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rebuilding executor

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This commit is contained in:
João Moura
2024-09-11 16:58:56 -07:00
parent 322780a5f3
commit 3cd464497b
134 changed files with 35040 additions and 1620079 deletions
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869
poetry.lock generated
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2
pyproject.toml
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@@ -30,6 +30,8 @@ agentops = { version = "^0.3.0", optional = true }
embedchain = "^0.1.114"
json-repair = "^0.25.2"
auth0-python = "^4.7.1"
poetry = "^1.8.3"
litellm = "^1.44.22"
[tool.poetry.extras]
tools = ["crewai-tools"]

3
score.json Normal file
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@@ -0,0 +1,3 @@
{
"score": 4
}

159
src/crewai/agent.py
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@@ -1,23 +1,17 @@
import os
from inspect import signature
from typing import Any, List, Optional, Tuple
from langchain.agents.agent import RunnableAgent
from langchain.agents.tools import BaseTool
from langchain.agents.tools import tool as LangChainTool
from langchain_core.agents import AgentAction
from langchain_core.callbacks import BaseCallbackHandler
from langchain_openai import ChatOpenAI
from typing import Any, List, Optional
from pydantic import Field, InstanceOf, PrivateAttr, model_validator
from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser
from crewai.agents import CacheHandler
from crewai.utilities import Converter, Prompts
from crewai.tools.agent_tools import AgentTools
from crewai.agents.crew_agent_executor import CrewAgentExecutor
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.memory.contextual.contextual_memory import ContextualMemory
from crewai.tools.agent_tools import AgentTools
from crewai.utilities import Converter, Prompts
from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
from crewai.utilities.token_counter_callback import TokenCalcHandler
from crewai.utilities.training_handler import CrewTrainingHandler
from crewai.utilities.token_counter_callback import TokenCalcHandler
def mock_agent_ops_provider():
@@ -34,7 +28,6 @@ agentops = None
if os.environ.get("AGENTOPS_API_KEY"):
try:
import agentops # type: ignore # Name "agentops" already defined on line 21
from agentops import track_agent
except ImportError:
track_agent = mock_agent_ops_provider()
@@ -64,7 +57,6 @@ class Agent(BaseAgent):
allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
tools: Tools at agents disposal
step_callback: Callback to be executed after each step of the agent execution.
callbacks: A list of callback functions from the langchain library that are triggered during the agent's execution process
"""
_times_executed: int = PrivateAttr(default=0)
@@ -82,17 +74,11 @@ class Agent(BaseAgent):
description="Callback to be executed after each step of the agent execution.",
)
llm: Any = Field(
default_factory=lambda: ChatOpenAI(
model=os.environ.get("OPENAI_MODEL_NAME", "gpt-4o")
),
description="Language model that will run the agent.",
description="Language model that will run the agent.", default="gpt-4o"
)
function_calling_llm: Optional[Any] = Field(
description="Language model that will run the agent.", default=None
)
callbacks: Optional[List[InstanceOf[BaseCallbackHandler]]] = Field(
default=None, description="Callback to be executed"
)
system_template: Optional[str] = Field(
default=None, description="System format for the agent."
)
@@ -108,6 +94,9 @@ class Agent(BaseAgent):
allow_code_execution: Optional[bool] = Field(
default=False, description="Enable code execution for the agent."
)
sliding_context_window: Optional[bool] = Field(
default=False, description="Enable sliding context window for the agent."
)
max_retry_limit: int = Field(
default=2,
description="Maximum number of retries for an agent to execute a task when an error occurs.",
@@ -116,38 +105,17 @@ class Agent(BaseAgent):
@model_validator(mode="after")
def post_init_setup(self):
self.agent_ops_agent_name = self.role
# Different llms store the model name in different attributes
model_name = getattr(self.llm, "model_name", None) or getattr(
self.llm, "deployment_name", None
self.llm = self.llm.model_name if hasattr(self.llm, "model_name") else self.llm
self.function_calling_llm = (
self.function_calling_llm.model_name
if hasattr(self.function_calling_llm, "model_name")
else self.function_calling_llm
)
if model_name:
self._setup_llm_callbacks(model_name)
if not self.agent_executor:
self._setup_agent_executor()
return self
def _setup_llm_callbacks(self, model_name: str):
token_handler = TokenCalcHandler(model_name, self._token_process)
if not isinstance(self.llm.callbacks, list):
self.llm.callbacks = []
if not any(
isinstance(handler, TokenCalcHandler) for handler in self.llm.callbacks
):
self.llm.callbacks.append(token_handler)
if agentops and not any(
isinstance(handler, agentops.LangchainCallbackHandler)
for handler in self.llm.callbacks
):
agentops.stop_instrumenting()
self.llm.callbacks.append(agentops.LangchainCallbackHandler())
def _setup_agent_executor(self):
if not self.cache_handler:
self.cache_handler = CacheHandler()
@@ -190,15 +158,7 @@ class Agent(BaseAgent):
task_prompt += self.i18n.slice("memory").format(memory=memory)
tools = tools or self.tools or []
parsed_tools = self._parse_tools(tools)
self.create_agent_executor(tools=tools)
self.agent_executor.tools = parsed_tools
self.agent_executor.task = task
self.agent_executor.tools_description = self._render_text_description_and_args(
parsed_tools
)
self.agent_executor.tools_names = self.__tools_names(parsed_tools)
self.create_agent_executor(tools=tools, task=task)
if self.crew and self.crew._train:
task_prompt = self._training_handler(task_prompt=task_prompt)
@@ -211,6 +171,7 @@ class Agent(BaseAgent):
"input": task_prompt,
"tool_names": self.agent_executor.tools_names,
"tools": self.agent_executor.tools_description,
"should_ask_for_human_input": task.human_input,
}
)["output"]
except Exception as e:
@@ -231,73 +192,24 @@ class Agent(BaseAgent):
return result
def format_log_to_str(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
observation_prefix: str = "Observation: ",
llm_prefix: str = "",
) -> str:
"""Construct the scratchpad that lets the agent continue its thought process."""
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\n{observation_prefix}{observation}\n{llm_prefix}"
return thoughts
def create_agent_executor(self, tools=None) -> None:
def create_agent_executor(self, tools=None, task=None) -> None:
"""Create an agent executor for the agent.
Returns:
An instance of the CrewAgentExecutor class.
"""
tools = tools or self.tools or []
agent_args = {
"input": lambda x: x["input"],
"tools": lambda x: x["tools"],
"tool_names": lambda x: x["tool_names"],
"agent_scratchpad": lambda x: self.format_log_to_str(
x["intermediate_steps"]
),
}
executor_args = {
"llm": self.llm,
"i18n": self.i18n,
"crew": self.crew,
"crew_agent": self,
"tools": self._parse_tools(tools),
"verbose": self.verbose,
"original_tools": tools,
"handle_parsing_errors": True,
"max_iterations": self.max_iter,
"max_execution_time": self.max_execution_time,
"step_callback": self.step_callback,
"tools_handler": self.tools_handler,
"function_calling_llm": self.function_calling_llm,
"callbacks": self.callbacks,
"max_tokens": self.max_tokens,
}
if self._rpm_controller:
executor_args["request_within_rpm_limit"] = (
self._rpm_controller.check_or_wait
)
parsed_tools = self._parse_tools(tools)
prompt = Prompts(
i18n=self.i18n,
agent=self,
tools=tools,
i18n=self.i18n,
system_template=self.system_template,
prompt_template=self.prompt_template,
response_template=self.response_template,
).task_execution()
execution_prompt = prompt.partial(
goal=self.goal,
role=self.role,
backstory=self.backstory,
)
stop_words = [self.i18n.slice("observation")]
if self.response_template:
@@ -305,11 +217,26 @@ class Agent(BaseAgent):
self.response_template.split("{{ .Response }}")[1].strip()
)
bind = self.llm.bind(stop=stop_words)
inner_agent = agent_args | execution_prompt | bind | CrewAgentParser(agent=self)
self.agent_executor = CrewAgentExecutor(
agent=RunnableAgent(runnable=inner_agent), **executor_args
llm=self.llm,
task=task,
agent=self,
crew=self.crew,
tools=parsed_tools,
prompt=prompt,
original_tools=tools,
stop_words=stop_words,
max_iter=self.max_iter,
tools_handler=self.tools_handler,
tools_names=self.__tools_names(parsed_tools),
tools_description=self._render_text_description_and_args(parsed_tools),
step_callback=self.step_callback,
function_calling_llm=self.function_calling_llm,
sliding_context_window=self.sliding_context_window,
request_within_rpm_limit=self._rpm_controller.check_or_wait
if self._rpm_controller
else None,
callbacks=[TokenCalcHandler(self._token_process)],
)
def get_delegation_tools(self, agents: List[BaseAgent]):
@@ -330,7 +257,7 @@ class Agent(BaseAgent):
def get_output_converter(self, llm, text, model, instructions):
return Converter(llm=llm, text=text, model=model, instructions=instructions)
def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]: # type: ignore # Function "langchain_core.tools.tool" is not valid as a type
def _parse_tools(self, tools: List[Any]) -> List[Any]: # type: ignore # Function "langchain_core.tools.tool" is not valid as a type
"""Parse tools to be used for the task."""
tools_list = []
try:
@@ -373,7 +300,7 @@ class Agent(BaseAgent):
)
return task_prompt
def _render_text_description(self, tools: List[BaseTool]) -> str:
def _render_text_description(self, tools: List[Any]) -> str:
"""Render the tool name and description in plain text.
Output will be in the format of:
@@ -392,7 +319,7 @@ class Agent(BaseAgent):
return description
def _render_text_description_and_args(self, tools: List[BaseTool]) -> str:
def _render_text_description_and_args(self, tools: List[Any]) -> str:
"""Render the tool name, description, and args in plain text.
Output will be in the format of:

3
src/crewai/agents/__init__.py
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@@ -1,6 +1,5 @@
from .cache.cache_handler import CacheHandler
from .executor import CrewAgentExecutor
from .parser import CrewAgentParser
from .tools_handler import ToolsHandler
__all__ = ["CacheHandler", "CrewAgentExecutor", "CrewAgentParser", "ToolsHandler"]
__all__ = ["CacheHandler", "CrewAgentParser", "ToolsHandler"]

2
src/crewai/agents/agent_builder/base_agent.py
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@@ -224,10 +224,8 @@ class BaseAgent(ABC, BaseModel):
# Copy llm and clear callbacks
existing_llm = shallow_copy(self.llm)
existing_llm.callbacks = []
copied_data = self.model_dump(exclude=exclude)
copied_data = {k: v for k, v in copied_data.items() if v is not None}
copied_agent = type(self)(**copied_data, llm=existing_llm, tools=self.tools)
return copied_agent

8
src/crewai/agents/agent_builder/base_agent_executor_mixin.py
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@@ -19,15 +19,15 @@ class CrewAgentExecutorMixin:
crew_agent: Optional["BaseAgent"]
task: Optional["Task"]
iterations: int
force_answer_max_iterations: int
have_forced_answer: bool
_i18n: I18N
def _should_force_answer(self) -> bool:
"""Determine if a forced answer is required based on iteration count."""
return (
self.iterations == self.force_answer_max_iterations
) and not self.have_forced_answer
print("*** self.iterations", self.iterations)
print("*** self.max_iter", self.max_iter)
print("*** self.have_forced_answer", self.have_forced_answer)
return (self.iterations >= self.max_iter) and not self.have_forced_answer
def _create_short_term_memory(self, output) -> None:
"""Create and save a short-term memory item if conditions are met."""

