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Merge branch 'main' into main

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This commit is contained in:
PJ
2024-12-18 09:30:25 -05:00
committed by GitHub
parent 4d06dc262c 627b9f1abb
commit d1df81a001
115 changed files with 2262 additions and 12444 deletions
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2
src/crewai/__init__.py
View File

@@ -14,7 +14,7 @@ warnings.filterwarnings(
category=UserWarning,
module="pydantic.main",
)
__version__ = "0.85.0"
__version__ = "0.86.0"
__all__ = [
"Agent",
"Crew",

43
src/crewai/agent.py
View File

@@ -8,7 +8,7 @@ from pydantic import Field, InstanceOf, PrivateAttr, model_validator
from crewai.agents import CacheHandler
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.agents.crew_agent_executor import CrewAgentExecutor
from crewai.cli.constants import ENV_VARS
from crewai.cli.constants import ENV_VARS, LITELLM_PARAMS
from crewai.knowledge.knowledge import Knowledge
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
@@ -23,27 +23,19 @@ from crewai.utilities.converter import generate_model_description
from crewai.utilities.token_counter_callback import TokenCalcHandler
from crewai.utilities.training_handler import CrewTrainingHandler
agentops = None
def mock_agent_ops_provider():
def track_agent(*args, **kwargs):
try:
import agentops # type: ignore # Name "agentops" is already defined
from agentops import track_agent # type: ignore
except ImportError:
def track_agent():
def noop(f):
return f
return noop
return track_agent
agentops = None
if os.environ.get("AGENTOPS_API_KEY"):
try:
from agentops import track_agent
except ImportError:
track_agent = mock_agent_ops_provider()
else:
track_agent = mock_agent_ops_provider()
@track_agent()
class Agent(BaseAgent):
@@ -181,20 +173,11 @@ class Agent(BaseAgent):
if key_name and key_name not in unaccepted_attributes:
env_value = os.environ.get(key_name)
if env_value:
# Map key names containing "API_KEY" to "api_key"
key_name = (
"api_key" if "API_KEY" in key_name else key_name
)
# Map key names containing "API_BASE" to "api_base"
key_name = (
"api_base" if "API_BASE" in key_name else key_name
)
# Map key names containing "API_VERSION" to "api_version"
key_name = (
"api_version"
if "API_VERSION" in key_name
else key_name
)
key_name = key_name.lower()
for pattern in LITELLM_PARAMS:
if pattern in key_name:
key_name = pattern
break
llm_params[key_name] = env_value
# Check for default values if the environment variable is not set
elif env_var.get("default", False):

14
src/crewai/agents/agent_builder/base_agent_executor_mixin.py
View File

@@ -3,16 +3,15 @@ from typing import TYPE_CHECKING, Optional
from crewai.memory.entity.entity_memory_item import EntityMemoryItem
from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
from crewai.utilities import I18N
from crewai.utilities.converter import ConverterError
from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
from crewai.utilities import I18N
from crewai.utilities.printer import Printer
if TYPE_CHECKING:
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.crew import Crew
from crewai.task import Task
from crewai.agents.agent_builder.base_agent import BaseAgent
class CrewAgentExecutorMixin:
@@ -100,14 +99,19 @@ class CrewAgentExecutorMixin:
print(f"Failed to add to long term memory: {e}")
pass
def _ask_human_input(self, final_answer: dict) -> str:
def _ask_human_input(self, final_answer: str) -> str:
"""Prompt human input for final decision making."""
self._printer.print(
content=f"\033[1m\033[95m ## Final Result:\033[00m \033[92m{final_answer}\033[00m"
)
self._printer.print(
content="\n\n=====\n## Please provide feedback on the Final Result and the Agent's actions:",
content=(
"\n\n=====\n"
"## Please provide feedback on the Final Result and the Agent's actions. "
"Respond with 'looks good' or a similar phrase when you're satisfied.\n"
"=====\n"
),
color="bold_yellow",
)
return input()

127
src/crewai/agents/crew_agent_executor.py
View File

@@ -16,7 +16,7 @@ from crewai.agents.tools_handler import ToolsHandler
from crewai.tools.base_tool import BaseTool
from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
from crewai.utilities import I18N, Printer
from crewai.utilities.constants import TRAINING_DATA_FILE
from crewai.utilities.constants import MAX_LLM_RETRY, TRAINING_DATA_FILE
from crewai.utilities.exceptions.context_window_exceeding_exception import (
LLMContextLengthExceededException,
)
@@ -90,7 +90,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
if "system" in self.prompt:
system_prompt = self._format_prompt(self.prompt.get("system", ""), inputs)
user_prompt = self._format_prompt(self.prompt.get("user", ""), inputs)
self.messages.append(self._format_msg(system_prompt, role="system"))
self.messages.append(self._format_msg(user_prompt))
else:
@@ -103,17 +102,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
formatted_answer = self._invoke_loop()
if self.ask_for_human_input:
human_feedback = self._ask_human_input(formatted_answer.output)
if self.crew and self.crew._train:
self._handle_crew_training_output(formatted_answer, human_feedback)
formatted_answer = self._handle_human_feedback(formatted_answer)
# Making sure we only ask for it once, so disabling for the next thought loop
self.ask_for_human_input = False
self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
formatted_answer = self._invoke_loop()
if self.crew and self.crew._train:
self._handle_crew_training_output(formatted_answer)
self._create_short_term_memory(formatted_answer)
self._create_long_term_memory(formatted_answer)
return {"output": formatted_answer.output}
@@ -153,6 +143,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
tool_result = self._execute_tool_and_check_finality(
formatted_answer
)
if self.step_callback:
self.step_callback(tool_result)
formatted_answer.text += f"\nObservation: {tool_result.result}"
formatted_answer.result = tool_result.result
if tool_result.result_as_answer:
@@ -309,7 +302,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
self._i18n.slice("summarizer_system_message"), role="system"
),
self._format_msg(
self._i18n.slice("sumamrize_instruction").format(group=group),
self._i18n.slice("summarize_instruction").format(group=group),
),
],
callbacks=self.callbacks,
@@ -326,16 +319,14 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
def _handle_context_length(self) -> None:
if self.respect_context_window:
self._logger.log(
"debug",
"Context length exceeded. Summarizing content to fit the model context window.",
self._printer.print(
content="Context length exceeded. Summarizing content to fit the model context window.",
color="yellow",
)
self._summarize_messages()
else:
self._logger.log(
"debug",
"Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
self._printer.print(
content="Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
color="red",
)
raise SystemExit(
@@ -362,15 +353,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
] = result.output
training_handler.save(training_data)
else:
self._logger.log(
"error",
"Invalid train iteration type or agent_id not in training data.",
self._printer.print(
content="Invalid train iteration type or agent_id not in training data.",
color="red",
)
else:
self._logger.log(
"error",
"Crew is None or does not have _train_iteration attribute.",
self._printer.print(
content="Crew is None or does not have _train_iteration attribute.",
color="red",
)
@@ -388,15 +377,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
train_iteration, agent_id, training_data
)
else:
self._logger.log(
"error",
"Invalid train iteration type. Expected int.",
self._printer.print(
content="Invalid train iteration type. Expected int.",
color="red",
)
else:
self._logger.log(
"error",
"Crew is None or does not have _train_iteration attribute.",
self._printer.print(
content="Crew is None or does not have _train_iteration attribute.",
color="red",
)
@@ -412,3 +399,81 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
def _format_msg(self, prompt: str, role: str = "user") -> Dict[str, str]:
prompt = prompt.rstrip()
return {"role": role, "content": prompt}
def _handle_human_feedback(self, formatted_answer: AgentFinish) -> AgentFinish:
"""
Handles the human feedback loop, allowing the user to provide feedback
on the agent's output and determining if additional iterations are needed.
Parameters:
formatted_answer (AgentFinish): The initial output from the agent.
Returns:
AgentFinish: The final output after incorporating human feedback.
"""
while self.ask_for_human_input:
human_feedback = self._ask_human_input(formatted_answer.output)
if self.crew and self.crew._train:
self._handle_crew_training_output(formatted_answer, human_feedback)
# Make an LLM call to verify if additional changes are requested based on human feedback
additional_changes_prompt = self._i18n.slice(
"human_feedback_classification"
).format(feedback=human_feedback)
retry_count = 0
llm_call_successful = False
additional_changes_response = None
while retry_count < MAX_LLM_RETRY and not llm_call_successful:
try:
additional_changes_response = (
self.llm.call(
[
self._format_msg(
additional_changes_prompt, role="system"
)
],
callbacks=self.callbacks,
)
.strip()
.lower()
)
llm_call_successful = True
except Exception as e:
retry_count += 1
self._printer.print(
content=f"Error during LLM call to classify human feedback: {e}. Retrying... ({retry_count}/{MAX_LLM_RETRY})",
color="red",
)
if not llm_call_successful:
self._printer.print(
content="Error processing feedback after multiple attempts.",
color="red",
)
self.ask_for_human_input = False
break
if additional_changes_response == "false":
self.ask_for_human_input = False
elif additional_changes_response == "true":
self.ask_for_human_input = True
# Add human feedback to messages
self.messages.append(self._format_msg(f"Feedback: {human_feedback}"))
# Invoke the loop again with updated messages
formatted_answer = self._invoke_loop()
if self.crew and self.crew._train:
self._handle_crew_training_output(formatted_answer)
else:
# Unexpected response
self._printer.print(
content=f"Unexpected response from LLM: '{additional_changes_response}'. Assuming no additional changes requested.",
color="red",
)
self.ask_for_human_input = False
return formatted_answer

