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agent.py
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/
agent.py
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import os
import uuid
from typing import Any, Dict, List, Optional, Tuple
from langchain.agents.agent import RunnableAgent
from langchain.agents.tools import tool as LangChainTool
from langchain.tools.render import render_text_description
from langchain_core.agents import AgentAction
from langchain_core.callbacks import BaseCallbackHandler
from langchain_openai import ChatOpenAI
from pydantic import (
UUID4,
BaseModel,
ConfigDict,
Field,
InstanceOf,
PrivateAttr,
field_validator,
model_validator,
)
from pydantic_core import PydanticCustomError
from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser, ToolsHandler
from crewai.memory.contextual.contextual_memory import ContextualMemory
from crewai.utilities import I18N, Logger, Prompts, RPMController
from crewai.utilities.token_counter_callback import TokenCalcHandler, TokenProcess
class Agent(BaseModel):
"""Represents an agent in a system.
Each agent has a role, a goal, a backstory, and an optional language model (llm).
The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents.
Attributes:
agent_executor: An instance of the CrewAgentExecutor class.
role: The role of the agent.
goal: The objective of the agent.
backstory: The backstory of the agent.
config: Dict representation of agent configuration.
llm: The language model that will run the agent.
function_calling_llm: The language model that will handle the tool calling for this agent, it overrides the crew function_calling_llm.
max_iter: Maximum number of iterations for an agent to execute a task.
memory: Whether the agent should have memory or not.
max_rpm: Maximum number of requests per minute for the agent execution to be respected.
verbose: Whether the agent execution should be in verbose mode.
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
"""
__hash__ = object.__hash__ # type: ignore
_logger: Logger = PrivateAttr()
_rpm_controller: RPMController = PrivateAttr(default=None)
_request_within_rpm_limit: Any = PrivateAttr(default=None)
_token_process: TokenProcess = TokenProcess()
formatting_errors: int = 0
model_config = ConfigDict(arbitrary_types_allowed=True)
id: UUID4 = Field(
default_factory=uuid.uuid4,
frozen=True,
description="Unique identifier for the object, not set by user.",
)
role: str = Field(description="Role of the agent")
goal: str = Field(description="Objective of the agent")
backstory: str = Field(description="Backstory of the agent")
cache: bool = Field(
default=True,
description="Whether the agent should use a cache for tool usage.",
)
config: Optional[Dict[str, Any]] = Field(
description="Configuration for the agent",
default=None,
)
max_rpm: Optional[int] = Field(
default=None,
description="Maximum number of requests per minute for the agent execution to be respected.",
)
verbose: bool = Field(
default=False, description="Verbose mode for the Agent Execution"
)
allow_delegation: bool = Field(
default=True, description="Allow delegation of tasks to agents"
)
tools: Optional[List[Any]] = Field(
default_factory=list, description="Tools at agents disposal"
)
max_iter: Optional[int] = Field(
default=25, description="Maximum iterations for an agent to execute a task"
)
max_execution_time: Optional[int] = Field(
default=None,
description="Maximum execution time for an agent to execute a task",
)
agent_executor: InstanceOf[CrewAgentExecutor] = Field(
default=None, description="An instance of the CrewAgentExecutor class."
)
crew: Any = Field(default=None, description="Crew to which the agent belongs.")
tools_handler: InstanceOf[ToolsHandler] = Field(
default=None, description="An instance of the ToolsHandler class."
)
cache_handler: InstanceOf[CacheHandler] = Field(
default=None, description="An instance of the CacheHandler class."
)
step_callback: Optional[Any] = Field(
default=None,
description="Callback to be executed after each step of the agent execution.",
)
i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
llm: Any = Field(
default_factory=lambda: ChatOpenAI(
model=os.environ.get("OPENAI_MODEL_NAME", "gpt-4o")
),
description="Language model that will run the agent.",
)
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."
)
prompt_template: Optional[str] = Field(
default=None, description="Prompt format for the agent."
)
response_template: Optional[str] = Field(
default=None, description="Response format for the agent."
)
_original_role: str | None = None
_original_goal: str | None = None
_original_backstory: str | None = None
def __init__(__pydantic_self__, **data):
config = data.pop("config", {})
super().__init__(**config, **data)
@field_validator("id", mode="before")
@classmethod
def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
if v:
raise PydanticCustomError(
"may_not_set_field", "This field is not to be set by the user.", {}
)
@model_validator(mode="after")
def set_attributes_based_on_config(self) -> "Agent":
"""Set attributes based on the agent configuration."""
if self.config:
for key, value in self.config.items():
setattr(self, key, value)
return self
@model_validator(mode="after")
def set_private_attrs(self):
"""Set private attributes."""