267
src/crewai/agents/crew_agent_executor.py Normal file
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@@ -0,0 +1,267 @@
from typing import Any, Dict, List
from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
from crewai.agents.parser import CrewAgentParser
from crewai.agents.tools_handler import ToolsHandler
from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
from crewai.utilities import I18N
from crewai.utilities.constants import TRAINING_DATA_FILE
from crewai.utilities.exceptions.context_window_exceeding_exception import (
LLMContextLengthExceededException,
)
from crewai.utilities.logger import Logger
from crewai.utilities.training_handler import CrewTrainingHandler
from crewai.llm import LLM
from crewai.agents.parser import AgentAction, AgentFinish, OutputParserException
class CrewAgentExecutor(CrewAgentExecutorMixin):
_logger: Logger = Logger()
def __init__(
self,
llm: Any,
task: Any,
crew: Any,
agent: Any,
prompt: str,
max_iter: int,
tools: List[Any],
tools_names: str,
stop_words: List[str],
tools_description: str,
tools_handler: ToolsHandler,
step_callback: Any = None,
original_tools: List[Any] = [],
function_calling_llm: Any = None,
sliding_context_window: bool = False,
request_within_rpm_limit: Any = None,
callbacks: List[Any] = [],
):
self._i18n: I18N = I18N()
self.llm = llm
self.task = task
self.agent = agent
self.crew = crew
self.prompt = prompt
self.tools = tools
self.tools_names = tools_names
self.stop = stop_words
self.max_iter = max_iter
self.callbacks = callbacks
self.tools_handler = tools_handler
self.original_tools = original_tools
self.step_callback = step_callback
self.tools_description = tools_description
self.function_calling_llm = function_calling_llm
self.sliding_context_window = sliding_context_window
self.request_within_rpm_limit = request_within_rpm_limit
self.should_ask_for_human_input = False
self.messages = []
self.iterations = 0
self.have_forced_answer = False
self.name_to_tool_map = {tool.name: tool for tool in self.tools}
def invoke(self, inputs: Dict[str, str]) -> Dict[str, Any]:
formatted_answer = None
formatted_prompt = self._format_prompt(self.prompt, inputs)
self.should_ask_for_human_input = inputs.get(
"should_ask_for_human_input", False
)
self.messages = self._messages(formatted_prompt)
formatted_answer = self._invoke_loop(formatted_answer)
if self.should_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)
# Making sure we only ask for it once, so disabling for the next thought loop
self.should_ask_for_human_input = False
self.messages.append(
{"role": "user", "content": f"Feedback: {human_feedback}"}
)
formatted_answer = self._invoke_loop(None)
return {"output": formatted_answer.output}
def _invoke_loop(self, formatted_answer):
try:
while not isinstance(formatted_answer, AgentFinish):
# print('2222222')
if not self.request_within_rpm_limit or self.request_within_rpm_limit():
# print('3333333')
answer = LLM(
self.llm, stop=self.stop, callbacks=self.callbacks
).call(self.messages)
self.iterations += 1
print("*** self.iterations", self.iterations)
# if self.iterations > 11:
# sadasd
formatted_answer = self._format_answer(answer)
if isinstance(formatted_answer, AgentAction):
# print('4444444')
action_result = self._use_tool(formatted_answer)
formatted_answer.text += f"\nObservation: {action_result}"
# print(formatted_answer)
if self.step_callback:
formatted_answer.result = action_result
self.step_callback(formatted_answer)
if self._should_force_answer():
if self.have_forced_answer:
return {
"output": self._i18n.errors(
"force_final_answer_error"
).format(formatted_answer.text)
}
else:
formatted_answer.text += (
f'\n{self._i18n.errors("force_final_answer")}'
)
self.have_forced_answer = True
self.messages.append(
{"role": "assistant", "content": formatted_answer.text}
)
except OutputParserException as e:
# print('5555555')
self.messages.append({"role": "assistant", "content": e.error})
self._invoke_loop(formatted_answer)
except Exception as e:
# print('6666666')
print("*** e", e)
if LLMContextLengthExceededException(str(e))._is_context_limit_error(
str(e)
):
self._handle_context_length()
self._invoke_loop(formatted_answer)
# print('7777777')
return formatted_answer
def _use_tool(self, agent_action: AgentAction) -> None:
tool_usage = ToolUsage(
tools_handler=self.tools_handler,
tools=self.tools,
original_tools=self.original_tools,
tools_description=self.tools_description,
tools_names=self.tools_names,
function_calling_llm=self.function_calling_llm,
task=self.task,
agent=self.agent,
action=agent_action,
)
tool_calling = tool_usage.parse(agent_action.text)
if isinstance(tool_calling, ToolUsageErrorException):
tool_result = tool_calling.message
else:
if tool_calling.tool_name.casefold().strip() in [
name.casefold().strip() for name in self.name_to_tool_map
] or tool_calling.tool_name.casefold().replace("_", " ") in [
name.casefold().strip() for name in self.name_to_tool_map
]:
tool_result = tool_usage.use(tool_calling, agent_action.text)
else:
tool_result = self._i18n.errors("wrong_tool_name").format(
tool=tool_calling.tool_name,
tools=", ".join([tool.name.casefold() for tool in self.tools]),
)
return tool_result
def _summarize_messages(self) -> None:
llm = LLM(self.llm)
grouped_messages = []
for message in self.messages:
content = message["content"]
for i in range(0, len(content), 5000):
grouped_messages.append(content[i : i + 5000])
summarized_contents = []
for group in grouped_messages:
summary = llm.call(
[
{
"role": "system",
"content": "You are a helpful assistant that summarizes text.",
},
{
"role": "user",
"content": f"Summarize the following text, make sure to include all the important information: {group}",
},
]
)
summarized_contents.append(summary)
merged_summary = " ".join(summarized_contents)
self.messages = [
{
"role": "user",
"content": f"This is a summary of our conversation so far:\n{merged_summary}",
}
]
def _handle_context_length(self) -> None:
if self.sliding_context_window:
self._logger.log(
"debug",
"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.",
color="red",
)
raise SystemExit(
"Context length exceeded and user opted not to summarize. Consider using smaller text or RAG tools from crewai_tools."
)
def _handle_crew_training_output(
self, result: AgentFinish, human_feedback: str | None = None
) -> None:
"""Function to handle the process of the training data."""
agent_id = str(self.agent.id)
if (
CrewTrainingHandler(TRAINING_DATA_FILE).load()
and not self.should_ask_for_human_input
):
training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
if training_data.get(agent_id):
training_data[agent_id][self.crew._train_iteration][
"improved_output"
] = result.output
CrewTrainingHandler(TRAINING_DATA_FILE).save(training_data)
if self.should_ask_for_human_input and human_feedback is not None:
training_data = {
"initial_output": result.output,
"human_feedback": human_feedback,
"agent": agent_id,
"agent_role": self.agent.role,
}
CrewTrainingHandler(TRAINING_DATA_FILE).append(
self.crew._train_iteration, agent_id, training_data
)
def _format_prompt(self, prompt: str, inputs: Dict[str, str]) -> str:
prompt = prompt.replace("{input}", inputs["input"])
prompt = prompt.replace("{tool_names}", inputs["tool_names"])
prompt = prompt.replace("{tools}", inputs["tools"])
return prompt
def _format_answer(self, answer: str) -> str:
return CrewAgentParser(agent=self).parse(answer)
def _messages(self, prompt: str) -> List[Dict[str, str]]:
return [{"role": "user", "content": prompt}]

397
src/crewai/agents/executor.py
View File

@@ -1,397 +0,0 @@
import threading
import time
from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
import click
from langchain.agents import AgentExecutor
from langchain.agents.agent import ExceptionTool
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.summarize import load_summarize_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.agents import AgentAction, AgentFinish, AgentStep
from langchain_core.exceptions import OutputParserException
from langchain_core.tools import BaseTool
from langchain_core.utils.input import get_color_mapping
from pydantic import InstanceOf
from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
from crewai.agents.tools_handler import ToolsHandler
from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
from crewai.utilities import I18N
from crewai.utilities.constants import TRAINING_DATA_FILE
from crewai.utilities.exceptions.context_window_exceeding_exception import (
LLMContextLengthExceededException,
)
from crewai.utilities.logger import Logger
from crewai.utilities.training_handler import CrewTrainingHandler
class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
_i18n: I18N = I18N()
should_ask_for_human_input: bool = False
llm: Any = None
iterations: int = 0
task: Any = None
tools_description: str = ""
tools_names: str = ""
original_tools: List[Any] = []
crew_agent: Any = None
crew: Any = None
function_calling_llm: Any = None
request_within_rpm_limit: Any = None
tools_handler: Optional[InstanceOf[ToolsHandler]] = None
max_iterations: Optional[int] = 15
have_forced_answer: bool = False
force_answer_max_iterations: Optional[int] = None # type: ignore # Incompatible types in assignment (expression has type "int | None", base class "CrewAgentExecutorMixin" defined the type as "int")
step_callback: Optional[Any] = None
system_template: Optional[str] = None
prompt_template: Optional[str] = None
response_template: Optional[str] = None
_logger: Logger = Logger()
_fit_context_window_strategy: Optional[Literal["summarize"]] = "summarize"
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run text through and get agent response."""
# Construct a mapping of tool name to tool for easy lookup
name_to_tool_map = {tool.name: tool for tool in self.tools}
# We construct a mapping from each tool to a color, used for logging.
color_mapping = get_color_mapping(
[tool.name.casefold() for tool in self.tools],
excluded_colors=["green", "red"],
)
intermediate_steps: List[Tuple[AgentAction, str]] = []
# Allowing human input given task setting
if self.task and self.task.human_input:
self.should_ask_for_human_input = True
# Let's start tracking the number of iterations and time elapsed
self.iterations = 0
time_elapsed = 0.0
start_time = time.time()
# We now enter the agent loop (until it returns something).
while self._should_continue(self.iterations, time_elapsed):
if not self.request_within_rpm_limit or self.request_within_rpm_limit():
next_step_output = self._take_next_step(
name_to_tool_map,
color_mapping,
inputs,
intermediate_steps,
run_manager=run_manager,
)
if self.step_callback:
self.step_callback(next_step_output)
if isinstance(next_step_output, AgentFinish):
# Creating long term memory
create_long_term_memory = threading.Thread(
target=self._create_long_term_memory, args=(next_step_output,)
)
create_long_term_memory.start()
return self._return(
next_step_output, intermediate_steps, run_manager=run_manager
)
intermediate_steps.extend(next_step_output)
if len(next_step_output) == 1:
next_step_action = next_step_output[0]
# See if tool should return directly
tool_return = self._get_tool_return(next_step_action)
if tool_return is not None:
return self._return(
tool_return, intermediate_steps, run_manager=run_manager
)
self.iterations += 1
time_elapsed = time.time() - start_time
output = self.agent.return_stopped_response(
self.early_stopping_method, intermediate_steps, **inputs
)
return self._return(output, intermediate_steps, run_manager=run_manager)
def _iter_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Iterator[Union[AgentFinish, AgentAction, AgentStep]]:
"""Take a single step in the thought-action-observation loop.
Override this to take control of how the agent makes and acts on choices.
"""
try:
if self._should_force_answer():
error = self._i18n.errors("force_final_answer")
output = AgentAction("_Exception", error, error)
self.have_forced_answer = True
yield AgentStep(action=output, observation=error)
return
intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
# Call the LLM to see what to do.
output = self.agent.plan(
intermediate_steps,
callbacks=run_manager.get_child() if run_manager else None,
**inputs,
)
except OutputParserException as e:
if isinstance(self.handle_parsing_errors, bool):
raise_error = not self.handle_parsing_errors
else:
raise_error = False
if raise_error:
raise ValueError(
"An output parsing error occurred. "
"In order to pass this error back to the agent and have it try "
"again, pass `handle_parsing_errors=True` to the AgentExecutor. "
f"This is the error: {str(e)}"
)
str(e)
if isinstance(self.handle_parsing_errors, bool):
if e.send_to_llm:
observation = f"\n{str(e.observation)}"
str(e.llm_output)
else:
observation = ""
elif isinstance(self.handle_parsing_errors, str):
observation = f"\n{self.handle_parsing_errors}"
elif callable(self.handle_parsing_errors):
observation = f"\n{self.handle_parsing_errors(e)}"
else:
raise ValueError("Got unexpected type of `handle_parsing_errors`")
output = AgentAction("_Exception", observation, "")
if run_manager:
run_manager.on_agent_action(output, color="green")
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = ExceptionTool().run(
output.tool_input,
verbose=False,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
if self._should_force_answer():
error = self._i18n.errors("force_final_answer")
output = AgentAction("_Exception", error, error)
yield AgentStep(action=output, observation=error)
return
yield AgentStep(action=output, observation=observation)
return
except Exception as e:
if LLMContextLengthExceededException(str(e))._is_context_limit_error(
str(e)
):
output = self._handle_context_length_error(
intermediate_steps, run_manager, inputs
)
if isinstance(output, AgentFinish):
yield output
elif isinstance(output, list):
for step in output:
yield step
return
raise e
# If the tool chosen is the finishing tool, then we end and return.
if isinstance(output, AgentFinish):
if self.should_ask_for_human_input:
human_feedback = self._ask_human_input(output.return_values["output"])
if self.crew and self.crew._train:
self._handle_crew_training_output(output, human_feedback)
# Making sure we only ask for it once, so disabling for the next thought loop
self.should_ask_for_human_input = False
action = AgentAction(
tool="Human Input", tool_input=human_feedback, log=output.log
)
yield AgentStep(
action=action,
observation=self._i18n.slice("human_feedback").format(
human_feedback=human_feedback
),
)
return
else:
if self.crew and self.crew._train:
self._handle_crew_training_output(output)
yield output
return
self._create_short_term_memory(output)
actions: List[AgentAction]
actions = [output] if isinstance(output, AgentAction) else output
yield from actions
for agent_action in actions:
if run_manager:
run_manager.on_agent_action(agent_action, color="green")
tool_usage = ToolUsage(
tools_handler=self.tools_handler, # type: ignore # Argument "tools_handler" to "ToolUsage" has incompatible type "ToolsHandler | None"; expected "ToolsHandler"
tools=self.tools, # type: ignore # Argument "tools" to "ToolUsage" has incompatible type "Sequence[BaseTool]"; expected "list[BaseTool]"
original_tools=self.original_tools,
tools_description=self.tools_description,
tools_names=self.tools_names,
function_calling_llm=self.function_calling_llm,
task=self.task,
agent=self.crew_agent,
action=agent_action,
)
tool_calling = tool_usage.parse(agent_action.log)
if isinstance(tool_calling, ToolUsageErrorException):
observation = tool_calling.message
else:
if tool_calling.tool_name.casefold().strip() in [
name.casefold().strip() for name in name_to_tool_map
] or tool_calling.tool_name.casefold().replace("_", " ") in [
name.casefold().strip() for name in name_to_tool_map
]:
observation = tool_usage.use(tool_calling, agent_action.log)
else:
observation = self._i18n.errors("wrong_tool_name").format(
tool=tool_calling.tool_name,
tools=", ".join([tool.name.casefold() for tool in self.tools]),
)
yield AgentStep(action=agent_action, observation=observation)
def _handle_crew_training_output(
self, output: AgentFinish, human_feedback: str | None = None
) -> None:
"""Function to handle the process of the training data."""
agent_id = str(self.crew_agent.id)
if (
CrewTrainingHandler(TRAINING_DATA_FILE).load()
and not self.should_ask_for_human_input
):
training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
if training_data.get(agent_id):
training_data[agent_id][self.crew._train_iteration][
"improved_output"
] = output.return_values["output"]
CrewTrainingHandler(TRAINING_DATA_FILE).save(training_data)
if self.should_ask_for_human_input and human_feedback is not None:
training_data = {
"initial_output": output.return_values["output"],
"human_feedback": human_feedback,
"agent": agent_id,
"agent_role": self.crew_agent.role,
}
CrewTrainingHandler(TRAINING_DATA_FILE).append(
self.crew._train_iteration, agent_id, training_data
)
def _handle_context_length(
self, intermediate_steps: List[Tuple[AgentAction, str]]
) -> List[Tuple[AgentAction, str]]:
text = intermediate_steps[0][1]
original_action = intermediate_steps[0][0]
text_splitter = RecursiveCharacterTextSplitter(
separators=["\n\n", "\n"],
chunk_size=8000,
chunk_overlap=500,
)
if self._fit_context_window_strategy == "summarize":
docs = text_splitter.create_documents([text])
self._logger.log(
"debug",
"Summarizing Content, it is recommended to use a RAG tool",
color="bold_blue",
)
summarize_chain = load_summarize_chain(
self.llm, chain_type="map_reduce", verbose=True
)
summarized_docs = []
for doc in docs:
summary = summarize_chain.invoke(
{"input_documents": [doc]}, return_only_outputs=True
)
summarized_docs.append(summary["output_text"])
formatted_results = "\n\n".join(summarized_docs)
summary_step = AgentStep(
action=AgentAction(
tool=original_action.tool,
tool_input=original_action.tool_input,
log=original_action.log,
),
observation=formatted_results,
)
summary_tuple = (summary_step.action, summary_step.observation)
return [summary_tuple]
return intermediate_steps
def _handle_context_length_error(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[CallbackManagerForChainRun],
inputs: Dict[str, str],
) -> Union[AgentFinish, List[AgentStep]]:
self._logger.log(
"debug",
"Context length exceeded. Asking user if they want to use summarize prompt to fit, this will reduce context length.",
color="yellow",
)
user_choice = click.confirm(
"Context length exceeded. Do you want to summarize the text to fit models context window?"
)
if user_choice:
self._logger.log(
"debug",
"Context length exceeded. Using summarize prompt to fit, this will reduce context length.",
color="bold_blue",
)
intermediate_steps = self._handle_context_length(intermediate_steps)
output = self.agent.plan(
intermediate_steps,
callbacks=run_manager.get_child() if run_manager else None,
**inputs,
)
if isinstance(output, AgentFinish):
return output
elif isinstance(output, AgentAction):
return [AgentStep(action=output, observation=None)]
else:
return [AgentStep(action=action, observation=None) for action in output]
else:
self._logger.log(
"debug",
"Context length exceeded. Consider using smaller text or RAG tools from crewai_tools.",
color="red",
)
raise SystemExit(
"Context length exceeded and user opted not to summarize. Consider using smaller text or RAG tools from crewai_tools."
)

54
src/crewai/agents/parser.py
View File

@@ -1,10 +1,6 @@
import re
from typing import Any, Union
from json_repair import repair_json
from langchain.agents.output_parsers import ReActSingleInputOutputParser
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from crewai.utilities import I18N
@@ -14,7 +10,35 @@ MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE = "I did it wrong. Invalid Forma
FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = "I did it wrong. Tried to both perform Action and give a Final Answer at the same time, I must do one or the other"
class CrewAgentParser(ReActSingleInputOutputParser):
class AgentAction:
tool: str
tool_input: str
text: str
result: str
def __init__(self, tool: str, tool_input: str, text: str):
self.tool = tool
self.tool_input = tool_input
self.text = text
class AgentFinish:
output: str
text: str
def __init__(self, output: str, text: str):
self.output = output
self.text = text
class OutputParserException(Exception):
error: str
def __init__(self, error: str):
self.error = error
class CrewAgentParser:
"""Parses ReAct-style LLM calls that have a single tool input.
Expects output to be in one of two formats.
@@ -38,6 +62,9 @@ class CrewAgentParser(ReActSingleInputOutputParser):
_i18n: I18N = I18N()
agent: Any = None
def __init__(self, agent: Any):
self.agent = agent
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
includes_answer = FINAL_ANSWER_ACTION in text
regex = (
@@ -60,27 +87,19 @@ class CrewAgentParser(ReActSingleInputOutputParser):
return AgentAction(clean_action, safe_tool_input, text)
elif includes_answer:
return AgentFinish(
{"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
)
return AgentFinish(text.split(FINAL_ANSWER_ACTION)[-1].strip(), text)
if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
self.agent.increment_formatting_errors()
raise OutputParserException(
f"Could not parse LLM output: `{text}`",
observation=f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{self._i18n.slice('final_answer_format')}",
llm_output=text,
send_to_llm=True,
f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{self._i18n.slice('final_answer_format')}",
)
elif not re.search(
r"[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)", text, re.DOTALL
):
self.agent.increment_formatting_errors()
raise OutputParserException(
f"Could not parse LLM output: `{text}`",
observation=MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
llm_output=text,
send_to_llm=True,
MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
)
else:
format = self._i18n.slice("format_without_tools")
@@ -88,9 +107,6 @@ class CrewAgentParser(ReActSingleInputOutputParser):
self.agent.increment_formatting_errors()
raise OutputParserException(
error,
observation=error,
llm_output=text,
send_to_llm=True,
)
def _clean_action(self, text: str) -> str:

