mirror of
https://github.com/crewAIInc/crewAI.git
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Lorenzejay/byoa (#776)
* better spacing * works with llama index * works on langchain custom just need delegation to work * cleanup for custom_agent class * works with different argument expectations for agent_executor * cleanup for hierarchial process, better agent_executor args handler and added to the crew agent doc page * removed code examples for langchain + llama index, added to docs instead * added key output if return is not a str for and added some tests * added hinting for CustomAgent class * removed pass as it was not needed * closer just need to figuire ou agentTools * running agents - llamaindex and langchain with base agent * some cleanup on baseAgent * minimum for agent to run for base class and ensure it works with hierarchical process * cleanup for original agent to take on BaseAgent class * Agent takes on langchainagent and cleanup across * token handling working for usage_metrics to continue working * installed llama-index, updated docs and added better name * fixed some type errors * base agent holds token_process * heirarchail process uses proper tools and no longer relies on hasattr for token_processes * removal of test_custom_agent_executions * this fixes copying agents * leveraging an executor class for trigger llamaindex agent * llama index now has ask_human * executor mixins added * added output converter base class * type listed * cleanup for output conversions and tokenprocess eliminated redundancy * properly handling tokens * simplified token calc handling * original agent with base agent builder structure setup * better docs * no more llama-index dep * cleaner docs * test fixes * poetry reverts and better docs * base_agent_tools set for third party agents * updated task and test fix
This commit is contained in:
Lorenze Jay
256
src/crewai/agents/agent_builder/base_agent.py
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256
src/crewai/agents/agent_builder/base_agent.py
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from copy import deepcopy
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import uuid
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from typing import Any, Dict, List, Optional
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from abc import ABC, abstractmethod
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from pydantic import (
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UUID4,
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BaseModel,
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Field,
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InstanceOf,
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field_validator,
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model_validator,
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ConfigDict,
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PrivateAttr,
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)
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from pydantic_core import PydanticCustomError
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from crewai.utilities import I18N, RPMController, Logger
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from crewai.agents import CacheHandler, ToolsHandler
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from crewai.utilities.token_counter_callback import TokenProcess
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class BaseAgent(ABC, BaseModel):
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"""Abstract Base Class for all third party agents compatible with CrewAI.
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Attributes:
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id (UUID4): Unique identifier for the agent.
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role (str): Role of the agent.
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goal (str): Objective of the agent.
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backstory (str): Backstory of the agent.
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cache (bool): Whether the agent should use a cache for tool usage.
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config (Optional[Dict[str, Any]]): Configuration for the agent.
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verbose (bool): Verbose mode for the Agent Execution.
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max_rpm (Optional[int]): Maximum number of requests per minute for the agent execution.
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allow_delegation (bool): Allow delegation of tasks to agents.
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tools (Optional[List[Any]]): Tools at the agent's disposal.
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max_iter (Optional[int]): Maximum iterations for an agent to execute a task.
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agent_executor (InstanceOf): An instance of the CrewAgentExecutor class.
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llm (Any): Language model that will run the agent.
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crew (Any): Crew to which the agent belongs.
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i18n (I18N): Internationalization settings.
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cache_handler (InstanceOf[CacheHandler]): An instance of the CacheHandler class.
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tools_handler (InstanceOf[ToolsHandler]): An instance of the ToolsHandler class.
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Methods:
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execute_task(task: Any, context: Optional[str] = None, tools: Optional[List[Any]] = None) -> str:
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Abstract method to execute a task.
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create_agent_executor(tools=None) -> None:
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Abstract method to create an agent executor.
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_parse_tools(tools: List[Any]) -> List[Any]:
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Abstract method to parse tools.
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get_delegation_tools(agents: List["BaseAgent"]):
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Abstract method to set the agents task tools for handling delegation and question asking to other agents in crew.
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get_output_converter(llm, model, instructions):
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Abstract method to get the converter class for the agent to create json/pydantic outputs.
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interpolate_inputs(inputs: Dict[str, Any]) -> None:
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Interpolate inputs into the agent description and backstory.
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set_cache_handler(cache_handler: CacheHandler) -> None:
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Set the cache handler for the agent.
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increment_formatting_errors() -> None:
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Increment formatting errors.
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copy() -> "BaseAgent":
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Create a copy of the agent.
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set_rpm_controller(rpm_controller: RPMController) -> None:
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Set the rpm controller for the agent.
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set_private_attrs() -> "BaseAgent":
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Set private attributes.
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"""
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__hash__ = object.__hash__ # type: ignore
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_logger: Logger = PrivateAttr()
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_rpm_controller: RPMController = PrivateAttr(default=None)
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_request_within_rpm_limit: Any = PrivateAttr(default=None)
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formatting_errors: int = 0
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model_config = ConfigDict(arbitrary_types_allowed=True)
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id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
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role: str = Field(description="Role of the agent")
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goal: str = Field(description="Objective of the agent")
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backstory: str = Field(description="Backstory of the agent")
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cache: bool = Field(
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default=True, description="Whether the agent should use a cache for tool usage."