1
src/crewai/agents/parser.py
View File

@@ -1,5 +1,6 @@
import re
from typing import Any, Union
from json_repair import repair_json
from crewai.utilities import I18N

7
src/crewai/cli/authentication/main.py
View File

@@ -5,9 +5,10 @@ from typing import Any, Dict
import requests
from rich.console import Console
from crewai.cli.tools.main import ToolCommand
from .constants import AUTH0_AUDIENCE, AUTH0_CLIENT_ID, AUTH0_DOMAIN
from .utils import TokenManager, validate_token
from crewai.cli.tools.main import ToolCommand
console = Console()
@@ -79,7 +80,9 @@ class AuthenticationCommand:
style="yellow",
)
console.print("\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n")
console.print(
"\n[bold green]Welcome to CrewAI Enterprise![/bold green]\n"
)
return
if token_data["error"] not in ("authorization_pending", "slow_down"):

3
src/crewai/cli/authentication/token.py
View File

@@ -1,10 +1,9 @@
from .utils import TokenManager
def get_auth_token() -> str:
"""Get the authentication token."""
access_token = TokenManager().get_token()
if not access_token:
raise Exception()
return access_token

12
src/crewai/cli/cli.py
View File

@@ -1,7 +1,7 @@
from importlib.metadata import version as get_version
from typing import Optional
import click
import pkg_resources
from crewai.cli.add_crew_to_flow import add_crew_to_flow
from crewai.cli.create_crew import create_crew
@@ -25,6 +25,7 @@ from .update_crew import update_crew
@click.group()
@click.version_option(get_version("crewai"))
def crewai():
"""Top-level command group for crewai."""
@@ -50,14 +51,17 @@ def create(type, name, provider, skip_provider=False):
)
def version(tools):
"""Show the installed version of crewai."""
crewai_version = pkg_resources.get_distribution("crewai").version
try:
crewai_version = get_version("crewai")
except Exception:
crewai_version = "unknown version"
click.echo(f"crewai version: {crewai_version}")
if tools:
try:
tools_version = pkg_resources.get_distribution("crewai-tools").version
tools_version = get_version("crewai")
click.echo(f"crewai tools version: {tools_version}")
except pkg_resources.DistributionNotFound:
except Exception:
click.echo("crewai tools not installed")

3
src/crewai/cli/command.py
View File

@@ -1,8 +1,9 @@
import requests
from requests.exceptions import JSONDecodeError
from rich.console import Console
from crewai.cli.plus_api import PlusAPI
from crewai.cli.authentication.token import get_auth_token
from crewai.cli.plus_api import PlusAPI
from crewai.telemetry.telemetry import Telemetry
console = Console()

12
src/crewai/cli/config.py
View File

@@ -1,13 +1,19 @@
import json
from pathlib import Path
from pydantic import BaseModel, Field
from typing import Optional
from pydantic import BaseModel, Field
DEFAULT_CONFIG_PATH = Path.home() / ".config" / "crewai" / "settings.json"
class Settings(BaseModel):
tool_repository_username: Optional[str] = Field(None, description="Username for interacting with the Tool Repository")
tool_repository_password: Optional[str] = Field(None, description="Password for interacting with the Tool Repository")
tool_repository_username: Optional[str] = Field(
None, description="Username for interacting with the Tool Repository"
)
tool_repository_password: Optional[str] = Field(
None, description="Password for interacting with the Tool Repository"
)
config_path: Path = Field(default=DEFAULT_CONFIG_PATH, exclude=True)
def __init__(self, config_path: Path = DEFAULT_CONFIG_PATH, **data):

3
src/crewai/cli/constants.py
View File

@@ -159,3 +159,6 @@ MODELS = {
}
JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"
LITELLM_PARAMS = ["api_key", "api_base", "api_version"]

8
src/crewai/cli/plus_api.py
View File

@@ -1,9 +1,11 @@
from typing import Optional
import requests
from os import getenv
from crewai.cli.version import get_crewai_version
from typing import Optional
from urllib.parse import urljoin
import requests
from crewai.cli.version import get_crewai_version
class PlusAPI:
"""

3
src/crewai/cli/reset_memories_command.py
View File

@@ -1,11 +1,12 @@
import subprocess
import click
from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
from crewai.memory.entity.entity_memory import EntityMemory
from crewai.memory.long_term.long_term_memory import LongTermMemory
from crewai.memory.short_term.short_term_memory import ShortTermMemory
from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
def reset_memories_command(

2
src/crewai/cli/templates/crew/README.md
View File

@@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co
## Installation
Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
Ensure you have Python >=3.10 <3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
First, if you haven't already, install uv:

2
src/crewai/cli/templates/crew/config/tasks.yaml
View File

@@ -12,6 +12,6 @@ reporting_task:
Review the context you got and expand each topic into a full section for a report.
Make sure the report is detailed and contains any and all relevant information.
expected_output: >
A fully fledge reports with the mains topics, each with a full section of information.
A fully fledged report with the main topics, each with a full section of information.
Formatted as markdown without '```'
agent: reporting_analyst