2
src/crewai/cli/deploy/main.py
View File

@@ -117,7 +117,7 @@ class DeployCommand:
else:
self._handle_error(json_response)
def create_crew(self, confirm: bool) -> None:
def create_crew(self, confirm: bool = False) -> None:
"""
Create a new crew deployment.
"""

15
src/crewai/crew.py
View File

@@ -205,6 +205,11 @@ class Crew(BaseModel):
if self.output_log_file:
self._file_handler = FileHandler(self.output_log_file)
self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
self.function_calling_llm = (
self.function_calling_llm.model_name
if hasattr(self.function_calling_llm, "model_name")
else self.function_calling_llm
)
self._telemetry = Telemetry()
self._telemetry.set_tracer()
return self
@@ -588,8 +593,14 @@ class Crew(BaseModel):
"warning", "Manager agent should not have tools", color="orange"
)
manager.tools = []
raise Exception("Manager agent should not have tools")
manager.tools = self.manager_agent.get_delegation_tools(self.agents)
else:
self.manager_llm = (
self.manager_llm.model_name
if hasattr(self.manager_llm, "model_name")
else self.manager_llm
)
manager = Agent(
role=i18n.retrieve("hierarchical_manager_agent", "role"),
goal=i18n.retrieve("hierarchical_manager_agent", "goal"),
@@ -921,16 +932,14 @@ class Crew(BaseModel):
def calculate_usage_metrics(self) -> UsageMetrics:
"""Calculates and returns the usage metrics."""
total_usage_metrics = UsageMetrics()
for agent in self.agents:
if hasattr(agent, "_token_process"):
token_sum = agent._token_process.get_summary()
total_usage_metrics.add_usage_metrics(token_sum)
if self.manager_agent and hasattr(self.manager_agent, "_token_process"):
token_sum = self.manager_agent._token_process.get_summary()
total_usage_metrics.add_usage_metrics(token_sum)
self.usage_metrics = total_usage_metrics
return total_usage_metrics
def test(

21
src/crewai/llm.py Normal file
View File

@@ -0,0 +1,21 @@
from typing import Any, Dict, List
from litellm import completion
import litellm
class LLM:
def __init__(self, model: str, stop: List[str] = [], callbacks: List[Any] = []):
self.stop = stop
self.model = model
litellm.callbacks = callbacks
litellm.set_verbose = True
def call(self, messages: List[Dict[str, str]]) -> Dict[str, Any]:
response = completion(
stop=self.stop, model=self.model, messages=messages, num_retries=5
)
return response["choices"][0]["message"]["content"]
def _call_callbacks(self, formatted_answer):
for callback in self.callbacks:
callback(formatted_answer)

95
src/crewai/lol.py Normal file
View File

@@ -0,0 +1,95 @@
from typing import Callable, List, Dict, Any
from functools import wraps
class Flow:
def __init__(self):
self._start_method = None
self._listeners: Dict[str, List[str]] = {}
self._methods: Dict[str, Callable] = {}
def run(self):
if not self._start_method:
raise ValueError("No start method defined")
result = self._methods[self._start_method](self)
self._execute_listeners(self._start_method, result)
def _execute_listeners(self, trigger_method: str, result: Any):
if trigger_method in self._listeners:
for listener in self._listeners[trigger_method]:
listener_result = self._methods[listener](self, result)
self._execute_listeners(listener, listener_result)
def start():
def decorator(func):
@wraps(func)
def wrapper(self, *args, **kwargs):
if not self._start_method:
self._start_method = func.__name__
return func(self, *args, **kwargs)
return wrapper
return decorator
def listen(*trigger_methods):
def decorator(func):
@wraps(func)
def wrapper(self, *args, **kwargs):
for trigger in trigger_methods:
trigger_name = trigger.__name__ if callable(trigger) else trigger
if trigger_name not in self._listeners:
self._listeners[trigger_name] = []
self._listeners[trigger_name].append(func.__name__)
return func(self, *args, **kwargs)
return wrapper
return decorator
class FlowMeta(type):
def __new__(mcs, name, bases, attrs):
new_cls = super().__new__(mcs, name, bases, attrs)
for name, method in attrs.items():
if hasattr(method, "_is_start"):
new_cls._start_method = name
if hasattr(method, "_listeners"):
for trigger in method._listeners:
if trigger not in new_cls._listeners:
new_cls._listeners[trigger] = []
new_cls._listeners[trigger].append(name)
new_cls._methods[name] = method
return new_cls
class BaseFlow(Flow, metaclass=FlowMeta):
_start_method = None
_listeners = {}
_methods = {}
# Example usage:
class ExampleFlow(BaseFlow):
@start()
def start_method(self):
print("Starting the flow")
return "Start result"
@listen(start_method)
def second_method(self, result):
print(f"Second method, received: {result}")
return "Second result"
@listen(second_method)
def third_method(self, result):
print(f"Third method, received: {result}")
return "Third result"
# Run the flow
flow = ExampleFlow()
flow.run()

42
src/crewai/telemetry/telemetry.py
View File

@@ -102,14 +102,8 @@ class Telemetry:
"max_iter": agent.max_iter,
"max_rpm": agent.max_rpm,
"i18n": agent.i18n.prompt_file,
"function_calling_llm": json.dumps(
self._safe_llm_attributes(
agent.function_calling_llm
)
),
"llm": json.dumps(
self._safe_llm_attributes(agent.llm)
),
"function_calling_llm": agent.function_calling_llm,
"llm": agent.llm,
"delegation_enabled?": agent.allow_delegation,
"allow_code_execution?": agent.allow_code_execution,
"max_retry_limit": agent.max_retry_limit,
@@ -173,14 +167,8 @@ class Telemetry:
"verbose?": agent.verbose,
"max_iter": agent.max_iter,
"max_rpm": agent.max_rpm,
"function_calling_llm": json.dumps(
self._safe_llm_attributes(
agent.function_calling_llm
)
),
"llm": json.dumps(
self._safe_llm_attributes(agent.llm)
),
"function_calling_llm": agent.function_calling_llm,
"llm": agent.llm,
"delegation_enabled?": agent.allow_delegation,
"allow_code_execution?": agent.allow_code_execution,
"max_retry_limit": agent.max_retry_limit,
@@ -294,9 +282,7 @@ class Telemetry:
self._add_attribute(span, "tool_name", tool_name)
self._add_attribute(span, "attempts", attempts)
if llm:
self._add_attribute(
span, "llm", json.dumps(self._safe_llm_attributes(llm))
)
self._add_attribute(span, "llm", llm)
span.set_status(Status(StatusCode.OK))
span.end()
except Exception:
@@ -316,9 +302,7 @@ class Telemetry:
self._add_attribute(span, "tool_name", tool_name)
self._add_attribute(span, "attempts", attempts)
if llm:
self._add_attribute(
span, "llm", json.dumps(self._safe_llm_attributes(llm))
)
self._add_attribute(span, "llm", llm)
span.set_status(Status(StatusCode.OK))
span.end()
except Exception:
@@ -336,9 +320,7 @@ class Telemetry:
pkg_resources.get_distribution("crewai").version,
)
if llm:
self._add_attribute(
span, "llm", json.dumps(self._safe_llm_attributes(llm))
)
self._add_attribute(span, "llm", llm)
span.set_status(Status(StatusCode.OK))
span.end()
except Exception:
@@ -491,7 +473,7 @@ class Telemetry:
"max_iter": agent.max_iter,
"max_rpm": agent.max_rpm,
"i18n": agent.i18n.prompt_file,
"llm": json.dumps(self._safe_llm_attributes(agent.llm)),
"llm": agent.llm,
"delegation_enabled?": agent.allow_delegation,
"tools_names": [
tool.name.casefold() for tool in agent.tools or []
@@ -567,11 +549,3 @@ class Telemetry:
return span.set_attribute(key, value)
except Exception:
pass
def _safe_llm_attributes(self, llm):
attributes = ["name", "model_name", "model", "top_k", "temperature"]
if llm:
safe_attributes = {k: v for k, v in vars(llm).items() if k in attributes}
safe_attributes["class"] = llm.__class__.__name__
return safe_attributes
return {}

28
src/crewai/tools/tool_usage.py
View File

@@ -4,9 +4,6 @@ from difflib import SequenceMatcher
from textwrap import dedent
from typing import Any, List, Union
from langchain_core.tools import BaseTool
from langchain_openai import ChatOpenAI
from crewai.agents.tools_handler import ToolsHandler
from crewai.task import Task
from crewai.telemetry import Telemetry
@@ -20,7 +17,7 @@ if os.environ.get("AGENTOPS_API_KEY"):
except ImportError:
pass
OPENAI_BIGGER_MODELS = ["gpt-4o"]
OPENAI_BIGGER_MODELS = ["gpt-4", "gpt-4o"]
class ToolUsageErrorException(Exception):
@@ -48,7 +45,7 @@ class ToolUsage:
def __init__(
self,
tools_handler: ToolsHandler,
tools: List[BaseTool],
tools: List[Any],
original_tools: List[Any],
tools_description: str,
tools_names: str,
@@ -73,18 +70,9 @@ class ToolUsage:
self.action = action
self.function_calling_llm = function_calling_llm
# Handling bug (see https://github.com/langchain-ai/langchain/pull/16395): raise an error if tools_names have space for ChatOpenAI
if isinstance(self.function_calling_llm, ChatOpenAI):
if " " in self.tools_names:
raise Exception(
"Tools names should not have spaces for ChatOpenAI models."
)
# Set the maximum parsing attempts for bigger models
if (isinstance(self.function_calling_llm, ChatOpenAI)) and (
self.function_calling_llm.openai_api_base is None
):
if self.function_calling_llm.model_name in OPENAI_BIGGER_MODELS:
if self._is_gpt(self.function_calling_llm) and "4" in self.function_calling_llm:
if self.function_calling_llm in OPENAI_BIGGER_MODELS:
self._max_parsing_attempts = 2
self._remember_format_after_usages = 4
@@ -116,7 +104,7 @@ class ToolUsage:
def _use(
self,
tool_string: str,
tool: BaseTool,
tool: Any,
calling: Union[ToolCalling, InstructorToolCalling],
) -> str: # TODO: Fix this return type
tool_event = agentops.ToolEvent(name=calling.tool_name) if agentops else None # type: ignore
@@ -265,7 +253,7 @@ class ToolUsage:
calling.arguments == last_tool_usage.arguments
)
def _select_tool(self, tool_name: str) -> BaseTool:
def _select_tool(self, tool_name: str) -> Any:
order_tools = sorted(
self.tools,
key=lambda tool: SequenceMatcher(
@@ -285,7 +273,7 @@ class ToolUsage:
self.task.increment_tools_errors()
if tool_name and tool_name != "":
raise Exception(
f"Action '{tool_name}' don't exist, these are the only available Actions:\n {self.tools_description}"
f"Action '{tool_name}' don't exist, these are the only available Actions:\n{self.tools_description}"
)
else:
raise Exception(
@@ -312,7 +300,7 @@ class ToolUsage:
return "\n--\n".join(descriptions)
def _is_gpt(self, llm) -> bool:
return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
return False if not llm else "gpt" in llm.lower()
def _tool_calling(
self, tool_string: str

