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Run pre-commit hooks
In the title !
This commit is contained in:
Greyson Lalonde
@@ -1,4 +1,4 @@
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from .task import Task
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from .crew import Crew
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from .agent import Agent
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from .process import Process
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from .crew import Crew
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from .process import Process
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from .task import Task
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204
crewai/agent.py
204
crewai/agent.py
@@ -1,128 +1,120 @@
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"""Generic agent."""
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from typing import List, Any, Optional
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from pydantic.v1 import BaseModel, PrivateAttr, Field, root_validator
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from typing import Any, List, Optional
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from langchain.agents import AgentExecutor
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from langchain.chat_models import ChatOpenAI
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from langchain.tools.render import render_text_description
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from langchain.agents.format_scratchpad import format_log_to_str
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from langchain.agents.output_parsers import ReActSingleInputOutputParser
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from langchain.chat_models import ChatOpenAI
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from langchain.memory import ConversationSummaryMemory
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from langchain.tools.render import render_text_description
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from pydantic.v1 import BaseModel, Field, PrivateAttr, root_validator
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from .prompts import Prompts
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class Agent(BaseModel):
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"""
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Represents an agent in a system.
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"""
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Represents an agent in a system.
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Each agent has a role, a goal, a backstory, and an optional language model (llm).
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The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents.
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Each agent has a role, a goal, a backstory, and an optional language model (llm).
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The agent can also have memory, can operate in verbose mode, and can delegate tasks to other agents.
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Attributes:
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agent_executor: An instance of the AgentExecutor class.
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role: The role of the agent.
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goal: The objective of the agent.
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backstory: The backstory of the agent.
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llm: The language model that will run the agent.
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memory: Whether the agent should have memory or not.
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verbose: Whether the agent execution should be in verbose mode.
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allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
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"""
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agent_executor: AgentExecutor = None
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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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llm: Optional[Any] = Field(description="LLM that will run the agent")
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memory: bool = Field(
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description="Whether the agent should have memory or not",
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default=True
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)
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verbose: bool = Field(
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description="Verbose mode for the Agent Execution",
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default=False
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)
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allow_delegation: bool = Field(
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description="Allow delegation of tasks to agents",
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default=True
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)
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tools: List[Any] = Field(
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description="Tools at agents disposal",
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default=[]
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)
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_task_calls: List[Any] = PrivateAttr()
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Attributes:
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agent_executor: An instance of the AgentExecutor class.
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role: The role of the agent.
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goal: The objective of the agent.
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backstory: The backstory of the agent.
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llm: The language model that will run the agent.
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memory: Whether the agent should have memory or not.
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verbose: Whether the agent execution should be in verbose mode.
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allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
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"""
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@root_validator(pre=True)
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def check_llm(_cls, values):
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if not values.get('llm'):
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values['llm'] = ChatOpenAI(
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temperature=0.7,
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model_name="gpt-4"
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)
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return values
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agent_executor: AgentExecutor = None
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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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llm: Optional[Any] = Field(description="LLM that will run the agent")
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memory: bool = Field(
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description="Whether the agent should have memory or not", default=True
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)
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verbose: bool = Field(
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description="Verbose mode for the Agent Execution", default=False
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)
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allow_delegation: bool = Field(
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description="Allow delegation of tasks to agents", default=True
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)
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tools: List[Any] = Field(description="Tools at agents disposal", default=[])
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_task_calls: List[Any] = PrivateAttr()
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def __init__(self, **data):
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super().__init__(**data)
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agent_args = {
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"input": lambda x: x["input"],
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"tools": lambda x: x["tools"],
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"tool_names": lambda x: x["tool_names"],
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"agent_scratchpad": lambda x: format_log_to_str(x['intermediate_steps']),
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}
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executor_args = {
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"tools": self.tools,
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"verbose": self.verbose,
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"handle_parsing_errors": True,
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}
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@root_validator(pre=True)
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def check_llm(_cls, values):
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if not values.get("llm"):