43
src/crewai/cli/templates/crew/crew.py
View File

@@ -1,37 +1,26 @@
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
# Uncomment the following line to use an example of a custom tool
# from {{folder_name}}.tools.custom_tool import MyCustomTool
# Uncomment the following line to use an example of a knowledge source
# from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
from crewai.project import CrewBase, agent, crew, task
# Check our tools documentations for more information on how to use them
# from crewai_tools import SerperDevTool
# If you want to run a snippet of code before or after the crew starts,
# you can use the @before_kickoff and @after_kickoff decorators
# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
@CrewBase
class {{crew_name}}():
"""{{crew_name}} crew"""
# Learn more about YAML configuration files here:
# Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
# Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
agents_config = 'config/agents.yaml'
tasks_config = 'config/tasks.yaml'
@before_kickoff # Optional hook to be executed before the crew starts
def pull_data_example(self, inputs):
# Example of pulling data from an external API, dynamically changing the inputs
inputs['extra_data'] = "This is extra data"
return inputs
@after_kickoff # Optional hook to be executed after the crew has finished
def log_results(self, output):
# Example of logging results, dynamically changing the output
print(f"Results: {output}")
return output
# If you would like to add tools to your agents, you can learn more about it here:
# https://docs.crewai.com/concepts/agents#agent-tools
@agent
def researcher(self) -> Agent:
return Agent(
config=self.agents_config['researcher'],
# tools=[MyCustomTool()], # Example of custom tool, loaded on the beginning of file
verbose=True
)
@@ -42,6 +31,9 @@ class {{crew_name}}():
verbose=True
)
# To learn more about structured task outputs,
# task dependencies, and task callbacks, check out the documentation:
# https://docs.crewai.com/concepts/tasks#overview-of-a-task
@task
def research_task(self) -> Task:
return Task(
@@ -58,14 +50,8 @@ class {{crew_name}}():
@crew
def crew(self) -> Crew:
"""Creates the {{crew_name}} crew"""
# You can add knowledge sources here
# knowledge_path = "user_preference.txt"
# sources = [
# TextFileKnowledgeSource(
# file_path="knowledge/user_preference.txt",
# metadata={"preference": "personal"}
# ),
# ]
# To learn how to add knowledge sources to your crew, check out the documentation:
# https://docs.crewai.com/concepts/knowledge#what-is-knowledge
return Crew(
agents=self.agents, # Automatically created by the @agent decorator
@@ -73,5 +59,4 @@ class {{crew_name}}():
process=Process.sequential,
verbose=True,
# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
# knowledge_sources=sources, # In the case you want to add knowledge sources
)

4
src/crewai/cli/templates/crew/pyproject.toml
View File

@@ -3,9 +3,9 @@ name = "{{folder_name}}"
version = "0.1.0"
description = "{{name}} using crewAI"
authors = [{ name = "Your Name", email = "you@example.com" }]
requires-python = ">=3.10,<=3.13"
requires-python = ">=3.10,<3.13"
dependencies = [
"crewai[tools]>=0.85.0,<1.0.0"
"crewai[tools]>=0.86.0,<1.0.0"
]
[project.scripts]

2
src/crewai/cli/templates/crew/tools/custom_tool.py
View File

@@ -10,7 +10,7 @@ class MyCustomToolInput(BaseModel):
class MyCustomTool(BaseTool):
name: str = "Name of my tool"
description: str = (
"Clear description for what this tool is useful for, you agent will need this information to use it."
"Clear description for what this tool is useful for, your agent will need this information to use it."
)
args_schema: Type[BaseModel] = MyCustomToolInput

2
src/crewai/cli/templates/flow/README.md
View File

@@ -4,7 +4,7 @@ Welcome to the {{crew_name}} Crew project, powered by [crewAI](https://crewai.co
## Installation
Ensure you have Python >=3.10 <=3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
Ensure you have Python >=3.10 <3.13 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management and package handling, offering a seamless setup and execution experience.
First, if you haven't already, install uv:

62
src/crewai/cli/templates/flow/crews/poem_crew/poem_crew.py
View File

@@ -1,31 +1,47 @@
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
# If you want to run a snippet of code before or after the crew starts,
# you can use the @before_kickoff and @after_kickoff decorators
# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
@CrewBase
class PoemCrew():
"""Poem Crew"""
class PoemCrew:
"""Poem Crew"""
agents_config = 'config/agents.yaml'
tasks_config = 'config/tasks.yaml'
# Learn more about YAML configuration files here:
# Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
# Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
agents_config = "config/agents.yaml"
tasks_config = "config/tasks.yaml"
@agent
def poem_writer(self) -> Agent:
return Agent(
config=self.agents_config['poem_writer'],
)
# If you would lik to add tools to your crew, you can learn more about it here:
# https://docs.crewai.com/concepts/agents#agent-tools
@agent
def poem_writer(self) -> Agent:
return Agent(
config=self.agents_config["poem_writer"],
)
@task
def write_poem(self) -> Task:
return Task(
config=self.tasks_config['write_poem'],
)
# To learn more about structured task outputs,
# task dependencies, and task callbacks, check out the documentation:
# https://docs.crewai.com/concepts/tasks#overview-of-a-task
@task
def write_poem(self) -> Task:
return Task(
config=self.tasks_config["write_poem"],
)
@crew
def crew(self) -> Crew:
"""Creates the Research Crew"""
return Crew(
agents=self.agents, # Automatically created by the @agent decorator
tasks=self.tasks, # Automatically created by the @task decorator
process=Process.sequential,
verbose=True,
)
@crew
def crew(self) -> Crew:
"""Creates the Research Crew"""
# To learn how to add knowledge sources to your crew, check out the documentation:
# https://docs.crewai.com/concepts/knowledge#what-is-knowledge
return Crew(
agents=self.agents, # Automatically created by the @agent decorator
tasks=self.tasks, # Automatically created by the @task decorator
process=Process.sequential,
verbose=True,
)

2
src/crewai/cli/templates/flow/main.py
View File

@@ -5,7 +5,7 @@ from pydantic import BaseModel
from crewai.flow.flow import Flow, listen, start
from .crews.poem_crew.poem_crew import PoemCrew
from {{folder_name}}.crews.poem_crew.poem_crew import PoemCrew
class PoemState(BaseModel):

4
src/crewai/cli/templates/flow/pyproject.toml
View File

@@ -3,9 +3,9 @@ name = "{{folder_name}}"
version = "0.1.0"
description = "{{name}} using crewAI"
authors = [{ name = "Your Name", email = "you@example.com" }]
requires-python = ">=3.10,<=3.13"
requires-python = ">=3.10,<3.13"
dependencies = [
"crewai[tools]>=0.85.0,<1.0.0",
"crewai[tools]>=0.86.0,<1.0.0",
]
[project.scripts]

2
src/crewai/cli/templates/flow/tools/custom_tool.py
View File

@@ -13,7 +13,7 @@ class MyCustomToolInput(BaseModel):
class MyCustomTool(BaseTool):
name: str = "Name of my tool"
description: str = (
"Clear description for what this tool is useful for, you agent will need this information to use it."
"Clear description for what this tool is useful for, your agent will need this information to use it."
)
args_schema: Type[BaseModel] = MyCustomToolInput

2
src/crewai/cli/templates/tool/README.md
View File

@@ -5,7 +5,7 @@ custom tools to power up your crews.
## Installing
Ensure you have Python >=3.10 <=3.13 installed on your system. This project
Ensure you have Python >=3.10 <3.13 installed on your system. This project
uses [UV](https://docs.astral.sh/uv/) for dependency management and package
handling, offering a seamless setup and execution experience.