9
src/crewai/translations/en.json
View File

@@ -10,17 +10,18 @@
"memory": "\n\n# Useful context: \n{memory}",
"role_playing": "You are {role}. {backstory}\nYour personal goal is: {goal}",
"tools": "\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple python dictionary, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all necessary information is gathered:\n\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n",
"no_tools": "To 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!",
"format": "I MUST either use a tool (use one at time) OR give my best final answer. To Use the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action, dictionary enclosed in curly braces\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\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\n ",
"no_tools": "\nTo 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: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!",
"format": "I MUST either use a tool (use one at time) OR give my best final answer. To Use the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action, dictionary enclosed in curly braces\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n ",
"final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfy the expect criteria, use the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n",
"format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\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\n",
"format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n",
"task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
"expected_output": "\nThis is the expect criteria for your final answer: {expected_output} \n you MUST return the actual complete content as the final answer, not a summary.",
"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}\nPlease provide feedback: "
},
"errors": {
"force_final_answer": "Tool won't be use because it's time to give your final answer. Don't use tools and just your absolute BEST Final answer.",
"force_final_answer_error": "I can't keep going, this was the best I 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",
"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}",

43
src/crewai/utilities/converter.py
View File

@@ -2,8 +2,7 @@ import json
import re
from typing import Any, Optional, Type, Union
from langchain.schema import HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI
from crewai.llm import LLM
from pydantic import BaseModel, ValidationError
from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
@@ -28,7 +27,12 @@ class Converter(OutputConverter):
if self.is_gpt:
return self._create_instructor().to_pydantic()
else:
return self._create_chain().invoke({})
return LLM(model=self.llm).call(
[
{"role": "system", "content": self.instructions},
{"role": "user", "content": self.text},
]
)
except Exception as e:
if current_attempt < self.max_attempts:
return self.to_pydantic(current_attempt + 1)
@@ -42,7 +46,14 @@ class Converter(OutputConverter):
if self.is_gpt:
return self._create_instructor().to_json()
else:
return json.dumps(self._create_chain().invoke({}).model_dump())
return json.dumps(
LLM(model=self.llm).call(
[
{"role": "system", "content": self.instructions},
{"role": "user", "content": self.text},
]
)
)
except Exception as e:
if current_attempt < self.max_attempts:
return self.to_json(current_attempt + 1)
@@ -61,22 +72,25 @@ class Converter(OutputConverter):
)
return inst
def _create_chain(self):
def _convert_with_instructions(self):
"""Create a chain."""
from crewai.utilities.crew_pydantic_output_parser import (
CrewPydanticOutputParser,
)
parser = CrewPydanticOutputParser(pydantic_object=self.model)
new_prompt = SystemMessage(content=self.instructions) + HumanMessage(
content=self.text
result = LLM(model=self.llm).call(
[
{"role": "system", "content": self.instructions},
{"role": "user", "content": self.text},
]
)
return new_prompt | self.llm | parser
return parser.parse_result(result)
@property
def is_gpt(self) -> bool:
"""Return if llm provided is of gpt from openai."""
return isinstance(self.llm, ChatOpenAI) and self.llm.openai_api_base is None
return "gpt" in str(self.llm).lower()
def convert_to_model(
@@ -87,9 +101,9 @@ def convert_to_model(
converter_cls: Optional[Type[Converter]] = None,
) -> Union[dict, BaseModel, str]:
model = output_pydantic or output_json
if model is None:
return result
try:
escaped_result = json.dumps(json.loads(result, strict=False))
return validate_model(escaped_result, model, bool(output_json))
@@ -140,11 +154,8 @@ def handle_partial_json(
if is_json_output:
return exported_result.model_dump()
return exported_result
except json.JSONDecodeError as e:
Printer().print(
content=f"Error parsing JSON: {e}. The extracted JSON-like string is not valid JSON. Attempting alternative conversion method.",
color="yellow",
)
except json.JSONDecodeError:
pass
except ValidationError as e:
Printer().print(
content=f"Pydantic validation error: {e}. The JSON structure doesn't match the expected model. Attempting alternative conversion method.",
@@ -170,7 +181,6 @@ def convert_with_instructions(
) -> Union[dict, BaseModel, str]:
llm = agent.function_calling_llm or agent.llm
instructions = get_conversion_instructions(model, llm)
converter = create_converter(
agent=agent,
converter_cls=converter_cls,
@@ -179,6 +189,7 @@ def convert_with_instructions(
model=model,
instructions=instructions,
)
exported_result = (
converter.to_pydantic() if not is_json_output else converter.to_json()
)

20
src/crewai/utilities/crew_pydantic_output_parser.py
View File

@@ -1,33 +1,31 @@
import json
from typing import Any, List, Type
import regex
from langchain.output_parsers import PydanticOutputParser
from langchain_core.exceptions import OutputParserException
from langchain_core.outputs import Generation
from typing import Any, Type
from crewai.agents.parser import OutputParserException
from pydantic import BaseModel, ValidationError
class CrewPydanticOutputParser(PydanticOutputParser):
class CrewPydanticOutputParser:
"""Parses the text into pydantic models"""
pydantic_object: Type[BaseModel]
def parse_result(self, result: List[Generation]) -> Any:
result[0].text = self._transform_in_valid_json(result[0].text)
def parse_result(self, result: str) -> Any:
result = self._transform_in_valid_json(result)
# Treating edge case of function calling llm returning the name instead of tool_name
json_object = json.loads(result[0].text)
json_object = json.loads(result)
if "tool_name" not in json_object:
json_object["tool_name"] = json_object.get("name", "")
result[0].text = json.dumps(json_object)
result = json.dumps(json_object)
try:
return self.pydantic_object.model_validate(json_object)
except ValidationError as e:
name = self.pydantic_object.__name__
msg = f"Failed to parse {name} from completion {json_object}. Got: {e}"
raise OutputParserException(msg, llm_output=json_object)
raise OutputParserException(error=msg)
def _transform_in_valid_json(self, text) -> str:
text = text.replace("```", "").replace("json", "")

5
src/crewai/utilities/evaluators/task_evaluator.py
View File

@@ -1,7 +1,6 @@
import os
from typing import List
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field
from crewai.utilities import Converter
@@ -84,7 +83,7 @@ class TaskEvaluator:
instructions = f"{instructions}\n\nReturn only valid JSON with the following schema:\n```json\n{model_schema}\n```"
converter = Converter(
llm=self.llm,
llm=self.function_calling_llm,
text=evaluation_query,
model=TaskEvaluation,
instructions=instructions,
@@ -93,7 +92,7 @@ class TaskEvaluator:
return converter.to_pydantic()
def _is_gpt(self, llm) -> bool:
return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
return "gpt" in str(self.llm).lower()
def evaluate_training_data(
self, training_data: dict, agent_id: str

9
src/crewai/utilities/instructor.py
View File

@@ -1,6 +1,7 @@
from typing import Any, Optional, Type
import instructor
from litellm import completion
from pydantic import BaseModel, Field, PrivateAttr, model_validator
@@ -12,7 +13,7 @@ class Instructor(BaseModel):
agent: Optional[Any] = Field(
description="The agent that needs to use instructor.", default=None
)
llm: Optional[Any] = Field(
llm: Optional[str] = Field(
description="The agent that needs to use instructor.", default=None
)
instructions: Optional[str] = Field(
@@ -29,8 +30,8 @@ class Instructor(BaseModel):
if self.agent and not self.llm:
self.llm = self.agent.function_calling_llm or self.agent.llm
self._client = instructor.patch(
self.llm.client._client,
self._client = instructor.from_litellm(
completion,
mode=instructor.Mode.TOOLS,
)
return self
@@ -45,6 +46,6 @@ class Instructor(BaseModel):
messages.append({"role": "system", "content": self.instructions})
model = self._client.chat.completions.create(
model=self.llm.model_name, response_model=self.model, messages=messages
model=self.llm, response_model=self.model, messages=messages
)
return model

25
src/crewai/utilities/prompts.py
View File

@@ -1,8 +1,5 @@
from typing import Any, ClassVar, Optional
from langchain.prompts import BasePromptTemplate, PromptTemplate
from pydantic import BaseModel, Field
from typing import Any, Optional
from crewai.utilities import I18N
@@ -14,9 +11,9 @@ class Prompts(BaseModel):
system_template: Optional[str] = None
prompt_template: Optional[str] = None
response_template: Optional[str] = None
SCRATCHPAD_SLICE: ClassVar[str] = "\n{agent_scratchpad}"
agent: Any
def task_execution(self) -> BasePromptTemplate:
def task_execution(self) -> str:
"""Generate a standard prompt for task execution."""
slices = ["role_playing"]
if len(self.tools) > 0:
@@ -42,12 +39,11 @@ class Prompts(BaseModel):
system_template=None,
prompt_template=None,
response_template=None,
) -> BasePromptTemplate:
) -> str:
"""Constructs a prompt string from specified components."""
if not system_template and not prompt_template:
prompt_parts = [self.i18n.slice(component) for component in components]
prompt_parts.append(self.SCRATCHPAD_SLICE)
prompt = PromptTemplate.from_template("".join(prompt_parts))
prompt = "".join(prompt_parts)
else:
prompt_parts = [
self.i18n.slice(component)
@@ -56,9 +52,14 @@ class Prompts(BaseModel):
]
system = system_template.replace("{{ .System }}", "".join(prompt_parts))
prompt = prompt_template.replace(
"{{ .Prompt }}",
"".join([self.i18n.slice("task"), self.SCRATCHPAD_SLICE]),
"{{ .Prompt }}", "".join(self.i18n.slice("task"))
)
response = response_template.split("{{ .Response }}")[0]
prompt = PromptTemplate.from_template(f"{system}\n{prompt}\n{response}")
prompt = f"{system}\n{prompt}\n{response}"
prompt = (
prompt.replace("{goal}", self.agent.goal)
.replace("{role}", self.agent.role)
.replace("{backstory}", self.agent.backstory)
)
return prompt