self._logger = Logger(self.verbose)
if self.max_rpm and not self._rpm_controller:
self._rpm_controller = RPMController(
max_rpm=self.max_rpm, logger=self._logger
)
return self
@model_validator(mode="after")
def set_agent_executor(self) -> "Agent":
"""set agent executor is set."""
if hasattr(self.llm, "model_name"):
token_handler = TokenCalcHandler(self.llm.model_name, self._token_process)
# Ensure self.llm.callbacks is a list
if not isinstance(self.llm.callbacks, list):
self.llm.callbacks = []
# Check if an instance of TokenCalcHandler already exists in the list
if not any(
isinstance(handler, TokenCalcHandler) for handler in self.llm.callbacks
):
self.llm.callbacks.append(token_handler)
if not self.agent_executor:
if not self.cache_handler:
self.cache_handler = CacheHandler()
self.set_cache_handler(self.cache_handler)
return self
def execute_task(
self,
task: Any,
context: Optional[str] = None,
tools: Optional[List[Any]] = None,
) -> str:
"""Execute a task with the agent.
Args:
task: Task to execute.
context: Context to execute the task in.
tools: Tools to use for the task.
Returns:
Output of the agent
"""
if self.tools_handler:
self.tools_handler.last_used_tool = {} # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
task_prompt = task.prompt()
if context:
task_prompt = self.i18n.slice("task_with_context").format(
task=task_prompt, context=context
)
if self.crew and self.crew.memory:
contextual_memory = ContextualMemory(
self.crew._short_term_memory,
self.crew._long_term_memory,
self.crew._entity_memory,
)
memory = contextual_memory.build_context_for_task(task, context)
if memory.strip() != "":
task_prompt += self.i18n.slice("memory").format(memory=memory)
tools = tools or self.tools
parsed_tools = self._parse_tools(tools) # type: ignore # Argument 1 to "_parse_tools" of "Agent" has incompatible type "list[Any] | None"; expected "list[Any]"
self.create_agent_executor(tools=tools)
self.agent_executor.tools = parsed_tools
self.agent_executor.task = task
self.agent_executor.tools_description = render_text_description(parsed_tools)
self.agent_executor.tools_names = self.__tools_names(parsed_tools)
result = self.agent_executor.invoke(
{
"input": task_prompt,
"tool_names": self.agent_executor.tools_names,
"tools": self.agent_executor.tools_description,
}
)["output"]
if self.max_rpm:
self._rpm_controller.stop_rpm_counter()
return result
def set_cache_handler(self, cache_handler: CacheHandler) -> None:
"""Set the cache handler for the agent.
Args:
cache_handler: An instance of the CacheHandler class.
"""
self.tools_handler = ToolsHandler()
if self.cache:
self.cache_handler = cache_handler
self.tools_handler.cache = cache_handler
self.create_agent_executor()
def set_rpm_controller(self, rpm_controller: RPMController) -> None:
"""Set the rpm controller for the agent.
Args:
rpm_controller: An instance of the RPMController class.
"""
if not self._rpm_controller:
self._rpm_controller = rpm_controller
self.create_agent_executor()
def create_agent_executor(self, tools=None) -> None:
"""Create an agent executor for the agent.
Returns:
An instance of the CrewAgentExecutor class.
"""
tools = tools or self.tools
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,
}
if self._rpm_controller:
executor_args["request_within_rpm_limit"] = (
self._rpm_controller.check_or_wait
)
prompt = Prompts(
i18n=self.i18n,
tools=tools,
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:
stop_words.append(
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
)
def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
"""Interpolate inputs into the agent description and backstory."""
if self._original_role is None:
self._original_role = self.role
if self._original_goal is None:
self._original_goal = self.goal
if self._original_backstory is None:
self._original_backstory = self.backstory
if inputs:
self.role = self._original_role.format(**inputs)
self.goal = self._original_goal.format(**inputs)
self.backstory = self._original_backstory.format(**inputs)
def increment_formatting_errors(self) -> None:
"""Count the formatting errors of the agent."""
self.formatting_errors += 1
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 _parse_tools(self, tools: List[Any]) -> List[LangChainTool]: # type: ignore # Function "langchain_core.tools.tool" is not valid as a type
"""Parse tools to be used for the task."""
# tentatively try to import from crewai_tools import BaseTool as CrewAITool
tools_list = []
try:
from crewai_tools import BaseTool as CrewAITool
for tool in tools:
if isinstance(tool, CrewAITool):
tools_list.append(tool.to_langchain())
else:
tools_list.append(tool)
except ModuleNotFoundError:
for tool in tools:
tools_list.append(tool)
return tools_list
@staticmethod
def __tools_names(tools) -> str:
return ", ".join([t.name for t in tools])
def __repr__(self):
return f"Agent(role={self.role}, goal={self.goal}, backstory={self.backstory})"