andre/crewAI
1
0
Fork 0
mirror of https://github.com/crewAIInc/crewAI.git synced 2026-01-09 16:18:30 +00:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Lorenzejay/byoa (#776)

Browse Source 
* 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
2024-06-27 10:56:08 -07:00
committed by GitHub
parent da9cc5f097
commit 10997dd175
22 changed files with 637 additions and 407 deletions
Download Patch File Download Diff File Expand all files Collapse all files

208
src/crewai/agent.py
View File

@@ -1,7 +1,5 @@
import os
import uuid
from copy import deepcopy
from typing import Any, Dict, List, Optional, Tuple
from typing import Any, List, Optional, Tuple
from langchain.agents.agent import RunnableAgent
from langchain.agents.tools import tool as LangChainTool
@@ -9,27 +7,20 @@ 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 pydantic import Field, InstanceOf, model_validator
from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser
from crewai.memory.contextual.contextual_memory import ContextualMemory
from crewai.utilities import I18N, Logger, Prompts, RPMController
from crewai.tools.agent_tools import AgentTools
from crewai.utilities import Prompts, Converter
from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
from crewai.utilities.token_counter_callback import TokenCalcHandler, TokenProcess
from crewai.utilities.token_counter_callback import TokenCalcHandler
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.utilities.training_handler import CrewTrainingHandler
class Agent(BaseModel):
class Agent(BaseAgent):
"""Represents an agent in a system.
Each agent has a role, a goal, a backstory, and an optional language model (llm).
@@ -53,57 +44,10 @@ class Agent(BaseModel):
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."
)
@@ -111,7 +55,6 @@ class Agent(BaseModel):
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")
@@ -133,47 +76,18 @@ class Agent(BaseModel):
response_template: Optional[str] = Field(
default=None, description="Response format for the agent."
)
allow_code_execution: Optional[bool] = Field(
default=False, description="Enable code execution 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."""
"""Ensure agent executor and token process is set."""
if hasattr(self.llm, "model_name"):
token_handler = TokenCalcHandler(self.llm.model_name, self._token_process)
@@ -232,8 +146,7 @@ class Agent(BaseModel):
tools = tools or self.tools
# type: ignore # Argument 1 to "_parse_tools" of "Agent" has incompatible type "list[Any] | None"; expected "list[Any]"
parsed_tools = self._parse_tools(tools)
parsed_tools = self._parse_tools(tools or [])
self.create_agent_executor(tools=tools)
self.agent_executor.tools = parsed_tools
self.agent_executor.task = task
@@ -253,33 +166,22 @@ class Agent(BaseModel):
"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 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:
"""Create an agent executor for the agent.
@@ -335,76 +237,32 @@ class Agent(BaseModel):
)
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
def get_delegation_tools(self, agents: List[BaseAgent]):
agent_tools = AgentTools(agents=agents)
tools = agent_tools.tools()
return tools
if inputs:
self.role = self._original_role.format(**inputs)
self.goal = self._original_goal.format(**inputs)
self.backstory = self._original_backstory.format(**inputs)
def get_output_converter(self, llm, text, model, instructions):
return Converter(llm=llm, text=text, model=model, instructions=instructions)
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 copy(self):
"""Create a deep copy of the Agent."""
exclude = {
"id",
"_logger",
"_rpm_controller",
"_request_within_rpm_limit",
"_token_process",
"agent_executor",
"tools",
"tools_handler",
"cache_handler",
}
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 = Agent(**copied_data)
copied_agent.tools = deepcopy(self.tools)
return copied_agent
# type: ignore # Function "langchain_core.tools.tool" is not valid as a type
def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]:
"""Parse tools to be used for the task."""
# tentatively try to import from crewai_tools import BaseTool as CrewAITool
tools_list = []
try:
# tentatively try to import from crewai_tools import BaseTool as CrewAITool
from crewai_tools import BaseTool as CrewAITool
for tool in tools: