Kye
9 months ago
parent
4fbd2281f4
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d572cafce6
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from swarms import Anthropic, Agent, SequentialWorkflow
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# Initialize the language model agent (e.g., GPT-3)
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llm = Anthropic()
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# Initialize agents for individual tasks
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agent1 = Agent(
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agent_name="Blog generator", llm=llm, max_loops=1, dashboard=False
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)
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agent2 = Agent(
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agent_name="summarizer", llm=llm, max_loops=1, dashboard=False
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)
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# Create the Sequential workflow
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workflow = SequentialWorkflow(
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max_loops=1, objective="Create a full blog and then summarize it"
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)
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# Add tasks to the workflow
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workflow.add(
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"Generate a 10,000 word blog on health and wellness.", agent1
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) # this task will be executed task,
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workflow.add(
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"Summarize the generated blog", agent2
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) # then the next agent will accomplish this task
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# Run the workflow
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out = workflow.run()
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print(f"{out}")
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@ -1,203 +1,134 @@
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from dataclasses import dataclass
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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from typing import List, Optional
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from termcolor import colored
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# from swarms.utils.logger import logger
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from swarms.structs.agent import Agent
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from swarms.structs.agent import Agent
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from swarms.structs.conversation import Conversation
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from swarms.structs.conversation import Conversation
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from swarms.structs.task import Task
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from swarms.utils.loguru_logger import logger
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from swarms.utils.loguru_logger import logger
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from swarms.utils.try_except_wrapper import try_except_wrapper
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# SequentialWorkflow class definition using dataclasses
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@dataclass
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@dataclass
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class SequentialWorkflow:
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class SequentialWorkflow:
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"""
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name: str = "Sequential Workflow"
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SequentialWorkflow class for running a sequence of task_pool using N number of autonomous agents.
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Args:
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max_loops (int): The maximum number of times to run the workflow.
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dashboard (bool): Whether to display the dashboard for the workflow.
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Attributes:
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task_pool (List[Task]): The list of task_pool to execute.
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max_loops (int): The maximum number of times to run the workflow.
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dashboard (bool): Whether to display the dashboard for the workflow.
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Examples:
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>>> from swarms.models import OpenAIChat
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>>> from swarms.structs import SequentialWorkflow
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>>> llm = OpenAIChat(openai_api_key="")
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>>> workflow = SequentialWorkflow(max_loops=1)
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>>> workflow.add("What's the weather in miami", llm)
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>>> workflow.add("Create a report on these metrics", llm)
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>>> workflow.run()
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>>> workflow.task_pool
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"""
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name: str = None
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description: str = None
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description: str = None
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task_pool: List[Task] = None
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objective: str = None
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max_loops: int = 1
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max_loops: int = 1
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autosave: bool = False
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autosave: bool = False
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saved_state_filepath: Optional[str] = "sequential_workflow_state.json"
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saved_state_filepath: Optional[str] = "sequential_workflow_state.json"
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restore_state_filepath: Optional[str] = None
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restore_state_filepath: Optional[str] = None
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dashboard: bool = False
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dashboard: bool = False
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agents: List[Agent] = None
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agent_pool: List[Agent] = field(default_factory=list)
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# task_pool: List[str] = field(
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# default_factory=list
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# ) # List to store tasks
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def __post_init__(self):
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def __post_init__(self):
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self.conversation = Conversation(
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self.conversation = Conversation(
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system_prompt=f"Objective: {self.description}",
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time_enabled=True,
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time_enabled=True,
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autosave=True,
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autosave=True,
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)
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)
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# Logging
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# If objective exists then set it
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logger.info("Number of agents activated:")
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if self.objective is not None:
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if self.agents:
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self.conversation.system_prompt = self.objective
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logger.info(f"Agents: {len(self.agents)}")
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else:
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def workflow_bootup(self):
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logger.info("No agents activated.")
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logger.info(f"{self.name} is activating...")
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if self.task_pool:
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for agent in self.agent_pool:
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logger.info(f"Task Pool Size: {len(self.task_pool)}")
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logger.info(f"Agent {agent.agent_name} Activated")
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else:
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logger.info("Task Pool is empty.")
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@try_except_wrapper
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def add(self, task: str, agent: Agent, *args, **kwargs):
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def add(
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self.agent_pool.append(agent)
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self,
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# self.task_pool.append(
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task: Optional[Task] = None,
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# task
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tasks: Optional[List[Task]] = None,
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# ) # Store tasks corresponding to each agent
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*args,
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**kwargs,
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return self.conversation.add(
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) -> None:
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role=agent.agent_name, content=task, *args, **kwargs
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"""
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)
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Add a task to the workflow.
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Args:
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agent (Union[Callable, Agent]): The model or agent to execute the task.
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task (str): The task description or the initial input for the Agent.
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*args: Additional arguments to pass to the task execution.
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**kwargs: Additional keyword arguments to pass to the task execution.
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"""
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for agent in self.agents:
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out = agent(str(self.description))
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self.conversation.add(agent.agent_name, out)
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prompt = self.conversation.return_history_as_string()
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out = agent(prompt)
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return out
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def reset_workflow(self) -> None:
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def reset_workflow(self) -> None:
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"""Resets the workflow by clearing the results of each task."""