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values["llm"] = ChatOpenAI(temperature=0.7, model_name="gpt-4")
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return values
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if self.memory:
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summary_memory = ConversationSummaryMemory(
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llm=self.llm,
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memory_key='chat_history',
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input_key="input"
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)
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executor_args['memory'] = summary_memory
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agent_args['chat_history'] = lambda x: x["chat_history"]
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prompt = Prompts.TASK_EXECUTION_WITH_MEMORY_PROMPT
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else:
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prompt = Prompts.TASK_EXECUTION_PROMPT
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def __init__(self, **data):
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super().__init__(**data)
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agent_args = {
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"input": lambda x: x["input"],
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"tools": lambda x: x["tools"],
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"tool_names": lambda x: x["tool_names"],
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"agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
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}
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executor_args = {
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"tools": self.tools,
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"verbose": self.verbose,
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"handle_parsing_errors": True,
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}
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execution_prompt = prompt.partial(
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goal=self.goal,
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role=self.role,
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backstory=self.backstory,
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)
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if self.memory:
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summary_memory = ConversationSummaryMemory(
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llm=self.llm, memory_key="chat_history", input_key="input"
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)
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executor_args["memory"] = summary_memory
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agent_args["chat_history"] = lambda x: x["chat_history"]
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prompt = Prompts.TASK_EXECUTION_WITH_MEMORY_PROMPT
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else:
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prompt = Prompts.TASK_EXECUTION_PROMPT
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bind = self.llm.bind(stop=["\nObservation"])
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inner_agent = agent_args | execution_prompt | bind | ReActSingleInputOutputParser()
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execution_prompt = prompt.partial(
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goal=self.goal,
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role=self.role,
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backstory=self.backstory,
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)
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self.agent_executor = AgentExecutor(
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agent=inner_agent,
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**executor_args
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)
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bind = self.llm.bind(stop=["\nObservation"])
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inner_agent = (
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agent_args | execution_prompt | bind | ReActSingleInputOutputParser()
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)
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def execute_task(self, task: str, context: str = None, tools: List[Any] = None) -> str:
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"""
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Execute a task with the agent.
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Parameters:
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task (str): Task to execute
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Returns:
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output (str): Output of the agent
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"""
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if context:
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task = "\n".join([
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task,
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"\nThis is the context you are working with:",
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context
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])
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self.agent_executor = AgentExecutor(agent=inner_agent, **executor_args)
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tools = tools or self.tools
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self.agent_executor.tools = tools
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return self.agent_executor.invoke({
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"input": task,
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"tool_names": self.__tools_names(tools),
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"tools": render_text_description(tools),
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})['output']
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def execute_task(
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self, task: str, context: str = None, tools: List[Any] = None
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) -> str:
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"""
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Execute a task with the agent.
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Parameters:
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task (str): Task to execute
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Returns:
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output (str): Output of the agent
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"""
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if context:
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task = "\n".join(
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[task, "\nThis is the context you are working with:", context]
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)
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def __tools_names(self, tools) -> str:
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return ", ".join([t.name for t in tools])
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tools = tools or self.tools
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self.agent_executor.tools = tools
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return self.agent_executor.invoke(
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{
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"input": task,
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"tool_names": self.__tools_names(tools),
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"tools": render_text_description(tools),
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}
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)["output"]
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def __tools_names(self, tools) -> str:
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return ", ".join([t.name for t in tools])
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147
crewai/crew.py
147
crewai/crew.py
@@ -1,89 +1,88 @@
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import json
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from typing import List, Optional
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from pydantic.v1 import BaseModel, Field, Json, root_validator
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from .process import Process
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from .agent import Agent
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from .process import Process
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from .task import Task
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from .tools.agent_tools import AgentTools
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class Crew(BaseModel):
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"""
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Class that represents a group of agents, how they should work together and
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their tasks.
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"""
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tasks: Optional[List[Task]] = Field(description="List of tasks")
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agents: Optional[List[Agent]] = Field(description="List of agents in this crew.")
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process: Process = Field(
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description="Process that the crew will follow.",
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default=Process.sequential
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)
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verbose: bool = Field(
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description="Verbose mode for the Agent Execution",
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default=False
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)
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config: Optional[Json] = Field(
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description="Configuration of the crew.",
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default=None
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)
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"""
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Class that represents a group of agents, how they should work together and
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their tasks.
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"""
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@root_validator(pre=True)
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def check_config(_cls, values):
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if (
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not values.get('config')
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and (
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not values.get('agents') and not values.get('tasks')
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)
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):
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raise ValueError('Either agents and task need to be set or config.')
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if values.get('config'):
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config = json.loads(values.get('config'))
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if not config.get('agents') or not config.get('tasks'):
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raise ValueError('Config should have agents and tasks.')
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values['agents'] = [Agent(**agent) for agent in config['agents']]
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tasks = []
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for task in config['tasks']:
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task_agent = [agt for agt in values['agents'] if agt.role == task['agent']][0]
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del task['agent']
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tasks.append(Task(**task, agent=task_agent))
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values['tasks'] = tasks
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return values
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tasks: Optional[List[Task]] = Field(description="List of tasks")
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agents: Optional[List[Agent]] = Field(description="List of agents in this crew.")
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process: Process = Field(
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description="Process that the crew will follow.", default=Process.sequential
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)
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verbose: bool = Field(
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description="Verbose mode for the Agent Execution", default=False
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)
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config: Optional[Json] = Field(
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description="Configuration of the crew.", default=None
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)
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def kickoff(self) -> str:
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"""
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Kickoff the crew to work on it's tasks.
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Returns:
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output (List[str]): Output of the crew for each task.
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"""
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if self.process == Process.sequential:
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return self.__sequential_loop()
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@root_validator(pre=True)
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def check_config(_cls, values):
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if not values.get("config") and (
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not values.get("agents") and not values.get("tasks")
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):
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raise ValueError("Either agents and task need to be set or config.")
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def __sequential_loop(self) -> str:
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"""
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Loop that executes the sequential process.
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Returns:
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output (str): Output of the crew.
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"""
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task_outcome = None
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for task in self.tasks:
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# Add delegation tools to the task if the agent allows it
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if task.agent.allow_delegation:
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tools = AgentTools(agents=self.agents).tools()
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task.tools += tools
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if values.get("config"):
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config = json.loads(values.get("config"))
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if not config.get("agents") or not config.get("tasks"):
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raise ValueError("Config should have agents and tasks.")
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self.__log(f"\nWorking Agent: {task.agent.role}")
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self.__log(f"Starting Task: {task.description} ...")
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values["agents"] = [Agent(**agent) for agent in config["agents"]]
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task_outcome = task.execute(task_outcome)
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tasks = []
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for task in config["tasks"]:
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task_agent = [
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agt for agt in values["agents"] if agt.role == task["agent"]
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][0]
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del task["agent"]
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tasks.append(Task(**task, agent=task_agent))
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self.__log(f"Task output: {task_outcome}")
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return task_outcome
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def __log(self, message):
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if self.verbose:
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print(message)
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values["tasks"] = tasks
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return values
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def kickoff(self) -> str:
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"""
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Kickoff the crew to work on it's tasks.
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Returns:
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output (List[str]): Output of the crew for each task.
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"""
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if self.process == Process.sequential:
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return self.__sequential_loop()
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def __sequential_loop(self) -> str:
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"""
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Loop that executes the sequential process.
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Returns:
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output (str): Output of the crew.
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"""
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task_outcome = None
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for task in self.tasks:
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# Add delegation tools to the task if the agent allows it
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if task.agent.allow_delegation:
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tools = AgentTools(agents=self.agents).tools()
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task.tools += tools
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self.__log(f"\nWorking Agent: {task.agent.role}")
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self.__log(f"Starting Task: {task.description} ...")