38
src/crewai/utilities/token_counter_callback.py
View File

@@ -1,36 +1,20 @@
from typing import Any, Dict, List
import tiktoken
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import LLMResult
from litellm.integrations.custom_logger import CustomLogger
from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
class TokenCalcHandler(BaseCallbackHandler):
model_name: str = ""
token_cost_process: TokenProcess
encoding: tiktoken.Encoding
def __init__(self, model_name, token_cost_process):
self.model_name = model_name
class TokenCalcHandler(CustomLogger):
def __init__(self, token_cost_process: TokenProcess):
self.token_cost_process = token_cost_process
try:
self.encoding = tiktoken.encoding_for_model(self.model_name)
except KeyError:
self.encoding = tiktoken.get_encoding("cl100k_base")
def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
def log_success_event(self, kwargs, response_obj, start_time, end_time):
if self.token_cost_process is None:
return
for prompt in prompts:
self.token_cost_process.sum_prompt_tokens(len(self.encoding.encode(prompt)))
async def on_llm_new_token(self, token: str, **kwargs) -> None:
self.token_cost_process.sum_completion_tokens(1)
def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
self.token_cost_process.sum_successful_requests(1)
print("*** response_obj", response_obj)
self.token_cost_process.sum_prompt_tokens(
response_obj["usage"]["prompt_tokens"]
)
self.token_cost_process.sum_completion_tokens(
response_obj["usage"]["completion_tokens"]
)

216
tests/agent_test.py
View File

@@ -6,14 +6,15 @@ from unittest.mock import patch
import pytest
from crewai import Agent, Crew, Task
from crewai.agents.cache import CacheHandler
from crewai.agents.executor import CrewAgentExecutor
from crewai.agents.parser import CrewAgentParser
from crewai.agents.crew_agent_executor import CrewAgentExecutor
from crewai.llm import LLM
from crewai.agents.parser import CrewAgentParser, OutputParserException
from crewai.tools.tool_calling import InstructorToolCalling
from crewai.tools.tool_usage import ToolUsage
from crewai.utilities import RPMController
from langchain.schema import AgentAction
from langchain.tools import tool
from langchain_core.exceptions import OutputParserException
from crewai_tools import tool
from langchain_openai import ChatOpenAI
@@ -28,15 +29,18 @@ def test_agent_creation():
def test_agent_default_values():
agent = Agent(role="test role", goal="test goal", backstory="test backstory")
assert isinstance(agent.llm, ChatOpenAI)
assert agent.llm.model_name == "gpt-4o"
assert agent.llm.temperature == 0.7
assert agent.llm.verbose is False
assert agent.llm == "gpt-4o"
assert agent.allow_delegation is True
def test_custom_llm():
agent = Agent(
role="test role", goal="test goal", backstory="test backstory", llm="gpt-4"
)
assert agent.llm == "gpt-4"
def test_custom_llm_with_langchain():
agent = Agent(
role="test role",
goal="test goal",
@@ -44,9 +48,7 @@ def test_custom_llm():
llm=ChatOpenAI(temperature=0, model="gpt-4"),
)
assert isinstance(agent.llm, ChatOpenAI)
assert agent.llm.model_name == "gpt-4"
assert agent.llm.temperature == 0
assert agent.llm == "gpt-4"
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -65,7 +67,7 @@ def test_agent_execution():
)
output = agent.execute_task(task)
assert output == "The result of the math operation 1 + 1 is 2."
assert output == "The result of the math operation is 2."
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -89,7 +91,7 @@ def test_agent_execution_with_tools():
expected_output="The result of the multiplication.",
)
output = agent.execute_task(task)
assert output == "The result of 3 times 4 is 12."
assert output == "12"
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -274,13 +276,13 @@ def test_agent_execution_with_specific_tools():
expected_output="The result of the multiplication.",
)
output = agent.execute_task(task=task, tools=[multiplier])
assert output == "The result of the multiplication is 12."
assert output == "12"
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_custom_max_iterations():
@tool
def get_final_answer(numbers) -> float:
def get_final_answer() -> float:
"""Get the final answer but don't give it yet, just re-use this
tool non-stop."""
return 42
@@ -294,7 +296,7 @@ def test_agent_custom_max_iterations():
)
with patch.object(
CrewAgentExecutor, "_iter_next_step", wraps=agent.agent_executor._iter_next_step
LLM, "call", wraps=LLM("gpt-4o", stop=["\nObservation"]).call
) as private_mock:
task = Task(
description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
@@ -304,7 +306,7 @@ def test_agent_custom_max_iterations():
task=task,
tools=[get_final_answer],
)
private_mock.assert_called_once()
assert private_mock.call_count == 2
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -320,7 +322,7 @@ def test_agent_repeated_tool_usage(capsys):
goal="test goal",
backstory="test backstory",
max_iter=4,
llm=ChatOpenAI(model="gpt-4"),
llm="gpt-4",
allow_delegation=False,
verbose=True,
)
@@ -357,7 +359,7 @@ def test_agent_repeated_tool_usage_check_even_with_disabled_cache(capsys):
goal="test goal",
backstory="test backstory",
max_iter=4,
llm=ChatOpenAI(model="gpt-4"),
llm="gpt-4",
allow_delegation=False,
verbose=True,
cache=False,
@@ -404,13 +406,13 @@ def test_agent_moved_on_after_max_iterations():
task=task,
tools=[get_final_answer],
)
assert output == "42"
assert output == "The final answer is 42."
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_respect_the_max_rpm_set(capsys):
@tool
def get_final_answer(anything: str) -> float:
def get_final_answer() -> float:
"""Get the final answer but don't give it yet, just re-use this
tool non-stop."""
return 42
@@ -441,14 +443,13 @@ def test_agent_respect_the_max_rpm_set(capsys):
moveon.assert_called()
@pytest.mark.vcr(filter_headers=["authorization"])
# @pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
from unittest.mock import patch
from langchain.tools import tool
from crewai_tools import tool
@tool
def get_final_answer(numbers) -> float:
def get_final_answer() -> float:
"""Get the final answer but don't give it yet, just re-use this
tool non-stop."""
return 42
@@ -475,6 +476,7 @@ def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
moveon.return_value = True
crew.kickoff()
captured = capsys.readouterr()
print("captured.out", captured.out)
assert "Max RPM reached, waiting for next minute to start." not in captured.out
moveon.assert_not_called()
@@ -483,10 +485,10 @@ def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
def test_agent_without_max_rpm_respet_crew_rpm(capsys):
from unittest.mock import patch
from langchain.tools import tool
from crewai_tools import tool
@tool
def get_final_answer(numbers) -> float:
def get_final_answer() -> float:
"""Get the final answer but don't give it yet, just re-use this
tool non-stop."""
return 42
@@ -504,7 +506,7 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
role="test role2",
goal="test goal2",
backstory="test backstory2",
max_iter=2,
max_iter=1,
verbose=True,
allow_delegation=False,
)
@@ -535,8 +537,7 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_error_on_parsing_tool(capsys):
from unittest.mock import patch
from langchain.tools import tool
from crewai_tools import tool
@tool
def get_final_answer() -> float:
@@ -577,10 +578,10 @@ def test_agent_error_on_parsing_tool(capsys):
def test_agent_remembers_output_format_after_using_tools_too_many_times():
from unittest.mock import patch
from langchain.tools import tool
from crewai_tools import tool
@tool
def get_final_answer(anything: str) -> float:
def get_final_answer() -> float:
"""Get the final answer but don't give it yet, just re-use this
tool non-stop."""
return 42
@@ -631,7 +632,7 @@ def test_agent_use_specific_tasks_output_as_context(capsys):
crew = Crew(agents=[agent1, agent2], tasks=tasks)
result = crew.kickoff()
print("LOWER RESULT", result.raw)
assert "bye" not in result.raw.lower()
assert "hi" in result.raw.lower() or "hello" in result.raw.lower()
@@ -645,7 +646,7 @@ def test_agent_step_callback():
with patch.object(StepCallback, "callback") as callback:
@tool
def learn_about_AI(topic) -> str:
def learn_about_AI() -> str:
"""Useful for when you need to learn about AI to write an paragraph about it."""
return "AI is a very broad field."
@@ -672,36 +673,39 @@ def test_agent_step_callback():
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_function_calling_llm():
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
with patch.object(llm.client, "create", wraps=llm.client.create) as private_mock:
@tool
def learn_about_AI(topic) -> str:
"""Useful for when you need to learn about AI to write an paragraph about it."""
return "AI is a very broad field."
@tool
def learn_about_AI(topic) -> str:
"""Useful for when you need to learn about AI to write an paragraph about it."""
return "AI is a very broad field."
agent1 = Agent(
role="test role",
goal="test goal",
backstory="test backstory",
tools=[learn_about_AI],
llm=ChatOpenAI(model="gpt-4-0125-preview"),
function_calling_llm=llm,
)
agent1 = Agent(
role="test role",
goal="test goal",
backstory="test backstory",
tools=[learn_about_AI],
llm=ChatOpenAI(model="gpt-4-0125-preview"),
function_calling_llm=llm,
)
essay = Task(
description="Write and then review an small paragraph on AI until it's AMAZING",
expected_output="The final paragraph.",
agent=agent1,
)
tasks = [essay]
crew = Crew(agents=[agent1], tasks=tasks)
essay = Task(
description="Write and then review an small paragraph on AI until it's AMAZING",
expected_output="The final paragraph.",
agent=agent1,
)
tasks = [essay]
crew = Crew(agents=[agent1], tasks=tasks)
from crewai.utilities import Instructor
with patch.object(Instructor, "__init__", return_value=None) as mock_instructor:
crew.kickoff()
private_mock.assert_called()
mock_instructor.assert_called()
calls = mock_instructor.call_args_list
assert any(
call.kwargs.get("llm") == "gpt-3.5-turbo-0125" for call in calls
), "Instructor was not created with the expected model"
def test_agent_count_formatting_error():
@@ -714,8 +718,7 @@ def test_agent_count_formatting_error():
verbose=True,
)
parser = CrewAgentParser()
parser.agent = agent1
parser = CrewAgentParser(agent=agent1)
with patch.object(Agent, "increment_formatting_errors") as mock_count_errors:
test_text = "This text does not match expected formats."
@@ -792,22 +795,6 @@ def test_tool_usage_information_is_appended_to_agent():
]
def test_agent_llm_uses_token_calc_handler_with_llm_has_model_name():
agent1 = Agent(
role="test role",
goal="test goal",
backstory="test backstory",
verbose=True,
)
assert len(agent1.llm.callbacks) == 1
assert agent1.llm.callbacks[0].__class__.__name__ == "TokenCalcHandler"
assert agent1.llm.callbacks[0].model_name == "gpt-4o"
assert (
agent1.llm.callbacks[0].token_cost_process.__class__.__name__ == "TokenProcess"
)
def test_agent_definition_based_on_dict():
config = {
"role": "test role",
@@ -846,7 +833,7 @@ def test_agent_human_input():
)
with patch.object(CrewAgentExecutor, "_ask_human_input") as mock_human_input:
mock_human_input.return_value = "Hello"
mock_human_input.return_value = "Don't say hi, say Hello instead!"
output = agent.execute_task(task)
mock_human_input.assert_called_once()
assert output == "Hello"
@@ -886,13 +873,11 @@ def test_system_and_prompt_template():
{{ .Response }}<|eot_id|>""",
)
template = agent.agent_executor.agent.dict()["runnable"]["middle"][0]["template"]
assert (
template
== """<|start_header_id|>system<|end_header_id|>
expected_prompt = """<|start_header_id|>system<|end_header_id|>
You are {role}. {backstory}
Your personal goal is: {goal}To give my best complete final answer to the task use the exact following format:
Your personal goal is: {goal}
To give my best complete final answer to the task use the exact following format:
Thought: I now can give a great answer
Final Answer: my best complete final answer to the task.
@@ -906,12 +891,22 @@ Current Task: {input}
Begin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!
Thought:
{agent_scratchpad}<|eot_id|>
Thought:<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
"""
)
with patch.object(CrewAgentExecutor, "_format_prompt") as mock_format_prompt:
mock_format_prompt.return_value = expected_prompt
# Trigger the _format_prompt method
agent.agent_executor._format_prompt("dummy_prompt", {})
# Assert that _format_prompt was called
mock_format_prompt.assert_called_once()
# Assert that the returned prompt matches the expected prompt
assert mock_format_prompt.return_value == expected_prompt
@patch("crewai.agent.CrewTrainingHandler")
@@ -1003,6 +998,7 @@ def test_agent_max_retry_limit():
"input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi \n you MUST return the actual complete content as the final answer, not a summary.",
"tool_names": "",
"tools": "",
"should_ask_for_human_input": True,
}
),
mock.call(
@@ -1010,6 +1006,7 @@ def test_agent_max_retry_limit():
"input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi \n you MUST return the actual complete content as the final answer, not a summary.",
"tool_names": "",
"tools": "",
"should_ask_for_human_input": True,
}
),
]
@@ -1028,7 +1025,7 @@ def test_handle_context_length_exceeds_limit():
)
with patch.object(
CrewAgentExecutor, "_iter_next_step", wraps=agent.agent_executor._iter_next_step
CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke
) as private_mock:
task = Task(
description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
@@ -1038,25 +1035,23 @@ def test_handle_context_length_exceeds_limit():
task=task,
)
private_mock.assert_called_once()
with patch("crewai.agents.executor.click") as mock_prompt:
mock_prompt.return_value = "y"
with patch.object(
CrewAgentExecutor, "_handle_context_length"
) as mock_handle_context:
mock_handle_context.side_effect = ValueError(
"Context length limit exceeded"
with patch.object(
CrewAgentExecutor, "_handle_context_length"
) as mock_handle_context:
mock_handle_context.side_effect = ValueError(
"Context length limit exceeded"
)
long_input = "This is a very long input. " * 10000
# Attempt to handle context length, expecting the mocked error
with pytest.raises(ValueError) as excinfo:
agent.agent_executor._handle_context_length(
[(original_action, long_input)]
)
long_input = "This is a very long input. " * 10000
# Attempt to handle context length, expecting the mocked error
with pytest.raises(ValueError) as excinfo:
agent.agent_executor._handle_context_length(
[(original_action, long_input)]
)
assert "Context length limit exceeded" in str(excinfo.value)
mock_handle_context.assert_called_once()
assert "Context length limit exceeded" in str(excinfo.value)
mock_handle_context.assert_called_once()
@pytest.mark.vcr(filter_headers=["authorization"])
@@ -1065,11 +1060,12 @@ def test_handle_context_length_exceeds_limit_cli_no():
role="test role",
goal="test goal",
backstory="test backstory",
sliding_context_window=False,
)
task = Task(description="test task", agent=agent, expected_output="test output")
with patch.object(
CrewAgentExecutor, "_iter_next_step", wraps=agent.agent_executor._iter_next_step
CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke
) as private_mock:
task = Task(
description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
@@ -1079,10 +1075,8 @@ def test_handle_context_length_exceeds_limit_cli_no():
task=task,
)
private_mock.assert_called_once()
with patch("crewai.agents.executor.click") as mock_prompt:
mock_prompt.return_value = "n"
pytest.raises(SystemExit)
with patch.object(
CrewAgentExecutor, "_handle_context_length"
) as mock_handle_context:
mock_handle_context.assert_not_called()
pytest.raises(SystemExit)
with patch.object(
CrewAgentExecutor, "_handle_context_length"
) as mock_handle_context:
mock_handle_context.assert_not_called()

