypeVar, cast
@@ -7,12 +8,16 @@ from dotenv import load_dotenv
load_dotenv()
logging.basicConfig(level=logging.WARNING)
T = TypeVar("T", bound=type)
"""Base decorator for creating crew classes with configuration and function management."""
def CrewBase(cls: T) -> T:
"""Wraps a class with crew functionality and configuration management."""
class WrappedClass(cls): # type: ignore
is_crew_class: bool = True # type: ignore
@@ -26,16 +31,9 @@ def CrewBase(cls: T) -> T:
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
agents_config_path = self.base_directory / self.original_agents_config_path
tasks_config_path = self.base_directory / self.original_tasks_config_path
self.agents_config = self.load_yaml(agents_config_path)
self.tasks_config = self.load_yaml(tasks_config_path)
self.load_configurations()
self.map_all_agent_variables()
self.map_all_task_variables()
# Preserve all decorated functions
self._original_functions = {
name: method
@@ -51,7 +49,6 @@ def CrewBase(cls: T) -> T:
]
)
}
# Store specific function types
self._original_tasks = self._filter_functions(
self._original_functions, "is_task"
@@ -69,6 +66,44 @@ def CrewBase(cls: T) -> T:
self._original_functions, "is_kickoff"
)
def load_configurations(self):
"""Load agent and task configurations from YAML files."""
if isinstance(self.original_agents_config_path, str):
agents_config_path = (
self.base_directory / self.original_agents_config_path
)
try:
self.agents_config = self.load_yaml(agents_config_path)
except FileNotFoundError:
logging.warning(
f"Agent config file not found at {agents_config_path}. "
"Proceeding with empty agent configurations."
)
self.agents_config = {}
else:
logging.warning(
"No agent configuration path provided. Proceeding with empty agent configurations."
)
self.agents_config = {}
if isinstance(self.original_tasks_config_path, str):
tasks_config_path = (
self.base_directory / self.original_tasks_config_path
)
try:
self.tasks_config = self.load_yaml(tasks_config_path)
except FileNotFoundError:
logging.warning(
f"Task config file not found at {tasks_config_path}. "
"Proceeding with empty task configurations."
)
self.tasks_config = {}
else:
logging.warning(
"No task configuration path provided. Proceeding with empty task configurations."
)
self.tasks_config = {}
@staticmethod
def load_yaml(config_path: Path):
try:

2
src/crewai/translations/en.json
View File

@@ -43,7 +43,7 @@
"ask_question": "Ask a specific question to one of the following coworkers: {coworkers}\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolute everything you know, don't reference things but instead explain them.",
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4
src/crewai/utilities/llm_utils.py
View File

@@ -1,11 +1,7 @@
import os
from typing import Any, Dict, List, Optional, Union
from packaging import version
from crewai.cli.constants import DEFAULT_LLM_MODEL, ENV_VARS, LITELLM_PARAMS
from crewai.cli.utils import read_toml
from crewai.cli.version import get_crewai_version
from crewai.llm import LLM

2
tests/agent_test.py
View File

@@ -1495,7 +1495,7 @@ def test_agent_execute_task_basic():
role="test role",
goal="test goal",
backstory="test backstory",
llm=LLM(model="gpt-3.5-turbo"),
llm="gpt-4o-mini",
)
task = Task(

70
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869
tests/cassettes/test_agent_with_ollama_llama3.yaml
View File

@@ -1,36 +1,863 @@
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117
tests/crew_test.py
View File

@@ -16,6 +16,7 @@ from crewai.crew import Crew
from crewai.crews.crew_output import CrewOutput
from crewai.memory.contextual.contextual_memory import ContextualMemory
from crewai.process import Process
from crewai.project import crew
from crewai.task import Task
from crewai.tasks.conditional_task import ConditionalTask
from crewai.tasks.output_format import OutputFormat
@@ -3474,3 +3475,119 @@ def test_crew_guardrail_feedback_in_context():
# Verify task retry count
assert task.retry_count == 1, "Task should have been retried once"
@pytest.mark.vcr(filter_headers=["authorization"])
def test_before_kickoff_callback():
from crewai.project import CrewBase, agent, before_kickoff, crew, task
@CrewBase
class TestCrewClass:
agents_config = None
tasks_config = None
def __init__(self):
self.inputs_modified = False
@before_kickoff
def modify_inputs(self, inputs):
self.inputs_modified = True
inputs["modified"] = True
return inputs
@agent
def my_agent(self):
return Agent(
role="Test Agent",
goal="Test agent goal",
backstory="Test agent backstory",
)
@task
def my_task(self):
task = Task(
description="Test task description",
expected_output="Test expected output",
agent=self.my_agent(), # Use the agent instance
)
return task
@crew
def crew(self):
return Crew(agents=self.agents, tasks=self.tasks)
test_crew_instance = TestCrewClass()
crew = test_crew_instance.crew()
# Verify that the before_kickoff_callbacks are set
assert len(crew.before_kickoff_callbacks) == 1
# Prepare inputs
inputs = {"initial": True}
# Call kickoff
crew.kickoff(inputs=inputs)
# Check that the before_kickoff function was called and modified inputs
assert test_crew_instance.inputs_modified
assert inputs.get("modified") == True
@pytest.mark.vcr(filter_headers=["authorization"])
def test_before_kickoff_without_inputs():
from crewai.project import CrewBase, agent, before_kickoff, crew, task
@CrewBase
class TestCrewClass:
agents_config = None
tasks_config = None
def __init__(self):
self.inputs_modified = False
self.received_inputs = None
@before_kickoff
def modify_inputs(self, inputs):
self.inputs_modified = True
inputs["modified"] = True
self.received_inputs = inputs
return inputs
@agent
def my_agent(self):
return Agent(
role="Test Agent",
goal="Test agent goal",
backstory="Test agent backstory",
)
@task
def my_task(self):
return Task(
description="Test task description",
expected_output="Test expected output",
agent=self.my_agent(),
)
@crew
def crew(self):
return Crew(agents=self.agents, tasks=self.tasks)
# Instantiate the class
test_crew_instance = TestCrewClass()
# Build the crew
crew = test_crew_instance.crew()
# Verify that the before_kickoff_callback is registered
assert len(crew.before_kickoff_callbacks) == 1
# Call kickoff without passing inputs
output = crew.kickoff()
# Check that the before_kickoff function was called
assert test_crew_instance.inputs_modified
# Verify that the inputs were initialized and modified inside the before_kickoff method
assert test_crew_instance.received_inputs is not None
assert test_crew_instance.received_inputs.get("modified") is True

15
tests/llm_test.py
View File

@@ -1,3 +1,5 @@
from time import sleep
import pytest
from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
@@ -5,24 +7,31 @@ from crewai.llm import LLM
from crewai.utilities.token_counter_callback import TokenCalcHandler
# TODO: This test fails without print statement, which makes me think that something is happening asynchronously that we need to eventually fix and dive deeper into at a later date
@pytest.mark.vcr(filter_headers=["authorization"])
def test_llm_callback_replacement():
llm = LLM(model="gpt-4o-mini")
llm1 = LLM(model="gpt-4o-mini")
llm2 = LLM(model="gpt-4o-mini")
calc_handler_1 = TokenCalcHandler(token_cost_process=TokenProcess())
calc_handler_2 = TokenCalcHandler(token_cost_process=TokenProcess())
llm.call(
result1 = llm1.call(
messages=[{"role": "user", "content": "Hello, world!"}],
callbacks=[calc_handler_1],
)
print("result1:", result1)
usage_metrics_1 = calc_handler_1.token_cost_process.get_summary()
print("usage_metrics_1:", usage_metrics_1)
llm.call(
result2 = llm2.call(
messages=[{"role": "user", "content": "Hello, world from another agent!"}],
callbacks=[calc_handler_2],
)
sleep(5)
print("result2:", result2)
usage_metrics_2 = calc_handler_2.token_cost_process.get_summary()
print("usage_metrics_2:", usage_metrics_2)
# The first handler should not have been updated
assert usage_metrics_1.successful_requests == 1

16
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