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task_outcome = task.execute(task_outcome)
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|
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self.__log(f"Task output: {task_outcome}")
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return task_outcome
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|
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def __log(self, message):
|
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if self.verbose:
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print(message)
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@@ -1,9 +1,11 @@
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from enum import Enum
|
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|
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|
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class Process(str, Enum):
|
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"""
|
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Class representing the different processes that can be used to tackle tasks
|
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"""
|
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sequential = 'sequential'
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# TODO: consensual = 'consensual'
|
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# TODO: hierarchical = 'hierarchical'
|
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"""
|
||||
Class representing the different processes that can be used to tackle tasks
|
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"""
|
||||
|
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sequential = "sequential"
|
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# TODO: consensual = 'consensual'
|
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# TODO: hierarchical = 'hierarchical'
|
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|
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@@ -2,32 +2,41 @@
|
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|
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from textwrap import dedent
|
||||
from typing import ClassVar
|
||||
from pydantic.v1 import BaseModel
|
||||
|
||||
from langchain.prompts import PromptTemplate
|
||||
from pydantic.v1 import BaseModel
|
||||
|
||||
|
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class Prompts(BaseModel):
|
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"""Prompts for generic agent."""
|
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"""Prompts for generic agent."""
|
||||
|
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TASK_SLICE: ClassVar[str] = dedent("""\
|
||||
TASK_SLICE: ClassVar[str] = dedent(
|
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"""\
|
||||
Begin! This is VERY important to you, your job depends on it!
|
||||
|
||||
Current Task: {input}
|
||||
{agent_scratchpad}
|
||||
""")
|
||||
"""
|
||||
)
|
||||
|
||||
MEMORY_SLICE: ClassVar[str] = dedent("""\
|
||||
MEMORY_SLICE: ClassVar[str] = dedent(
|
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"""\
|
||||
This is the summary of your work so far:
|
||||
{chat_history}
|
||||
""")
|
||||
"""
|
||||
)
|
||||
|
||||
ROLE_PLAYING_SLICE: ClassVar[str] = dedent("""\
|
||||
ROLE_PLAYING_SLICE: ClassVar[str] = dedent(
|
||||
"""\
|
||||
You are {role}.
|
||||
{backstory}
|
||||
|
||||
Your personal goal is: {goal}
|
||||
""")
|
||||
"""
|
||||
)
|
||||
|
||||
TOOLS_SLICE: ClassVar[str] = dedent("""\
|
||||
TOOLS_SLICE: ClassVar[str] = dedent(
|
||||
"""\
|
||||
|
||||
TOOLS:
|
||||
------
|
||||
@@ -50,9 +59,11 @@ class Prompts(BaseModel):
|
||||
Thought: Do I need to use a tool? No
|
||||
Final Answer: [your response here]
|
||||
```
|
||||
""")
|
||||
"""
|
||||
)
|
||||
|
||||
VOTING_SLICE: ClassVar[str] = dedent("""\
|
||||
VOTING_SLICE: ClassVar[str] = dedent(
|
||||
"""\
|
||||
You are working on a crew with your co-workers and need to decide who will execute the task.