4
src/crewai/cli/templates/tool/pyproject.toml
View File

@@ -3,8 +3,8 @@ name = "{{folder_name}}"
version = "0.1.0"
description = "Power up your crews with {{folder_name}}"
readme = "README.md"
requires-python = ">=3.10,<=3.13"
requires-python = ">=3.10,<3.13"
dependencies = [
"crewai[tools]>=0.85.0"
"crewai[tools]>=0.86.0"
]

4
src/crewai/cli/tools/main.py
View File

@@ -117,7 +117,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
published_handle = publish_response.json()["handle"]
console.print(
f"Succesfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
f"Successfully published {published_handle} ({project_version}).\nInstall it in other projects with crewai tool install {published_handle}",
style="bold green",
)
@@ -138,7 +138,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
self._add_package(get_response.json())
console.print(f"Succesfully installed {handle}", style="bold green")
console.print(f"Successfully installed {handle}", style="bold green")
def login(self):
login_response = self.plus_api_client.login_to_tool_repository()

22
src/crewai/cli/utils.py
View File

@@ -33,26 +33,6 @@ def copy_template(src, dst, name, class_name, folder_name):
click.secho(f" - Created {dst}", fg="green")
# Drop the simple_toml_parser when we move to python3.11
def simple_toml_parser(content):
result = {}
current_section = result
for line in content.split("\n"):
line = line.strip()
if line.startswith("[") and line.endswith("]"):
# New section
section = line[1:-1].split(".")
current_section = result
for key in section:
current_section = current_section.setdefault(key, {})
elif "=" in line:
key, value = line.split("=", 1)
key = key.strip()
value = value.strip().strip('"')
current_section[key] = value
return result
def read_toml(file_path: str = "pyproject.toml"):
"""Read the content of a TOML file and return it as a dictionary."""
with open(file_path, "rb") as f:
@@ -63,7 +43,7 @@ def read_toml(file_path: str = "pyproject.toml"):
def parse_toml(content):
if sys.version_info >= (3, 11):
return tomllib.loads(content)
return simple_toml_parser(content)
return tomli.loads(content)
def get_project_name(

2
src/crewai/cli/version.py
View File

@@ -1,6 +1,6 @@
import importlib.metadata
def get_crewai_version() -> str:
"""Get the version number of CrewAI running the CLI"""
return importlib.metadata.version("crewai")

12
src/crewai/crew.py
View File

@@ -1,6 +1,5 @@
import asyncio
import json
import os
import uuid
import warnings
from concurrent.futures import Future
@@ -49,12 +48,10 @@ from crewai.utilities.planning_handler import CrewPlanner
from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
from crewai.utilities.training_handler import CrewTrainingHandler
agentops = None
if os.environ.get("AGENTOPS_API_KEY"):
try:
import agentops # type: ignore
except ImportError:
pass
try:
import agentops # type: ignore
except ImportError:
agentops = None
warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
@@ -1032,6 +1029,7 @@ class Crew(BaseModel):
agentops.end_session(
end_state="Success",
end_state_reason="Finished Execution",
is_auto_end=True,
)
self._telemetry.end_crew(self, final_string_output)

55
src/crewai/flow/flow.py
View File

@@ -14,8 +14,15 @@ from typing import (
cast,
)
from blinker import Signal
from pydantic import BaseModel, ValidationError
from crewai.flow.flow_events import (
FlowFinishedEvent,
FlowStartedEvent,
MethodExecutionFinishedEvent,
MethodExecutionStartedEvent,
)
from crewai.flow.flow_visualizer import plot_flow
from crewai.flow.utils import get_possible_return_constants
from crewai.telemetry import Telemetry
@@ -159,6 +166,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
_routers: Dict[str, str] = {}
_router_paths: Dict[str, List[str]] = {}
initial_state: Union[Type[T], T, None] = None
event_emitter = Signal("event_emitter")
def __class_getitem__(cls: Type["Flow"], item: Type[T]) -> Type["Flow"]:
class _FlowGeneric(cls): # type: ignore
@@ -253,6 +261,14 @@ class Flow(Generic[T], metaclass=FlowMeta):
Returns:
The final output from the flow execution.
"""
self.event_emitter.send(
self,
event=FlowStartedEvent(
type="flow_started",
flow_name=self.__class__.__name__,
),
)
if inputs is not None:
self._initialize_state(inputs)
return asyncio.run(self.kickoff_async())
@@ -267,8 +283,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
Returns:
The final output from the flow execution.
"""
if inputs is not None:
self._initialize_state(inputs)
if not self._start_methods:
raise ValueError("No start method defined")
@@ -285,11 +299,19 @@ class Flow(Generic[T], metaclass=FlowMeta):
# Run all start methods concurrently
await asyncio.gather(*tasks)
# Return the final output (from the last executed method)
if self._method_outputs:
return self._method_outputs[-1]
else:
return None # Or raise an exception if no methods were executed
# Determine the final output (from the last executed method)
final_output = self._method_outputs[-1] if self._method_outputs else None
self.event_emitter.send(
self,
event=FlowFinishedEvent(
type="flow_finished",
flow_name=self.__class__.__name__,
result=final_output,
),
)
return final_output
async def _execute_start_method(self, start_method_name: str) -> None:
result = await self._execute_method(
@@ -352,6 +374,16 @@ class Flow(Generic[T], metaclass=FlowMeta):
async def _execute_single_listener(self, listener_name: str, result: Any) -> None:
try:
method = self._methods[listener_name]
self.event_emitter.send(
self,
event=MethodExecutionStartedEvent(
type="method_execution_started",
method_name=listener_name,
flow_name=self.__class__.__name__,
),
)
sig = inspect.signature(method)
params = list(sig.parameters.values())
@@ -367,6 +399,15 @@ class Flow(Generic[T], metaclass=FlowMeta):
# If listener does not expect parameters, call without arguments
listener_result = await self._execute_method(listener_name, method)
self.event_emitter.send(
self,
event=MethodExecutionFinishedEvent(
type="method_execution_finished",
method_name=listener_name,
flow_name=self.__class__.__name__,
),
)
# Execute listeners of this listener
await self._execute_listeners(listener_name, listener_result)
except Exception as e:

33
src/crewai/flow/flow_events.py Normal file
View File

@@ -0,0 +1,33 @@
from dataclasses import dataclass, field
from datetime import datetime
from typing import Any, Optional
@dataclass
class Event:
type: str
flow_name: str
timestamp: datetime = field(init=False)
def __post_init__(self):
self.timestamp = datetime.now()
@dataclass
class FlowStartedEvent(Event):
pass
@dataclass
class MethodExecutionStartedEvent(Event):
method_name: str
@dataclass
class MethodExecutionFinishedEvent(Event):
method_name: str
@dataclass
class FlowFinishedEvent(Event):
result: Optional[Any] = None

9
src/crewai/knowledge/knowledge.py
View File

@@ -1,11 +1,10 @@
import os
from typing import Any, Dict, List, Optional
from typing import List, Optional, Dict, Any
from pydantic import BaseModel, ConfigDict, Field
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
from crewai.utilities.constants import DEFAULT_SCORE_THRESHOLD
os.environ["TOKENIZERS_PARALLELISM"] = "false" # removes logging from fastembed
@@ -46,9 +45,7 @@ class Knowledge(BaseModel):
source.storage = self.storage
source.add()
def query(
self, query: List[str], limit: int = 3, preference: Optional[str] = None
) -> List[Dict[str, Any]]:
def query(self, query: List[str], limit: int = 3) -> List[Dict[str, Any]]:
"""
Query across all knowledge sources to find the most relevant information.
Returns the top_k most relevant chunks.
@@ -57,8 +54,6 @@ class Knowledge(BaseModel):
results = self.storage.search(
query,
limit,
filter={"preference": preference} if preference else None,
score_threshold=DEFAULT_SCORE_THRESHOLD,
)
return results

11
src/crewai/knowledge/source/base_file_knowledge_source.py
View File

@@ -1,13 +1,13 @@
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Union, List, Dict, Any
from typing import Dict, List, Union
from pydantic import Field
from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
from crewai.utilities.logger import Logger
from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
from crewai.utilities.logger import Logger
class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
@@ -38,7 +38,7 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
if not path.exists():
self._logger.log(
"error",
f"File not found: {path}. Try adding sources to the knowledge directory. If its inside the knowledge directory, use the relative path.",
f"File not found: {path}. Try adding sources to the knowledge directory. If it's inside the knowledge directory, use the relative path.",
color="red",
)
raise FileNotFoundError(f"File not found: {path}")
@@ -49,10 +49,9 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
color="red",
)
def save_documents(self, metadata: Dict[str, Any]):
def _save_documents(self):
"""Save the documents to the storage."""
chunk_metadatas = [metadata.copy() for _ in self.chunks]
self.storage.save(self.chunks, chunk_metadatas)
self.storage.save(self.chunks)
def convert_to_path(self, path: Union[Path, str]) -> Path:
"""Convert a path to a Path object."""

