You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
239 lines
7.5 KiB
239 lines
7.5 KiB
from typing import List, Callable
|
|
|
|
from swarms.structs.agent import Agent
|
|
from swarms.utils.logger import logger
|
|
from swarms.structs.base_swarm import BaseSwarm
|
|
from swarms.structs.conversation import Conversation
|
|
|
|
|
|
# def select_next_speaker_bid(
|
|
# step: int,
|
|
# agents: List[Agent],
|
|
# ) -> int:
|
|
# """Selects the next speaker."""
|
|
# bids = []
|
|
# for agent in agents:
|
|
# bid = ask_for_bid(agent)
|
|
# bids.append(bid)
|
|
# max_value = max(bids)
|
|
# max_indices = [i for i, x in enumerate(bids) if x == max_value]
|
|
# idx = random.choice(max_indices)
|
|
# return idx
|
|
|
|
|
|
def select_next_speaker_roundtable(step: int, agents: List[Agent]) -> int:
|
|
"""Selects the next speaker."""
|
|
return step % len(agents)
|
|
|
|
|
|
def select_next_speaker_director(
|
|
step: int, agents: List[Agent], director: Agent
|
|
) -> int:
|
|
# if the step if even => director
|
|
# => director selects next speaker
|
|
if step % 2 == 1:
|
|
idx = 0
|
|
else:
|
|
idx = director.select_next_speaker() + 1
|
|
return idx
|
|
|
|
|
|
def run_director(self, task: str):
|
|
"""Runs the multi-agent collaboration with a director."""
|
|
n = 0
|
|
self.reset()
|
|
self.inject("Debate Moderator", task)
|
|
print("(Debate Moderator): \n")
|
|
|
|
while n < self.max_loops:
|
|
name, message = self.step()
|
|
print(f"({name}): {message}\n")
|
|
n += 1
|
|
|
|
|
|
# [MAYBE]: Add type hints
|
|
class MultiAgentCollaboration(BaseSwarm):
|
|
"""
|
|
Multi-agent collaboration class.
|
|
|
|
Attributes:
|
|
agents (List[Agent]): The agents in the collaboration.
|
|
selection_function (callable): The function that selects the next speaker.
|
|
Defaults to select_next_speaker.
|
|
max_loops (int): The maximum number of iterations. Defaults to 10.
|
|
autosave (bool): Whether to autosave the state of all agents. Defaults to True.
|
|
saved_file_path_name (str): The path to the saved file. Defaults to
|
|
"multi_agent_collab.json".
|
|
stopping_token (str): The token that stops the collaboration. Defaults to
|
|
"<DONE>".
|
|
results (list): The results of the collaboration. Defaults to [].
|
|
logger (logging.Logger): The logger. Defaults to logger.
|
|
logging (bool): Whether to log the collaboration. Defaults to True.
|
|
|
|
|
|
Methods:
|
|
reset: Resets the state of all agents.
|
|
inject: Injects a message into the collaboration.
|
|
inject_agent: Injects an agent into the collaboration.
|
|
step: Steps through the collaboration.
|
|
ask_for_bid: Asks an agent for a bid.
|
|
select_next_speaker: Selects the next speaker.
|
|
run: Runs the collaboration.
|
|
format_results: Formats the results of the run method.
|
|
|
|
|
|
Usage:
|
|
>>> from swarms.models import OpenAIChat
|
|
>>> from swarms.structs import Agent
|
|
>>> from swarms.swarms.multi_agent_collab import MultiAgentCollaboration
|
|
>>>
|
|
>>> # Initialize the language model
|
|
>>> llm = OpenAIChat(
|
|
>>> temperature=0.5,
|
|
>>> )
|
|
>>>
|
|
>>>
|
|
>>> ## Initialize the workflow
|
|
>>> agent = Agent(llm=llm, max_loops=1, dashboard=True)
|
|
>>>
|
|
>>> # Run the workflow on a task
|
|
>>> out = agent.run("Generate a 10,000 word blog on health and wellness.")
