mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-11 09:08:31 +00:00
10997dd175
* 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
82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
from abc import ABC, abstractmethod
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from typing import List, Optional, Union
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from pydantic import BaseModel, Field
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from crewai.agents.agent_builder.base_agent import BaseAgent
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from crewai.task import Task
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from crewai.utilities import I18N
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class BaseAgentTools(BaseModel, ABC):
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"""Default tools around agent delegation"""
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agents: List[BaseAgent] = Field(description="List of agents in this crew.")
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i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
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@abstractmethod
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def tools(self):
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pass
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def _get_coworker(self, coworker: Optional[str], **kwargs) -> Optional[str]:
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coworker = coworker or kwargs.get("co_worker") or kwargs.get("coworker")
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if coworker:
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is_list = coworker.startswith("[") and coworker.endswith("]")
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if is_list:
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coworker = coworker[1:-1].split(",")[0]
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return coworker
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def delegate_work(
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self, task: str, context: str, coworker: Optional[str] = None, **kwargs
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):
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"""Useful to delegate a specific task to a coworker passing all necessary context and names."""
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coworker = self._get_coworker(coworker, **kwargs)
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return self._execute(coworker, task, context)
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def ask_question(
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self, question: str, context: str, coworker: Optional[str] = None, **kwargs
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):
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"""Useful to ask a question, opinion or take from a coworker passing all necessary context and names."""
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coworker = self._get_coworker(coworker, **kwargs)
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return self._execute(coworker, question, context)
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def _execute(self, agent: Union[str, None], task: str, context: Union[str, None]):
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"""Execute the command."""
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try:
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if agent is None:
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agent = ""
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# It is important to remove the quotes from the agent name.
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# The reason we have to do this is because less-powerful LLM's
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# have difficulty producing valid JSON.
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# As a result, we end up with invalid JSON that is truncated like this:
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# {"task": "....", "coworker": "....
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# when it should look like this:
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# {"task": "....", "coworker": "...."}
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agent_name = agent.casefold().replace('"', "").replace("\n", "")
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agent = [
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available_agent
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for available_agent in self.agents
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if available_agent.role.casefold().replace("\n", "") == agent_name
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]
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except Exception as _:
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return self.i18n.errors("agent_tool_unexsiting_coworker").format(
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coworkers="\n".join(
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[f"- {agent.role.casefold()}" for agent in self.agents]
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)
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)
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if not agent:
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return self.i18n.errors("agent_tool_unexsiting_coworker").format(
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coworkers="\n".join(
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[f"- {agent.role.casefold()}" for agent in self.agents]
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)
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)
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agent = agent[0]
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task = Task(
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description=task,
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agent=agent,
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expected_output="Your best answer to your coworker asking you this, accounting for the context shared.",
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)
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return agent.execute_task(task, context)
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