8
src/crewai/knowledge/source/base_knowledge_source.py
View File

@@ -1,5 +1,5 @@
from abc import ABC, abstractmethod
from typing import List, Dict, Any, Optional
from typing import Any, Dict, List, Optional
import numpy as np
from pydantic import BaseModel, ConfigDict, Field
@@ -17,7 +17,7 @@ class BaseKnowledgeSource(BaseModel, ABC):
model_config = ConfigDict(arbitrary_types_allowed=True)
storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
metadata: Dict[str, Any] = Field(default_factory=dict)
metadata: Dict[str, Any] = Field(default_factory=dict) # Currently unused
collection_name: Optional[str] = Field(default=None)
@abstractmethod
@@ -41,9 +41,9 @@ class BaseKnowledgeSource(BaseModel, ABC):
for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
]
def save_documents(self, metadata: Dict[str, Any]):
def _save_documents(self):
"""
Save the documents to the storage.
This method should be called after the chunks and embeddings are generated.
"""
self.storage.save(self.chunks, metadata)
self.storage.save(self.chunks)

4
src/crewai/knowledge/source/csv_knowledge_source.py
View File

@@ -1,6 +1,6 @@
import csv
from typing import Dict, List
from pathlib import Path
from typing import Dict, List
from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
@@ -30,7 +30,7 @@ class CSVKnowledgeSource(BaseFileKnowledgeSource):
)
new_chunks = self._chunk_text(content_str)
self.chunks.extend(new_chunks)
self.save_documents(metadata=self.metadata)
self._save_documents()
def _chunk_text(self, text: str) -> List[str]:
"""Utility method to split text into chunks."""

5
src/crewai/knowledge/source/excel_knowledge_source.py
View File

@@ -1,5 +1,6 @@
from typing import Dict, List
from pathlib import Path
from typing import Dict, List
from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
@@ -44,7 +45,7 @@ class ExcelKnowledgeSource(BaseFileKnowledgeSource):
new_chunks = self._chunk_text(content_str)
self.chunks.extend(new_chunks)
self.save_documents(metadata=self.metadata)
self._save_documents()
def _chunk_text(self, text: str) -> List[str]:
"""Utility method to split text into chunks."""

4
src/crewai/knowledge/source/json_knowledge_source.py
View File

@@ -1,6 +1,6 @@
import json
from typing import Any, Dict, List
from pathlib import Path
from typing import Any, Dict, List
from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
@@ -42,7 +42,7 @@ class JSONKnowledgeSource(BaseFileKnowledgeSource):
)
new_chunks = self._chunk_text(content_str)
self.chunks.extend(new_chunks)
self.save_documents(metadata=self.metadata)
self._save_documents()
def _chunk_text(self, text: str) -> List[str]:
"""Utility method to split text into chunks."""

4
src/crewai/knowledge/source/pdf_knowledge_source.py
View File

@@ -1,5 +1,5 @@
from typing import List, Dict
from pathlib import Path
from typing import Dict, List
from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
@@ -43,7 +43,7 @@ class PDFKnowledgeSource(BaseFileKnowledgeSource):
for _, text in self.content.items():
new_chunks = self._chunk_text(text)
self.chunks.extend(new_chunks)
self.save_documents(metadata=self.metadata)
self._save_documents()
def _chunk_text(self, text: str) -> List[str]:
"""Utility method to split text into chunks."""

2
src/crewai/knowledge/source/string_knowledge_source.py
View File

@@ -24,7 +24,7 @@ class StringKnowledgeSource(BaseKnowledgeSource):
"""Add string content to the knowledge source, chunk it, compute embeddings, and save them."""
new_chunks = self._chunk_text(self.content)
self.chunks.extend(new_chunks)
self.save_documents(metadata=self.metadata)
self._save_documents()
def _chunk_text(self, text: str) -> List[str]:
"""Utility method to split text into chunks."""

4
src/crewai/knowledge/source/text_file_knowledge_source.py
View File

@@ -1,5 +1,5 @@
from typing import Dict, List
from pathlib import Path
from typing import Dict, List
from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
@@ -24,7 +24,7 @@ class TextFileKnowledgeSource(BaseFileKnowledgeSource):
for _, text in self.content.items():
new_chunks = self._chunk_text(text)
self.chunks.extend(new_chunks)
self.save_documents(metadata=self.metadata)
self._save_documents()
def _chunk_text(self, text: str) -> List[str]:
"""Utility method to split text into chunks."""

2
src/crewai/knowledge/storage/base_knowledge_storage.py
View File

@@ -1,5 +1,5 @@
from abc import ABC, abstractmethod
from typing import Dict, Any, List, Optional
from typing import Any, Dict, List, Optional
class BaseKnowledgeStorage(ABC):

114
src/crewai/knowledge/storage/knowledge_storage.py
View File

@@ -1,18 +1,22 @@
import contextlib
import hashlib
import io
import logging
import chromadb
import os
import shutil
from typing import Any, Dict, List, Optional, Union, cast
import chromadb
import chromadb.errors
from crewai.utilities.paths import db_storage_path
from typing import Optional, List, Dict, Any, Union
from crewai.utilities import EmbeddingConfigurator
from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
import hashlib
from chromadb.config import Settings
from chromadb.api import ClientAPI
from chromadb.api.types import OneOrMany
from chromadb.config import Settings
from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
from crewai.utilities import EmbeddingConfigurator
from crewai.utilities.constants import KNOWLEDGE_DIRECTORY
from crewai.utilities.logger import Logger
from crewai.utilities.paths import db_storage_path
@contextlib.contextmanager
@@ -103,53 +107,77 @@ class KnowledgeStorage(BaseKnowledgeStorage):
raise Exception("Failed to create or get collection")
def reset(self):
if self.app:
self.app.reset()
else:
base_path = os.path.join(db_storage_path(), "knowledge")
base_path = os.path.join(db_storage_path(), KNOWLEDGE_DIRECTORY)
if not self.app:
self.app = chromadb.PersistentClient(
path=base_path,
settings=Settings(allow_reset=True),
)
self.app.reset()
self.app.reset()
shutil.rmtree(base_path)
self.app = None
self.collection = None
def save(
self,
documents: List[str],
metadata: Union[Dict[str, Any], List[Dict[str, Any]]],
metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
):
if self.collection:
try:
metadatas = [metadata] if isinstance(metadata, dict) else metadata
ids = [
hashlib.sha256(doc.encode("utf-8")).hexdigest() for doc in documents
]
self.collection.upsert(
documents=documents,
metadatas=metadatas,
ids=ids,
)
except chromadb.errors.InvalidDimensionException as e:
Logger(verbose=True).log(
"error",
"Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
"red",
)
raise ValueError(
"Embedding dimension mismatch. Make sure you're using the same embedding model "
"across all operations with this collection."
"Try resetting the collection using `crewai reset-memories -a`"
) from e
except Exception as e:
Logger(verbose=True).log(
"error", f"Failed to upsert documents: {e}", "red"
)
raise
else:
if not self.collection:
raise Exception("Collection not initialized")
try:
# Create a dictionary to store unique documents
unique_docs = {}
# Generate IDs and create a mapping of id -> (document, metadata)
for idx, doc in enumerate(documents):
doc_id = hashlib.sha256(doc.encode("utf-8")).hexdigest()
doc_metadata = None
if metadata is not None:
if isinstance(metadata, list):
doc_metadata = metadata[idx]
else:
doc_metadata = metadata
unique_docs[doc_id] = (doc, doc_metadata)
# Prepare filtered lists for ChromaDB
filtered_docs = []
filtered_metadata = []
filtered_ids = []
# Build the filtered lists
for doc_id, (doc, meta) in unique_docs.items():
filtered_docs.append(doc)
filtered_metadata.append(meta)
filtered_ids.append(doc_id)
# If we have no metadata at all, set it to None
final_metadata: Optional[OneOrMany[chromadb.Metadata]] = (
None if all(m is None for m in filtered_metadata) else filtered_metadata
)
self.collection.upsert(
documents=filtered_docs,
metadatas=final_metadata,
ids=filtered_ids,
)
except chromadb.errors.InvalidDimensionException as e:
Logger(verbose=True).log(
"error",
"Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
"red",
)
raise ValueError(
"Embedding dimension mismatch. Make sure you're using the same embedding model "
"across all operations with this collection."
"Try resetting the collection using `crewai reset-memories -a`"
) from e
except Exception as e:
Logger(verbose=True).log("error", f"Failed to upsert documents: {e}", "red")
raise
def _create_default_embedding_function(self):
from chromadb.utils.embedding_functions.openai_embedding_function import (
OpenAIEmbeddingFunction,

58
src/crewai/llm.py
View File

@@ -1,4 +1,5 @@
import logging
import os
import sys
import threading
import warnings
@@ -42,6 +43,11 @@ LLM_CONTEXT_WINDOW_SIZES = {
"gpt-4-turbo": 128000,
"o1-preview": 128000,
"o1-mini": 128000,
# gemini
"gemini-2.0-flash": 1048576,
"gemini-1.5-pro": 2097152,
"gemini-1.5-flash": 1048576,
"gemini-1.5-flash-8b": 1048576,
# deepseek
"deepseek-chat": 128000,
# groq
@@ -60,6 +66,9 @@ LLM_CONTEXT_WINDOW_SIZES = {
"mixtral-8x7b-32768": 32768,
}
DEFAULT_CONTEXT_WINDOW_SIZE = 8192
CONTEXT_WINDOW_USAGE_RATIO = 0.75
@contextmanager
def suppress_warnings():
@@ -123,11 +132,13 @@ class LLM:
self.api_version = api_version
self.api_key = api_key
self.callbacks = callbacks
self.context_window_size = 0
self.kwargs = kwargs
litellm.drop_params = True
litellm.set_verbose = False
self.set_callbacks(callbacks)
self.set_env_callbacks()
def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
with suppress_warnings():
@@ -189,7 +200,16 @@ class LLM:
def get_context_window_size(self) -> int:
# Only using 75% of the context window size to avoid cutting the message in the middle
return int(LLM_CONTEXT_WINDOW_SIZES.get(self.model, 8192) * 0.75)
if self.context_window_size != 0:
return self.context_window_size
self.context_window_size = int(
DEFAULT_CONTEXT_WINDOW_SIZE * CONTEXT_WINDOW_USAGE_RATIO
)
for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
if self.model.startswith(key):
self.context_window_size = int(value * CONTEXT_WINDOW_USAGE_RATIO)
return self.context_window_size
def set_callbacks(self, callbacks: List[Any]):
callback_types = [type(callback) for callback in callbacks]
@@ -202,3 +222,39 @@ class LLM:
litellm._async_success_callback.remove(callback)
litellm.callbacks = callbacks
def set_env_callbacks(self):
"""
Sets the success and failure callbacks for the LiteLLM library from environment variables.
This method reads the `LITELLM_SUCCESS_CALLBACKS` and `LITELLM_FAILURE_CALLBACKS`
environment variables, which should contain comma-separated lists of callback names.
It then assigns these lists to `litellm.success_callback` and `litellm.failure_callback`,
respectively.
If the environment variables are not set or are empty, the corresponding callback lists
will be set to empty lists.
Example:
LITELLM_SUCCESS_CALLBACKS="langfuse,langsmith"
LITELLM_FAILURE_CALLBACKS="langfuse"
This will set `litellm.success_callback` to ["langfuse", "langsmith"] and
`litellm.failure_callback` to ["langfuse"].
"""
success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
success_callbacks = []
if success_callbacks_str:
success_callbacks = [
callback.strip() for callback in success_callbacks_str.split(",")
]
failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
failure_callbacks = []
if failure_callbacks_str:
failure_callbacks = [
callback.strip() for callback in failure_callbacks_str.split(",")
]
litellm.success_callback = success_callbacks
litellm.failure_callback = failure_callbacks

2
src/crewai/memory/contextual/contextual_memory.py
View File

@@ -1,4 +1,4 @@
from typing import Optional, Dict, Any
from typing import Any, Dict, Optional
from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory

2
src/crewai/memory/memory.py
View File

@@ -1,4 +1,4 @@
from typing import Any, Dict, Optional, List
from typing import Any, Dict, List, Optional
from crewai.memory.storage.rag_storage import RAGStorage

6
src/crewai/memory/short_term/short_term_memory.py
View File

@@ -1,4 +1,5 @@
from typing import Any, Dict, Optional
from crewai.memory.memory import Memory
from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
from crewai.memory.storage.rag_storage import RAGStorage
@@ -32,7 +33,10 @@ class ShortTermMemory(Memory):
storage
if storage
else RAGStorage(
type="short_term", embedder_config=embedder_config, crew=crew, path=path
type="short_term",
embedder_config=embedder_config,
crew=crew,
path=path,
)
)
super().__init__(storage)

1
src/crewai/memory/storage/mem0_storage.py
View File

@@ -2,6 +2,7 @@ import os
from typing import Any, Dict, List
from mem0 import MemoryClient
from crewai.memory.storage.interface import Storage

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

@@ -4,12 +4,14 @@ import logging
import os
import shutil
import uuid
from typing import Any, Dict, List, Optional
from chromadb.api import ClientAPI
from crewai.memory.storage.base_rag_storage import BaseRAGStorage
from crewai.utilities.paths import db_storage_path
from crewai.utilities import EmbeddingConfigurator
from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
from crewai.utilities.paths import db_storage_path
@contextlib.contextmanager
@@ -37,12 +39,15 @@ class RAGStorage(BaseRAGStorage):
app: ClientAPI | None = None
def __init__(self, type, allow_reset=True, embedder_config=None, crew=None, path=None):
def __init__(
self, type, allow_reset=True, embedder_config=None, crew=None, path=None
):
super().__init__(type, allow_reset, embedder_config, crew)
agents = crew.agents if crew else []
agents = [self._sanitize_role(agent.role) for agent in agents]
agents = "_".join(agents)
self.agents = agents
self.storage_file_name = self._build_storage_file_name(type, agents)
self.type = type
@@ -60,7 +65,7 @@ class RAGStorage(BaseRAGStorage):
self._set_embedder_config()
chroma_client = chromadb.PersistentClient(
path=self.path if self.path else f"{db_storage_path()}/{self.type}/{self.agents}",
path=self.path if self.path else self.storage_file_name,
settings=Settings(allow_reset=self.allow_reset),
)
@@ -81,6 +86,20 @@ class RAGStorage(BaseRAGStorage):
"""
return role.replace("\n", "").replace(" ", "_").replace("/", "_")
def _build_storage_file_name(self, type: str, file_name: str) -> str:
"""
Ensures file name does not exceed max allowed by OS
"""
base_path = f"{db_storage_path()}/{type}"
if len(file_name) > MAX_FILE_NAME_LENGTH:
logging.warning(
f"Trimming file name from {len(file_name)} to {MAX_FILE_NAME_LENGTH} characters."
)
file_name = file_name[:MAX_FILE_NAME_LENGTH]
return f"{base_path}/{file_name}"
def save(self, value: Any, metadata: Dict[str, Any]) -> None:
if not hasattr(self, "app") or not hasattr(self, "collection"):
self._initialize_app()
@@ -131,9 +150,11 @@ class RAGStorage(BaseRAGStorage):
def reset(self) -> None:
try:
shutil.rmtree(f"{db_storage_path()}/{self.type}")
if self.app:
self.app.reset()
shutil.rmtree(f"{db_storage_path()}/{self.type}")
self.app = None
self.collection = None
except Exception as e:
if "attempt to write a readonly database" in str(e):
# Ignore this specific error