12
tests/agent_tools/agent_tools_test.py
View File

@@ -24,7 +24,7 @@ def test_delegate_work():
assert (
result
== "As a researcher, my opinions are based on facts and extensive study. Regarding AI Agents, they are a fundamental part of the advancement in technology. AI agents are essentially the entities that perceive their environment and take actions to maximize their chances of success. They have a wide range of applications from self-driving cars to intelligent personal assistants like Siri and Alexa. They have the potential to greatly improve our lives by automating mundane tasks, helping us make better decisions, and even potentially solving complex problems. However, like any technology, they have their own set of challenges such as the risk of job displacement and the ethical implications of their use. My goal as a researcher is not to love or hate AI agents, but to understand them, their benefits, and their implications. It's about maintaining an objective view in order to provide the most accurate and comprehensive analysis."
== "AI Agents, sometimes known as intelligent agents, represent a groundbreaking shift in the realm of artificial intelligence and automation. These entities operate by perceiving their environment through sensors and acting upon that environment using actuators, while following a specific set of rules or algorithms to achieve designated goals. AI agents can be categorized based on their complexity and autonomy, ranging from simple reactive systems to more sophisticated, learning-based or cognitive agents.\n\nFirstly, it's important to clarify that while I might have expressed concerns about certain implementations of AI agents, it doesn't equate to hating them. My apprehension often stems from the ethical and societal implications, rather than the technology itself. Let's dive into the detailed facets that make AI agents both fascinating and somewhat contentious:\n\n1. **Types of AI Agents**:\n - **Reactive Agents**: These agents do not store past states and act only on current perceptions. An example would be a chatbot that provides information based on pre-set responses.\n - **Deliberative Agents**: They have an internal model of the world and can deliberate on actions to take. This might include AI used in autonomous vehicles which plan a route by assessing various factors.\n - **Learning Agents**: These agents improve their performance over time by learning from interactions. Machine learning algorithms in recommendation systems (like those used by Netflix) fall into this category.\n - **Cognitive Agents**: The most advanced, these mimic human-like understanding and reasoning. An example is IBM’s Watson, which can understand natural language and provide insights across various domains.\n\n2. **Applications of AI Agents**:\n - **Healthcare**: AI agents monitor patient vitals, assist in diagnostics, and personalize treatment plans.\n - **Finance**: Automated trading systems and fraud detection are driven by sophisticated AI agents.\n - **Customer Service**: Virtual assistants and AI-powered chatbots enhance user experience by resolving queries quickly.\n - **Autonomous Vehicles**: Self-driving cars rely on AI agents to navigate and make real-time decisions.\n\n3. **Challenges**:\n - **Ethical Concerns**: Decisions made by AI agents can impact lives significantly. Ensuring fairness and accountability is paramount.\n - **Security**: AI systems can be vulnerable to adversarial attacks where malicious inputs lead to harmful outcomes.\n - **Bias**: AI agents can inherit biases present in their training data, leading to unfair or discriminatory outcomes.\n - **Job Displacement**: Automation driven by AI agents may lead to significant shifts in employment and require policy interventions.\n\n4. **Future Prospects**:\n - **Enhanced Decision Making**: With advances in AI, agents will become better at making nuanced decisions, balancing risk, and optimizing outcomes.\n - **Interdisciplinary Integrations**: AI agents will increasingly integrate insights from various fields, leading to more holistic solutions.\n - **Human-AI Collaboration**: The future will likely see improved synergies between humans and AI agents, amplifying human capabilities rather than replacing them.\n\nIn summary, while AI agents bring substantial advancements and efficiencies, they also pose significant challenges that need thoughtful navigation. Balancing the technological benefits with ethical considerations remains a central task for researchers, developers, and policymakers."
)
@@ -38,7 +38,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
assert (
result
== "AI Agents are essentially computer programs that are designed to perform tasks autonomously, with the ability to adapt and learn from their environment. These tasks range from simple ones such as setting alarms, to more complex ones like diagnosing diseases or driving cars. AI agents have the potential to revolutionize many industries, making processes more efficient and accurate. \n\nHowever, like any technology, AI agents have their downsides. They can be susceptible to biases based on the data they're trained on and they can also raise privacy concerns. Moreover, the widespread adoption of AI agents could result in significant job displacement in certain industries.\n\nDespite these concerns, it's important to note that the development and use of AI agents are heavily dependent on human decisions and policies. Therefore, the key to harnessing the benefits of AI agents while mitigating the risks lies in responsible and thoughtful development and implementation.\n\nWhether one 'loves' or 'hates' AI agents often comes down to individual perspectives and experiences. But as a researcher, it is my job to provide balanced and factual information, so I hope this explanation helps you understand better what AI Agents are and the implications they have."
== "When it comes to AI agents, my stance is more nuanced than simple hatred. AI agents, which are autonomous programs capable of performing tasks typically requiring human intervention, have vast potential to revolutionize industries and improve quality of life. However, this potential comes with both opportunities and challenges that must be carefully considered.\n\nFirstly, the primary advantage of AI agents lies in their ability to process and analyze vast amounts of data at speeds much greater than humans can achieve. This capability can optimize workflows, enhance decision-making processes, and lead to innovative solutions in fields such as healthcare, finance, and logistics. For example, AI agents can assist doctors by providing diagnostic suggestions based on pattern recognition in medical imaging, or they could help financial analysts by predicting market trends using real-time data analytics.\n\nMoreover, AI agents can handle repetitive and mundane tasks, freeing up human workers to focus on more complex and creative endeavors. This can improve job satisfaction and productivity, as well as drive economic growth by allowing for more innovation.\n\nHowever, there are significant concerns that cannot be overlooked. One major issue is the ethical implications surrounding AI agents. These include questions of accountability, transparency, and bias. Since AI systems learn from data, they can inadvertently perpetuate existing biases present in the data they are trained on, leading to unfair or discriminatory outcomes. For example, in hiring processes, an AI trained on biased historical data may unfairly favor certain demographics over others.\n\nAdditionally, there is the risk of job displacement. While AI agents can enhance productivity, they can also render certain job roles obsolete, leading to economic disruption and social challenges. This necessitates policies for workforce retraining and social safety nets to mitigate these impacts.\n\nPrivacy is another looming concern. AI agents often require access to vast amounts of personal data to function effectively, raising questions about how this data is stored, shared, and protected. Without robust safeguards, there is a risk of data breaches and misuse, which can have severe consequences for individuals' privacy and security.\n\nIn summary, AI agents represent a powerful tool that can drive significant advancements and efficiencies across various domains. However, their adoption must be approached with caution, considering the ethical, social, and privacy concerns they raise. By addressing these challenges proactively, we can harness the benefits of AI agents while minimizing their potential drawbacks.\n\nMy perspective on AI agents is thus balanced: while I recognize their enormous potential, I am also keenly aware of the serious challenges they present. This critical stance ensures that we can work towards responsible and beneficial integration of AI agents into our society."
)
@@ -52,7 +52,7 @@ def test_ask_question():
assert (
result
== "As an AI researcher, I don't have personal feelings or emotions like love or hate. However, I recognize the importance of AI Agents in today's technological landscape. They have the potential to greatly enhance our lives and make tasks more efficient. At the same time, it is crucial to consider the ethical implications and societal impacts that come with their use. My role is to provide objective research and analysis on these topics."
== "As an expert researcher specializing in technology, particularly artificial intelligence and AI agents, I can say that I do not hate AI agents. In fact, my professional stance towards AI and AI agents is driven by a keen interest and a deep appreciation for their potential and the transformative impact they can have on various sectors. AI agents can augment human capabilities, streamline processes, and bring about efficiencies that were previously unattainable. They are instrumental in advancing research, improving customer service, enhancing decision-making processes, and even contributing to groundbreaking innovations in healthcare, finance, and other critical industries. \n\nWhile it is important to approach the development and deployment of AI agents with a critical eye towards ethical considerations and potential biases, my work focuses on maximizing their benefits while mitigating any risks. Therefore, given my dedication to understanding and leveraging the power of AI, it would be more accurate to say that I have a deep professional respect and enthusiasm for AI agents rather than any form of disdain. My goal is to make the best research and analysis on the subject to continually improve AI technology and its applications."
)
@@ -66,7 +66,7 @@ def test_ask_question_with_wrong_co_worker_variable():
assert (
result
== "No, I don't hate AI agents. In fact, I find them quite fascinating. They are powerful tools that can greatly assist in various tasks, including my research. As a technology researcher, AI and AI agents are subjects of interest to me due to their potential in advancing our understanding and capabilities in various fields. My supposed love for them stems from this professional interest and the potential they hold."
== "In my role as a researcher specialized in technology, particularly in AI and AI agents, I approach these subjects with an objective and analytical mindset rather than with emotions like love or hate. Advanced AI agents are remarkable tools that can significantly enhance productivity, problem-solving, and innovation across various fields. They have the potential to democratize access to knowledge, automate repetitive tasks, and provide insights that might not be readily apparent to human analysts.\n\nAI agents, when designed and utilized ethically, can bring about tremendous positive changes. They can improve healthcare outcomes through predictive analytics, optimize supply chains in business, enhance personalized education, and even assist in environmental monitoring and conservation efforts. The sophistication of these technologies often inspires admiration and respect for the expertise and ingenuity that goes into their creation.\n\nHowever, it is equally important to acknowledge and critically assess the challenges and ethical considerations associated with AI agents. Issues such as privacy, bias, accountability, and the potential for job displacement must be thoughtfully addressed. Comprehensive research and continuous dialogue among technologists, policymakers, and society at large are essential to navigating these complexities and ensuring that AI agents are developed and deployed in ways that are fair, transparent, and beneficial to all.\n\nTherefore, my stance is neither of love nor hate but one of a cautious optimist and a diligent researcher, dedicated to exploring both the potential and the pitfalls of AI agents. It is this balanced perspective that drives me to make thorough and impactful analyses in my work."
)
@@ -80,7 +80,7 @@ def test_delegate_work_withwith_coworker_as_array():
assert (
result
== "AI Agents are software entities which operate in an environment to achieve a particular goal. They can perceive their environment, reason about it, and take actions to fulfill their objectives. This includes everything from chatbots to self-driving cars. They are designed to act autonomously to a certain extent and are capable of learning from their experiences to improve their performance over time.\n\nDespite some people's fears or dislikes, AI Agents are not inherently good or bad. They are tools, and like any tool, their value depends on how they are used. For instance, AI Agents can be used to automate repetitive tasks, provide customer support, or analyze vast amounts of data far more quickly and accurately than a human could. They can also be used in ways that invade privacy or replace jobs, which is often where the apprehension comes from.\n\nThe key is to create regulations and ethical guidelines for the use of AI Agents, and to continue researching and developing them in a way that maximizes their benefits and minimizes their potential harm. From a research perspective, there's a lot of potential in AI Agents, and it's a fascinating field to be a part of."
== "My perspective on AI agents is nuanced rather than outright negative. While I do see tremendous potential in their applications, I also recognize the inherent challenges and risks that must be carefully managed. \n\nAI agents can significantly streamline tasks, enhance productivity, and enable new possibilities in various fields such as healthcare, finance, customer service, and more. By automating routine processes, providing real-time data analysis, and facilitating more informed decision-making, they offer remarkable efficiency and effectiveness. AI agents are also foundational in advancing technologies like autonomous vehicles, personalized recommendations, and intelligent virtual assistants.\n\nHowever, there are critical concerns that need to be addressed. One major issue is the potential for bias and ethical dilemmas. Since AI systems are trained on existing datasets, they can inadvertently perpetuate existing biases or generate unfair outcomes. Furthermore, there is the question of accountability—determining who is responsible when an AI system makes a mistake can be complex.\n\nPrivacy and security are also significant concerns. AI agents often require vast amounts of data, some of which may be sensitive or personal. Ensuring that this data is protected against breaches and misuse is paramount. Additionally, there is the looming issue of job displacement. As AI agents take over certain tasks, there is the potential for significant disruption in the job market, necessitating strategies for workforce transition and upskilling.\n\nDespite these challenges, I believe that with robust ethical frameworks, continuous oversight, and a focus on transparency and fairness, the benefits of AI agents can far outweigh the drawbacks. It’s not about hating AI agents; it’s about recognizing the full spectrum of their impact and striving to optimize their positive attributes while mitigating the risks."
)
@@ -94,7 +94,7 @@ def test_ask_question_with_coworker_as_array():
assert (
result
== "I don't hate or love AI agents. My passion lies in understanding them, researching about their capabilities, implications, and potential for development. As a researcher, my feelings toward AI are more of fascination and interest rather than personal love or hate."
== "As an expert researcher specializing in technology, I approach AI agents with a nuanced perspective rather than simply loving or hating them. AI agents represent a significant leap in technology, bringing a range of potential benefits and challenges. \n\nThe advancements in AI agents have led to impressive applications across various industries. They improve efficiency, automate repetitive tasks, and enhance decision-making processes. For example, in healthcare, AI agents assist in diagnosing diseases with higher accuracy and speed than human doctors could achieve alone. In customer service, AI chatbots provide 24/7 support, handling numerous queries simultaneously and improving user experience.\n\nOn the other hand, there are legitimate concerns about AI agents. Ethical considerations surrounding privacy, security, and the potential for biased algorithms are critical issues that need ongoing research and regulation. The displacement of jobs due to automation is another serious concern, which requires strategic planning and adaptation in the workforce.\n\nTherefore, my stance on AI agents is one of cautious optimism. While I don't hate AI agents, I recognize the importance of a balanced approach that involves responsible development, thoughtful implementation, and continuous evaluation. This helps ensure that AI agents are used to their full potential while mitigating the associated risks.\n\nIn summary, my feelings towards AI agents are not about love or hate, but about understanding their complexities and working towards harnessing their benefits responsibly."
)

615
tests/agent_tools/cassettes/test_ask_question.yaml
View File

@@ -1,26 +1,18 @@
interactions:
- request:
body: '{"messages": [{"role": "user", "content": "You are researcher.\nYou''re
an expert researcher, specialized in technology\n\nYour personal goal is: make
the best research and analysis on content about AI and AI agentsTOOLS:\n------\nYou
have access to only the following tools:\n\n\n\nTo use a tool, please use the
exact following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction:
the tool you wanna use, should be one of [], just the name.\nAction Input: Any
and all relevant information input and context for using the tool\nObservation:
the result of using the tool\n```\n\nWhen you have a response for your task,
or if you do not need to use a tool, you MUST use the format:\n\n```\nThought:
Do I need to use a tool? No\nFinal Answer: [your response here]```This is the
summary of your work so far:\nThe human asks the AI''s opinion on AI Agents,
suggesting that the AI dislikes them. The AI, identifying as a researcher, clarifies
that its opinions are based on research and study. It views AI Agents as a key
part of technological advancement, with potential to improve lives through automation
and decision-making assistance. However, it also acknowledges challenges, including
job displacement risk and ethical implications. The AI aims to maintain an objective
view for accurate analysis, rather than loving or hating AI Agents.Begin! This
is VERY important to you, your job depends on it!\n\nCurrent Task: do you hate
AI Agents?\nThis is the context you''re working with:\nI heard you LOVE them\n"}],
"model": "gpt-4", "n": 1, "stop": ["\nObservation"], "stream": true, "temperature":
0.7}'
body: '{"messages": [{"role": "user", "content": "You are researcher. You''re
an expert researcher, specialized in technology\nYour personal goal is: make
the best research and analysis on content about AI and AI agents\nTo 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: Your final answer must be the great
and the most complete as possible, it must be outcome described.\n\nI MUST use
these formats, my job depends on it!\nCurrent Task: do you hate AI Agents?\n\nThis
is the expect criteria for your final answer: Your best answer to your coworker
asking you this, accounting for the context shared. \n you MUST return the actual
complete content as the final answer, not a summary.\n\nThis is the context
you''re working with:\nI heard you LOVE them\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:"}], "model": "gpt-4o", "stop": ["\nObservation"]}'
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@@ -48,7 +40,9 @@ interactions:
x-stainless-os:
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x-stainless-raw-response:
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tests/agents/test_crew_agent_parser.py
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import pytest
from crewai.agents.parser import CrewAgentParser
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from crewai.agents.crew_agent_executor import (
AgentAction,
AgentFinish,
OutputParserException,
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@pytest.fixture
def parser():
p = CrewAgentParser()
p.agent = MockAgent()
agent = MockAgent()
p = CrewAgentParser(agent)
return p
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result = parser.parse(text)
assert isinstance(result, AgentFinish)
assert result.return_values["output"] == "The temperature is 100 degrees"
assert result.output == "The temperature is 100 degrees"
def test_missing_action_error(parser):
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with pytest.raises(OutputParserException) as exc_info:
parser.parse(text)
assert "Could not parse LLM output" in str(exc_info.value)
assert "Invalid Format: I missed the 'Action:' after 'Thought:'." in str(
exc_info.value
)
def test_missing_action_input_error(parser):
text = "Thought: Let's find the temperature\nAction: search"
with pytest.raises(OutputParserException) as exc_info:
parser.parse(text)
assert "Could not parse LLM output" in str(exc_info.value)
assert "I missed the 'Action Input:' after 'Action:'." in str(exc_info.value)
def test_action_and_final_answer_error(parser):

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