|
||||
|
||||
These are your format instructions:
|
||||
@@ -60,16 +71,17 @@ class Prompts(BaseModel):
|
||||
|
||||
These are your co-workers and their roles:
|
||||
{coworkers}
|
||||
""")
|
||||
"""
|
||||
)
|
||||
|
||||
TASK_EXECUTION_WITH_MEMORY_PROMPT: ClassVar[str] = PromptTemplate.from_template(
|
||||
ROLE_PLAYING_SLICE + TOOLS_SLICE + MEMORY_SLICE + TASK_SLICE
|
||||
)
|
||||
TASK_EXECUTION_WITH_MEMORY_PROMPT: ClassVar[str] = PromptTemplate.from_template(
|
||||
ROLE_PLAYING_SLICE + TOOLS_SLICE + MEMORY_SLICE + TASK_SLICE
|
||||
)
|
||||
|
||||
TASK_EXECUTION_PROMPT: ClassVar[str] = PromptTemplate.from_template(
|
||||
ROLE_PLAYING_SLICE + TOOLS_SLICE + TASK_SLICE
|
||||
)
|
||||
TASK_EXECUTION_PROMPT: ClassVar[str] = PromptTemplate.from_template(
|
||||
ROLE_PLAYING_SLICE + TOOLS_SLICE + TASK_SLICE
|
||||
)
|
||||
|
||||
CONSENSUNS_VOTING_PROMPT: ClassVar[str] = PromptTemplate.from_template(
|
||||
ROLE_PLAYING_SLICE + VOTING_SLICE + TASK_SLICE
|
||||
)
|
||||
CONSENSUNS_VOTING_PROMPT: ClassVar[str] = PromptTemplate.from_template(
|
||||
ROLE_PLAYING_SLICE + VOTING_SLICE + TASK_SLICE
|
||||
)
|
||||
|
||||
@@ -1,42 +1,41 @@
|
||||
from typing import List, Optional
|
||||
from pydantic.v1 import BaseModel, Field, root_validator
|
||||
|
||||
from langchain.tools import Tool
|
||||
from pydantic.v1 import BaseModel, Field, root_validator
|
||||
|
||||
from .agent import Agent
|
||||
|
||||
|
||||
class Task(BaseModel):
|
||||
"""
|
||||
Class that represent a task to be executed.
|
||||
"""
|
||||
|
||||
description: str = Field(description="Description of the actual task.")
|
||||
agent: Optional[Agent] = Field(
|
||||
description="Agent responsible for the task.",
|
||||
default=None
|
||||
)
|
||||
tools: Optional[List[Tool]] = Field(
|
||||
description="Tools the agent are limited to use for this task.",
|
||||
default=[]
|
||||
)
|
||||
"""
|
||||
Class that represent a task to be executed.
|
||||
"""
|
||||
|
||||
@root_validator(pre=False)
|
||||
def _set_tools(_cls, values):
|
||||
if (values.get('agent')) and not (values.get('tools')):
|
||||
values['tools'] = values.get('agent').tools
|
||||
return values
|
||||
description: str = Field(description="Description of the actual task.")
|
||||
agent: Optional[Agent] = Field(
|
||||
description="Agent responsible for the task.", default=None
|
||||
)
|
||||
tools: Optional[List[Tool]] = Field(
|
||||
description="Tools the agent are limited to use for this task.", default=[]
|
||||
)
|
||||
|
||||
def execute(self, context: str = None) -> str:
|
||||
"""
|
||||
Execute the task.
|
||||
Returns:
|
||||
output (str): Output of the task.
|
||||
"""
|
||||
if self.agent:
|
||||
return self.agent.execute_task(
|
||||
task = self.description,
|
||||
context = context,
|
||||
tools = self.tools
|
||||
)
|
||||
else:
|
||||
raise Exception(f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, either consensual or hierarchical.")
|
||||
@root_validator(pre=False)
|
||||
def _set_tools(_cls, values):
|
||||
if (values.get("agent")) and not (values.get("tools")):
|
||||
values["tools"] = values.get("agent").tools
|
||||
return values
|
||||
|
||||
def execute(self, context: str = None) -> str:
|
||||
"""
|
||||
Execute the task.
|
||||
Returns:
|
||||
output (str): Output of the task.
|
||||
"""
|
||||
if self.agent:
|
||||
return self.agent.execute_task(
|
||||
task=self.description, context=context, tools=self.tools
|
||||
)
|
||||
else:
|
||||
raise Exception(
|
||||
f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, either consensual or hierarchical."
|
||||
)
|
||||
|
||||
@@ -1,61 +1,74 @@
|
||||
from typing import List, Any
|
||||
from pydantic.v1 import BaseModel, Field
|
||||
from textwrap import dedent
|
||||
from typing import List
|
||||
|
||||
from langchain.tools import Tool
|
||||
from pydantic.v1 import BaseModel, Field
|
||||
|
||||
from ..agent import Agent
|
||||
|
||||
class AgentTools(BaseModel):
|
||||
"""Tools for generic agent."""
|
||||
agents: List[Agent] = Field(description="List of agents in this crew.")