2
src/crewai/memory/user/user_memory.py
View File

@@ -37,7 +37,7 @@ class UserMemory(Memory):
limit: int = 3,
score_threshold: float = 0.35,
):
results = super().search(
results = self.storage.search(
query=query,
limit=limit,
score_threshold=score_threshold,

2
src/crewai/project/annotations.py
View File

@@ -66,6 +66,8 @@ def cache_handler(func):
def crew(func) -> Callable[..., Crew]:
@wraps(func)
def wrapper(self, *args, **kwargs) -> Crew:
instantiated_tasks = []
instantiated_agents = []

4
src/crewai/project/crew_base.py
View File

@@ -213,4 +213,8 @@ def CrewBase(cls: T) -> T:
callback_functions[callback]() for callback in callbacks
]
# Include base class (qual)name in the wrapper class (qual)name.
WrappedClass.__name__ = CrewBase.__name__ + "(" + cls.__name__ + ")"
WrappedClass.__qualname__ = CrewBase.__qualname__ + "(" + cls.__name__ + ")"
return cast(T, WrappedClass)

5
src/crewai/project/utils.py
View File

@@ -1,11 +1,14 @@
from functools import wraps
def memoize(func):
cache = {}
@wraps(func)
def memoized_func(*args, **kwargs):
key = (args, tuple(kwargs.items()))
if key not in cache:
cache[key] = func(*args, **kwargs)
return cache[key]
memoized_func.__dict__.update(func.__dict__)
return memoized_func

11
src/crewai/task.py
View File

@@ -1,11 +1,11 @@
import datetime
import json
import os
import threading
import uuid
from concurrent.futures import Future
from copy import copy
from hashlib import md5
from pathlib import Path
from typing import Any, Dict, List, Optional, Set, Tuple, Type, Union
from opentelemetry.trace import Span
@@ -393,12 +393,13 @@ class Task(BaseModel):
if self.output_file is None:
raise ValueError("output_file is not set.")
directory = os.path.dirname(self.output_file) # type: ignore # Value of type variable "AnyOrLiteralStr" of "dirname" cannot be "str | None"
resolved_path = Path(self.output_file).expanduser().resolve()
directory = resolved_path.parent
if directory and not os.path.exists(directory):
os.makedirs(directory)
if not directory.exists():
directory.mkdir(parents=True, exist_ok=True)
with open(self.output_file, "w", encoding="utf-8") as file:
with resolved_path.open("w", encoding="utf-8") as file:
if isinstance(result, dict):
import json

25
src/crewai/telemetry/telemetry.py
View File

@@ -6,6 +6,7 @@ import os
import platform
import warnings
from contextlib import contextmanager
from importlib.metadata import version
from typing import TYPE_CHECKING, Any, Optional
@@ -16,12 +17,10 @@ def suppress_warnings():
yield
with suppress_warnings():
import pkg_resources
from opentelemetry import trace # noqa: E402
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter # noqa: E402
from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
OTLPSpanExporter, # noqa: E402
)
from opentelemetry.sdk.resources import SERVICE_NAME, Resource # noqa: E402
from opentelemetry.sdk.trace import TracerProvider # noqa: E402
from opentelemetry.sdk.trace.export import BatchSpanProcessor # noqa: E402
@@ -104,7 +103,7 @@ class Telemetry:
self._add_attribute(
span,
"crewai_version",
pkg_resources.get_distribution("crewai").version,
version("crewai"),
)
self._add_attribute(span, "python_version", platform.python_version())
self._add_attribute(span, "crew_key", crew.key)
@@ -306,7 +305,7 @@ class Telemetry:
self._add_attribute(
span,
"crewai_version",
pkg_resources.get_distribution("crewai").version,
version("crewai"),
)
self._add_attribute(span, "tool_name", tool_name)
self._add_attribute(span, "attempts", attempts)
@@ -326,7 +325,7 @@ class Telemetry:
self._add_attribute(
span,
"crewai_version",
pkg_resources.get_distribution("crewai").version,
version("crewai"),
)
self._add_attribute(span, "tool_name", tool_name)
self._add_attribute(span, "attempts", attempts)
@@ -346,7 +345,7 @@ class Telemetry:
self._add_attribute(
span,
"crewai_version",
pkg_resources.get_distribution("crewai").version,
version("crewai"),
)
if llm:
self._add_attribute(span, "llm", llm.model)
@@ -365,7 +364,7 @@ class Telemetry:
self._add_attribute(
span,
"crewai_version",
pkg_resources.get_distribution("crewai").version,
version("crewai"),
)
self._add_attribute(span, "crew_key", crew.key)
self._add_attribute(span, "crew_id", str(crew.id))
@@ -391,7 +390,7 @@ class Telemetry:
self._add_attribute(
span,
"crewai_version",
pkg_resources.get_distribution("crewai").version,
version("crewai"),
)
self._add_attribute(span, "crew_key", crew.key)
self._add_attribute(span, "crew_id", str(crew.id))
@@ -472,7 +471,7 @@ class Telemetry:
self._add_attribute(
span,
"crewai_version",
pkg_resources.get_distribution("crewai").version,
version("crewai"),
)
self._add_attribute(span, "crew_key", crew.key)
self._add_attribute(span, "crew_id", str(crew.id))
@@ -541,7 +540,7 @@ class Telemetry:
self._add_attribute(
crew._execution_span,
"crewai_version",
pkg_resources.get_distribution("crewai").version,
version("crewai"),
)
self._add_attribute(
crew._execution_span, "crew_output", final_string_output

4
src/crewai/tools/agent_tools/agent_tools.py
View File

@@ -1,9 +1,9 @@
from crewai.tools.base_tool import BaseTool
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.tools.base_tool import BaseTool
from crewai.utilities import I18N
from .delegate_work_tool import DelegateWorkTool
from .ask_question_tool import AskQuestionTool
from .delegate_work_tool import DelegateWorkTool
class AgentTools:

4
src/crewai/tools/agent_tools/ask_question_tool.py
View File

@@ -1,7 +1,9 @@
from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
from typing import Optional
from pydantic import BaseModel, Field
from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
class AskQuestionToolSchema(BaseModel):
question: str = Field(..., description="The question to ask")

7
src/crewai/tools/agent_tools/base_agent_tools.py
View File

@@ -1,9 +1,10 @@
from typing import Optional, Union
from pydantic import Field
from crewai.tools.base_tool import BaseTool
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.task import Task
from crewai.tools.base_tool import BaseTool
from crewai.utilities import I18N
@@ -44,14 +45,14 @@ class BaseAgentTool(BaseTool):
if available_agent.role.casefold().replace("\n", "") == agent_name
]
except Exception as _:
return self.i18n.errors("agent_tool_unexsiting_coworker").format(
return self.i18n.errors("agent_tool_unexisting_coworker").format(
coworkers="\n".join(
[f"- {agent.role.casefold()}" for agent in self.agents]
)
)
if not agent:
return self.i18n.errors("agent_tool_unexsiting_coworker").format(
return self.i18n.errors("agent_tool_unexisting_coworker").format(
coworkers="\n".join(
[f"- {agent.role.casefold()}" for agent in self.agents]
)

3
src/crewai/tools/agent_tools/delegate_work_tool.py
View File

@@ -1,8 +1,9 @@
from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
from typing import Optional
from pydantic import BaseModel, Field
from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
class DelegateWorkToolSchema(BaseModel):
task: str = Field(..., description="The task to delegate")