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self.conversation = {}
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try:
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for task in self.task_pool:
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# @try_except_wrapper
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task.result = None
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# WITH TASK POOL
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logger.info(
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# def run(self):
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f"[INFO][SequentialWorkflow] Reset task {task} in"
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# if not self.agent_pool:
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" workflow"
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# raise ValueError("No agents have been added to the workflow.")
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)
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except Exception as error:
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# self.workflow_bootup()
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logger.error(
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# loops = 0
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colored(f"Error resetting workflow: {error}", "red"),
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# prompt = None # Initialize prompt to None; will be updated with the output of each agent
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)
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# while loops < self.max_loops:
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# for i, agent in enumerate(self.agent_pool):
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def get_task_results(self) -> Dict[str, Any]:
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# task = (
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"""
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# self.task_pool[i] if prompt is None else prompt
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Returns the results of each task in the workflow.
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# ) # Use initial task or the output from the previous agent
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# logger.info(
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Returns:
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# f"Agent: {agent.agent_name} {i+1} is executing the task"
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Dict[str, Any]: The results of each task in the workflow
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# )
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"""
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# logger.info("\n")
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try:
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# output = agent.run(task)
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return {
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# if output is None:
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task.description: task.result for task in self.task_pool
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# logger.error(
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}
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# f"Agent {i+1} returned None for task: {task}"
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except Exception as error:
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# )
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logger.error(
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# raise ValueError(f"Agent {i+1} returned None.")
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colored(f"Error getting task results: {error}", "red"),
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# self.conversation.add(agent.agent_name, output)
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)
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# prompt = output # Update prompt with current agent's output to pass to the next agent
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# logger.info(f"Prompt: {prompt}")
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def remove_task(self, task: Task) -> None:
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# loops += 1
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"""Remove task_pool from sequential workflow"""
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# return self.conversation.return_history_as_string()
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try:
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@try_except_wrapper
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self.task_pool.remove(task)
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def run(self):
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logger.info(
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if not self.agent_pool:
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f"[INFO][SequentialWorkflow] Removed task {task} from"
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raise ValueError("No agents have been added to the workflow.")
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" workflow"
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)
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except Exception as error:
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logger.error(
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colored(
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f"Error removing task from workflow: {error}",
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"red",
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),
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)
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def run(self) -> None:
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"""
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Run the workflow.
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Raises:
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ValueError: If an Agent instance is used as a task and the 'task' argument is not provided.
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"""
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self.workflow_bootup()
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self.workflow_bootup()
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loops = 0
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loops = 0
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while loops < self.max_loops:
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while loops < self.max_loops:
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for i, agent in enumerate(self.agents):
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previous_output = None # Initialize to None; will hold the output of the previous agent
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logger.info(f"Agent {i+1} is executing the task.")
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for i, agent in enumerate(self.agent_pool):
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out = agent(self.description)
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# Fetch the last task specific to this agent from the conversation history
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self.conversation.add(agent.agent_name, str(out))
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tasks_for_agent = [
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prompt = self.conversation.return_history_as_string()
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msg["content"]
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print(prompt)
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for msg in self.conversation.conversation_history
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print("Next agent...........")
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if msg["role"] == agent.agent_name
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out = agent(prompt)
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]
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task = tasks_for_agent[-1] if tasks_for_agent else None
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return out
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# try:
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if task is None and previous_output is not None:
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# self.workflow_bootup()
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# If no specific task for this agent, use the output from the previous agent
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# loops = 0
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task = previous_output
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# while loops < self.max_loops:
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# for i in range(len(self.task_pool)):
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if task is None:
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# task = self.task_pool[i]
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# If no initial task is found, and there's no previous output, log error and skip this agent
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# # Check if the current task can be executed
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logger.error(
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# if task.result is None:
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f"No initial task found for agent {agent.agent_name}, and no previous output to use."
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# # Get the inputs for the current task
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)
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# task.context(task)
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continue
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# result = task.execute()
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logger.info(
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f" \n Agent {i+1} ({agent.agent_name}) is executing the task: {task} \n"
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# # Pass the inputs to the next task
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)
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# if i < len(self.task_pool) - 1:
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# next_task = self.task_pool[i + 1]
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# Space the log
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# next_task.description = result
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output = agent.run(task)
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# # Execute the current task
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if output is None:
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# task.execute()
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logger.error(
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f"Agent {agent.agent_name} returned None for task: {task}"
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# # Autosave the workflow state
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)
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# if self.autosave:
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raise ValueError(
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# self.save_workflow_state(
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f"Agent {agent.agent_name} returned None."
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# "sequential_workflow_state.json"
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)
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# )
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# Update the conversation history with the new output using agent's role
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# self.workflow_shutdown()
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self.conversation.add(
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# loops += 1
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role=agent.agent_name, content=output
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# except Exception as e:
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)
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# logger.error(
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previous_output = output # Update the previous_output to pass to the next agent
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# colored(
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# (
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loops += 1
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# "Error initializing the Sequential workflow:"
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return self.conversation.return_history_as_string()
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# f" {e} try optimizing your inputs like the"
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# " agent class and task description"
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# ),
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# "red",
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# attrs=["bold", "underline"],
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# )
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# )
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