|
|
>>>
|
|
>>> # Initialize the multi-agent collaboration
|
|
>>> swarm = MultiAgentCollaboration(
|
|
>>> agents=[agent],
|
|
>>> max_loops=4,
|
|
>>> )
|
|
>>>
|
|
>>> # Run the multi-agent collaboration
|
|
>>> swarm.run()
|
|
>>>
|
|
>>> # Format the results of the multi-agent collaboration
|
|
>>> swarm.format_results(swarm.results)
|
|
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
name: str = "MultiAgentCollaboration",
|
|
description: str = "A multi-agent collaboration.",
|
|
director: Agent = None,
|
|
agents: List[Agent] = None,
|
|
select_next_speaker: Callable = None,
|
|
max_loops: int = 10,
|
|
autosave: bool = True,
|
|
saved_file_path_name: str = "multi_agent_collab.json",
|
|
stopping_token: str = "<DONE>",
|
|
logging: bool = True,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
super().__init__(
|
|
name=name,
|
|
description=description,
|
|
agents=agents,
|
|
*args,
|
|
**kwargs,
|
|
)
|
|
self.name = name
|
|
self.description = description
|
|
self.director = director
|
|
self.agents = agents
|
|
self.select_next_speaker = select_next_speaker
|
|
self._step = 0
|
|
self.max_loops = max_loops
|
|
self.autosave = autosave
|
|
self.saved_file_path_name = saved_file_path_name
|
|
self.stopping_token = stopping_token
|
|
self.results = []
|
|
self.logger = logger
|
|
self.logging = logging
|
|
|
|
# Conversation
|
|
self.conversation = Conversation(
|
|
time_enabled=True, *args, **kwargs
|
|
)
|
|
|
|
def default_select_next_speaker(
|
|
self, step: int, agents: List[Agent]
|
|
) -> int:
|
|
"""Default speaker selection function."""
|
|
return step % len(agents)
|
|
|
|
def inject(self, name: str, message: str):
|
|
"""Injects a message into the multi-agent collaboration."""
|
|
for agent in self.agents:
|
|
self.conversation.add(name, message)
|
|
agent.run(self.conversation.return_history_as_string())
|
|
self._step += 1
|
|
|
|
def step(self) -> str:
|
|
"""Steps through the multi-agent collaboration."""
|
|
speaker_idx = self.select_next_speaker(self._step, self.agents)
|
|
speaker = self.agents[speaker_idx]
|
|
message = speaker.send()
|
|
|
|
for receiver in self.agents:
|
|
self.conversation.add(speaker.name, message)
|
|
receiver.run(self.conversation.return_history_as_string())
|
|
|
|
self._step += 1
|
|
|
|
if self.logging:
|
|
self.log_step(speaker, message)
|
|
|
|
return self.conversation.return_history_as_string()
|
|
|
|
def log_step(self, speaker: str, response: str):
|
|
"""Logs the step of the multi-agent collaboration."""
|
|
self.logger.info(f"{speaker.name}: {response}")
|
|
|
|
def run(self, task: str, *args, **kwargs):
|
|
"""Runs the multi-agent collaboration."""
|
|
for _ in range(self.max_loops):
|
|
result = self.step()
|
|
if self.autosave:
|
|
self.save_state()
|
|
if self.stopping_token in result:
|
|
break
|
|
return self.conversation.return_history_as_string()
|
|
|
|
# def format_results(self, results):
|
|
# """Formats the results of the run method"""
|
|
# formatted_results = "\n".join(
|
|
# [
|
|
# f"{result['agent']} responded: {result['response']}"
|
|
# for result in results
|
|
# ]
|
|
# )
|
|
# return formatted_results
|
|
|
|
# def save(self):
|
|
# """Saves the state of all agents."""
|
|
# state = {
|
|
# "step": self._step,
|
|
# "results": [
|
|
# {"agent": r["agent"].name, "response": r["response"]}
|
|
# for r in self.results
|
|
# ],
|
|
# }
|
|
|
|
# with open(self.saved_file_path_name, "w") as file:
|
|
# json.dump(state, file)
|
|
|
|
# def load(self):
|
|
# """Loads the state of all agents."""
|
|
# with open(self.saved_file_path_name) as file:
|
|
# state = json.load(file)
|
|
# self._step = state["step"]
|
|
# self.results = state["results"]
|
|
# return state
|
|
|
|
# def __repr__(self):
|
|
# return (
|
|
# f"MultiAgentCollaboration(agents={self.agents},"
|
|
# f" selection_function={self.select_next_speaker},"
|
|
# f" max_loops={self.max_loops}, autosave={self.autosave},"
|
|
# f" saved_file_path_name={self.saved_file_path_name})"
|
|
# )
|