16
src/crewai/tools/tool_usage.py
View File

@@ -1,6 +1,5 @@
import ast
import datetime
import os
import time
from difflib import SequenceMatcher
from textwrap import dedent
@@ -15,12 +14,10 @@ from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
from crewai.tools.tool_usage_events import ToolUsageError, ToolUsageFinished
from crewai.utilities import I18N, Converter, ConverterError, Printer
agentops = None
if os.environ.get("AGENTOPS_API_KEY"):
try:
import agentops # type: ignore
except ImportError:
pass
try:
import agentops # type: ignore
except ImportError:
agentops = None
OPENAI_BIGGER_MODELS = ["gpt-4", "gpt-4o", "o1-preview", "o1-mini"]
@@ -422,9 +419,10 @@ class ToolUsage:
elif value.lower() in [
"true",
"false",
"null",
]: # Check for boolean and null values
value = value.lower()
value = value.lower().capitalize()
elif value.lower() == "null":
value = "None"
else:
# Assume the value is a string and needs quotes
value = '"' + value.replace('"', '\\"') + '"'

5
src/crewai/tools/tool_usage_events.py
View File

@@ -1,6 +1,7 @@
from typing import Any, Dict
from pydantic import BaseModel
from datetime import datetime
from typing import Any, Dict
from pydantic import BaseModel
class ToolUsageEvent(BaseModel):

7
src/crewai/translations/en.json
View File

@@ -19,15 +19,16 @@
"human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
"getting_input": "This is the agent's final answer: {final_answer}\n\n",
"summarizer_system_message": "You are a helpful assistant that summarizes text.",
"sumamrize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
"summarize_instruction": "Summarize the following text, make sure to include all the important information: {group}",
"summary": "This is a summary of our conversation so far:\n{merged_summary}",
"manager_request": "Your best answer to your coworker asking you this, accounting for the context shared.",
"formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python."
"formatted_task_instructions": "Ensure your final answer contains only the content in the following format: {output_format}\n\nEnsure the final output does not include any code block markers like ```json or ```python.",
"human_feedback_classification": "Determine if the following feedback indicates that the user is satisfied or if further changes are needed. Respond with 'True' if further changes are needed, or 'False' if the user is satisfied. **Important** Do not include any additional commentary outside of your 'True' or 'False' response.\n\nFeedback: \"{feedback}\""
},
"errors": {
"force_final_answer_error": "You can't keep going, this was the best you could do.\n {formatted_answer.text}",
"force_final_answer": "Now it's time you MUST give your absolute best final answer. You'll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer.",
"agent_tool_unexsiting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n",
"agent_tool_unexisting_coworker": "\nError executing tool. coworker mentioned not found, it must be one of the following options:\n{coworkers}\n",
"task_repeated_usage": "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n\n",
"tool_usage_error": "I encountered an error: {error}",
"tool_arguments_error": "Error: the Action Input is not a valid key, value dictionary.",

2
src/crewai/utilities/constants.py
View File

@@ -2,3 +2,5 @@ TRAINING_DATA_FILE = "training_data.pkl"
TRAINED_AGENTS_DATA_FILE = "trained_agents_data.pkl"
DEFAULT_SCORE_THRESHOLD = 0.35
KNOWLEDGE_DIRECTORY = "knowledge"
MAX_LLM_RETRY = 3
MAX_FILE_NAME_LENGTH = 255

10
src/crewai/utilities/crew_json_encoder.py
View File

@@ -1,15 +1,17 @@
from datetime import datetime, date
import json
from uuid import UUID
from pydantic import BaseModel
from datetime import date, datetime
from decimal import Decimal
from enum import Enum
from uuid import UUID
from pydantic import BaseModel
class CrewJSONEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, BaseModel):
return self._handle_pydantic_model(obj)
elif isinstance(obj, UUID) or isinstance(obj, Decimal):
elif isinstance(obj, UUID) or isinstance(obj, Decimal) or isinstance(obj, Enum):
return str(obj)
elif isinstance(obj, datetime) or isinstance(obj, date):

5
src/crewai/utilities/crew_pydantic_output_parser.py
View File

@@ -1,10 +1,11 @@
import json
import regex
from typing import Any, Type
from crewai.agents.parser import OutputParserException
import regex
from pydantic import BaseModel, ValidationError
from crewai.agents.parser import OutputParserException
class CrewPydanticOutputParser:
"""Parses the text into pydantic models"""

3
src/crewai/utilities/embedding_configurator.py
View File

@@ -1,6 +1,7 @@
import os
from typing import Any, Dict, cast
from chromadb import EmbeddingFunction, Documents, Embeddings
from chromadb import Documents, EmbeddingFunction, Embeddings
from chromadb.api.types import validate_embedding_function

9
src/crewai/utilities/evaluators/crew_evaluator_handler.py
View File

@@ -1,13 +1,14 @@
from collections import defaultdict
from pydantic import BaseModel, Field
from rich.box import HEAVY_EDGE
from rich.console import Console
from rich.table import Table
from crewai.agent import Agent
from crewai.task import Task
from crewai.tasks.task_output import TaskOutput
from crewai.telemetry import Telemetry
from pydantic import BaseModel, Field
from rich.box import HEAVY_EDGE
from rich.console import Console
from rich.table import Table
class TaskEvaluationPydanticOutput(BaseModel):

21
src/crewai/utilities/evaluators/task_evaluator.py
View File

@@ -1,4 +1,3 @@
import os
from typing import List
from pydantic import BaseModel, Field
@@ -6,27 +5,17 @@ from pydantic import BaseModel, Field
from crewai.utilities import Converter
from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
agentops = None
try:
from agentops import track_agent # type: ignore
except ImportError:
def mock_agent_ops_provider():
def track_agent(*args, **kwargs):
def track_agent(name):
def noop(f):
return f
return noop
return track_agent
agentops = None
if os.environ.get("AGENTOPS_API_KEY"):
try:
from agentops import track_agent
except ImportError:
track_agent = mock_agent_ops_provider()
else:
track_agent = mock_agent_ops_provider()
class Entity(BaseModel):
name: str = Field(description="The name of the entity.")

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

@@ -1,7 +1,7 @@
from typing import Any, Callable, Generic, List, Dict, Type, TypeVar
from functools import wraps
from pydantic import BaseModel
from typing import Any, Callable, Dict, Generic, List, Type, TypeVar
from pydantic import BaseModel
T = TypeVar("T")
EVT = TypeVar("EVT", bound=BaseModel)

1
src/crewai/utilities/planning_handler.py
View File

@@ -1,4 +1,5 @@
from typing import Any, List, Optional
from pydantic import BaseModel, Field
from crewai.agent import Agent

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

@@ -1,5 +1,7 @@
from pydantic import BaseModel, Field
from typing import Any, Optional
from pydantic import BaseModel, Field
from crewai.utilities import I18N

2
src/crewai/utilities/pydantic_schema_parser.py
View File

@@ -1,4 +1,4 @@
from typing import Type, get_args, get_origin, Union
from typing import Type, Union, get_args, get_origin
from pydantic import BaseModel

6
src/crewai/utilities/task_output_storage_handler.py
View File

@@ -1,6 +1,8 @@
from pydantic import BaseModel, Field
from datetime import datetime
from typing import Dict, Any, Optional, List
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Field
from crewai.memory.storage.kickoff_task_outputs_storage import (
KickoffTaskOutputsSQLiteStorage,
)

3
src/crewai/utilities/token_counter_callback.py
View File

@@ -1,5 +1,6 @@
from litellm.integrations.custom_logger import CustomLogger
from litellm.types.utils import Usage
from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
@@ -11,7 +12,7 @@ class TokenCalcHandler(CustomLogger):
if self.token_cost_process is None:
return
usage : Usage = response_obj["usage"]
usage: Usage = response_obj["usage"]
self.token_cost_process.sum_successful_requests(1)
self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
self.token_cost_process.sum_completion_tokens(usage.completion_tokens)