|
||||
|
||||
def tools(self):
|
||||
return [
|
||||
Tool.from_function(
|
||||
func=self.delegate_work,
|
||||
name="Delegate Work to Co-Worker",
|
||||
description=dedent(f"""Useful to delegate a specific task to one of the
|
||||
class AgentTools(BaseModel):
|
||||
"""Tools for generic agent."""
|
||||
|
||||
agents: List[Agent] = Field(description="List of agents in this crew.")
|
||||
|
||||
def tools(self):
|
||||
return [
|
||||
Tool.from_function(
|
||||
func=self.delegate_work,
|
||||
name="Delegate Work to Co-Worker",
|
||||
description=dedent(
|
||||
f"""Useful to delegate a specific task to one of the
|
||||
following co-workers: [{', '.join([agent.role for agent in self.agents])}].
|
||||
The input to this tool should be a pipe (|) separated text of length
|
||||
three, representing the role you want to delegate it to, the task and
|
||||
information necessary. For example, `coworker|task|information`.
|
||||
""")
|
||||
),
|
||||
Tool.from_function(
|
||||
func=self.ask_question,
|
||||
name="Ask Question to Co-Worker",
|
||||
description=dedent(f"""Useful to ask a question, opinion or take from on
|
||||
"""
|
||||
),
|
||||
),
|
||||
Tool.from_function(
|
||||
func=self.ask_question,
|
||||
name="Ask Question to Co-Worker",
|
||||
description=dedent(
|
||||
f"""Useful to ask a question, opinion or take from on
|
||||
of the following co-workers: [{', '.join([agent.role for agent in self.agents])}].
|
||||
The input to this tool should be a pipe (|) separated text of length
|
||||
three, representing the role you want to ask it to, the question and
|
||||
information necessary. For example, `coworker|question|information`.
|
||||
""")
|
||||
),
|
||||
]
|
||||
"""
|
||||
),
|
||||
),
|
||||
]
|
||||
|
||||
def delegate_work(self, command):
|
||||
"""Useful to delegate a specific task to a coworker."""
|
||||
return self.__execute(command)
|
||||
def delegate_work(self, command):
|
||||
"""Useful to delegate a specific task to a coworker."""
|
||||
return self.__execute(command)
|
||||
|
||||
def ask_question(self, command):
|
||||
"""Useful to ask a question, opinion or take from a coworker."""
|
||||
return self.__execute(command)
|
||||
def ask_question(self, command):
|
||||
"""Useful to ask a question, opinion or take from a coworker."""
|
||||
return self.__execute(command)
|
||||
|
||||
def __execute(self, command):
|
||||
"""Execute the command."""
|
||||
try:
|
||||
agent, task, information = command.split("|")
|
||||
except ValueError:
|
||||
return "Error executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|information`."
|
||||
def __execute(self, command):
|
||||
"""Execute the command."""
|
||||
try:
|
||||
agent, task, information = command.split("|")
|
||||
except ValueError:
|
||||
return "Error executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|information`."
|
||||
|
||||
if not agent or not task or not information:
|
||||
return "Error executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|question|information`."
|
||||
if not agent or not task or not information:
|
||||
return "Error executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|question|information`."
|
||||
|
||||
agent = [available_agent for available_agent in self.agents if available_agent.role == agent]
|
||||
agent = [
|
||||
available_agent
|
||||
for available_agent in self.agents
|
||||
if available_agent.role == agent
|
||||
]
|
||||
|
||||
if len(agent) == 0:
|
||||
return "Error executing tool. Co-worker not found, double check the co-worker."
|
||||
if len(agent) == 0:
|
||||
return (
|
||||
"Error executing tool. Co-worker not found, double check the co-worker."
|
||||
)
|
||||
|
||||
agent = agent[0]
|
||||
result = agent.execute_task(task, information)
|
||||
return result
|
||||
agent = agent[0]
|
||||
result = agent.execute_task(task, information)
|
||||
return result
|
||||
|
||||
Reference in New Issue
Block a user