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)
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config: Optional[Dict[str, Any]] = Field(
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description="Configuration for the agent", default=None
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)
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verbose: bool = Field(
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default=False, description="Verbose mode for the Agent Execution"
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)
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max_rpm: Optional[int] = Field(
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default=None,
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description="Maximum number of requests per minute for the agent execution to be respected.",
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)
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allow_delegation: bool = Field(
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default=True, description="Allow delegation of tasks to agents"
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)
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tools: Optional[List[Any]] = Field(
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default_factory=list, description="Tools at agents' disposal"
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)
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max_iter: Optional[int] = Field(
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default=25, description="Maximum iterations for an agent to execute a task"
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)
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agent_executor: InstanceOf = Field(
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default=None, description="An instance of the CrewAgentExecutor class."
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)
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llm: Any = Field(
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default=None, description="Language model that will run the agent."
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)
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crew: Any = Field(default=None, description="Crew to which the agent belongs.")
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i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
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cache_handler: InstanceOf[CacheHandler] = Field(
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default=None, description="An instance of the CacheHandler class."
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)
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tools_handler: InstanceOf[ToolsHandler] = Field(
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default=None, description="An instance of the ToolsHandler class."
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)
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_original_role: str | None = None
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_original_goal: str | None = None
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_original_backstory: str | None = None
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_token_process: TokenProcess = TokenProcess()
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def __init__(__pydantic_self__, **data):
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config = data.pop("config", {})
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super().__init__(**config, **data)
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@model_validator(mode="after")
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def set_config_attributes(self):
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if self.config:
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for key, value in self.config.items():
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setattr(self, key, value)
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return self
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@field_validator("id", mode="before")
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@classmethod
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def _deny_user_set_id(cls, v: Optional[UUID4]) -> None:
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if v:
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raise PydanticCustomError(
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"may_not_set_field", "This field is not to be set by the user.", {}
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)
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@model_validator(mode="after")
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def set_attributes_based_on_config(self) -> "BaseAgent":
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"""Set attributes based on the agent configuration."""
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if self.config:
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for key, value in self.config.items():
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setattr(self, key, value)
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return self
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@model_validator(mode="after")
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def set_private_attrs(self):
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"""Set private attributes."""
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self._logger = Logger(self.verbose)
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if self.max_rpm and not self._rpm_controller:
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self._rpm_controller = RPMController(
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max_rpm=self.max_rpm, logger=self._logger
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)
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if not self._token_process:
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self._token_process = TokenProcess()
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return self
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@abstractmethod
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def execute_task(
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self,
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task: Any,
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context: Optional[str] = None,
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tools: Optional[List[Any]] = None,
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) -> str:
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pass
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@abstractmethod
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def create_agent_executor(self, tools=None) -> None:
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pass
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@abstractmethod
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def _parse_tools(self, tools: List[Any]) -> List[Any]:
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pass
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@abstractmethod
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def get_delegation_tools(self, agents: List["BaseAgent"]):
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"""Set the task tools that init BaseAgenTools class."""
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pass
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@abstractmethod
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def get_output_converter(
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self, llm: Any, text: str, model: type[BaseModel] | None, instructions: str
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):
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"""Get the converter class for the agent to create json/pydantic outputs."""
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pass
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def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
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"""Interpolate inputs into the agent description and backstory."""
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if self._original_role is None:
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self._original_role = self.role
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if self._original_goal is None:
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self._original_goal = self.goal
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if self._original_backstory is None:
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self._original_backstory = self.backstory
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if inputs:
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self.role = self._original_role.format(**inputs)
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self.goal = self._original_goal.format(**inputs)
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self.backstory = self._original_backstory.format(**inputs)
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def set_cache_handler(self, cache_handler: CacheHandler) -> None:
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"""Set the cache handler for the agent.
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Args:
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cache_handler: An instance of the CacheHandler class.
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"""
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self.tools_handler = ToolsHandler()
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if self.cache:
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self.cache_handler = cache_handler
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self.tools_handler.cache = cache_handler
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self.create_agent_executor()
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def increment_formatting_errors(self) -> None:
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print("Formatting errors incremented")
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def copy(self):
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exclude = {
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"id",
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"_logger",
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"_rpm_controller",
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"_request_within_rpm_limit",
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"token_process",
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"agent_executor",
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"tools",
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"tools_handler",
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"cache_handler",
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"crew",
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"llm",
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}
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copied_data = self.model_dump(exclude=exclude, exclude_unset=True)
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copied_agent = self.__class__(**copied_data)
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# Copy mutable attributes separately
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copied_agent.tools = deepcopy(self.tools)
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copied_agent.config = deepcopy(self.config)
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# Preserve original values for interpolation
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copied_agent._original_role = self._original_role
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copied_agent._original_goal = self._original_goal
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copied_agent._original_backstory = self._original_backstory
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return copied_agent
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def set_rpm_controller(self, rpm_controller: RPMController) -> None:
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"""Set the rpm controller for the agent.
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Args:
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rpm_controller: An instance of the RPMController class.
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"""
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if not self._rpm_controller:
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self._rpm_controller = rpm_controller
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self.create_agent_executor()
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