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343 lines
13 KiB
343 lines
13 KiB
"""
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Todo:
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- Add multi-agent selection for a task and then run them automatically
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- Add shared memory for large instances of agents
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"""
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import os
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import uuid
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from datetime import datetime
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from typing import List, Optional
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from loguru import logger
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from pydantic import BaseModel, Field
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from swarms.utils.function_caller_model import OpenAIFunctionCaller
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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.output_type import OutputType
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from swarms.utils.any_to_str import any_to_str
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class AgentResponse(BaseModel):
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"""Response from the boss agent indicating which agent should handle the task"""
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selected_agent: str = Field(
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description="Name of the agent selected to handle the task"
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)
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reasoning: str = Field(
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description="Explanation for why this agent was selected"
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)
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modified_task: Optional[str] = Field(
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None, description="Optional modified version of the task"
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)
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class MultiAgentRouter:
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"""
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Routes tasks to appropriate agents based on their capabilities.
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This class is responsible for managing a pool of agents and routing incoming tasks to the most suitable agent. It uses a boss agent to analyze the task and select the best agent for the job. The boss agent's decision is based on the capabilities and descriptions of the available agents.
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Attributes:
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name (str): The name of the router.
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description (str): A description of the router's purpose.
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agents (dict): A dictionary of agents, where the key is the agent's name and the value is the agent object.
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api_key (str): The API key for OpenAI.
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output_type (str): The type of output expected from the agents. Can be either "json" or "string".
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execute_task (bool): A flag indicating whether the task should be executed by the selected agent.
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boss_system_prompt (str): A system prompt for the boss agent that includes information about all available agents.
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function_caller (OpenAIFunctionCaller): An instance of OpenAIFunctionCaller for calling the boss agent.
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"""
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def __init__(
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self,
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name: str = "swarm-router",
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description: str = "Routes tasks to specialized agents based on their capabilities",
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agents: List[Agent] = [],
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model: str = "gpt-4o-mini",
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temperature: float = 0.1,
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shared_memory_system: callable = None,
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output_type: OutputType = "dict",
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execute_task: bool = True,
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):
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"""
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Initializes the MultiAgentRouter with a list of agents and configuration options.
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Args:
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name (str, optional): The name of the router. Defaults to "swarm-router".
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description (str, optional): A description of the router's purpose. Defaults to "Routes tasks to specialized agents based on their capabilities".
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agents (List[Agent], optional): A list of agents to be managed by the router. Defaults to an empty list.
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model (str, optional): The model to use for the boss agent. Defaults to "gpt-4-0125-preview".
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temperature (float, optional): The temperature for the boss agent's model. Defaults to 0.1.
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output_type (Literal["json", "string"], optional): The type of output expected from the agents. Defaults to "json".
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execute_task (bool, optional): A flag indicating whether the task should be executed by the selected agent. Defaults to True.
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"""
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self.name = name
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self.description = description
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self.shared_memory_system = shared_memory_system
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self.output_type = output_type
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self.execute_task = execute_task
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self.model = model
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self.temperature = temperature
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# Initialize Agents
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self.agents = {agent.name: agent for agent in agents}
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self.conversation = Conversation()
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self.api_key = os.getenv("OPENAI_API_KEY")
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if not self.api_key:
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raise ValueError("OpenAI API key must be provided")
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self.boss_system_prompt = self._create_boss_system_prompt()
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# Initialize the function caller
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self.function_caller = OpenAIFunctionCaller(
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system_prompt=self.boss_system_prompt,
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api_key=self.api_key,
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temperature=temperature,
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)
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def __repr__(self):
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return f"MultiAgentRouter(name={self.name}, agents={list(self.agents.keys())})"
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def query_ragent(self, task: str) -> str:
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"""Query the ResearchAgent"""
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return self.shared_memory_system.query(task)
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def _create_boss_system_prompt(self) -> str:
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"""
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Creates a system prompt for the boss agent that includes information about all available agents.
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Returns:
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str: The system prompt for the boss agent.
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"""
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agent_descriptions = "\n".join(
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[
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f"- {name}: {agent.description}"
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for name, agent in self.agents.items()
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]
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)
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return f"""You are a boss agent responsible for routing tasks to the most appropriate specialized agent.
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Available agents:
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{agent_descriptions}
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Your job is to:
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1. Analyze the incoming task
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2. Select the most appropriate agent based on their descriptions
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3. Provide clear reasoning for your selection
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4. Optionally modify the task to better suit the selected agent's capabilities
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You must respond with JSON that contains:
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- selected_agent: Name of the chosen agent (must be one of the available agents)
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- reasoning: Brief explanation of why this agent was selected
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- modified_task: (Optional) A modified version of the task if needed
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Always select exactly one agent that best matches the task requirements.
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"""
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def find_agent_in_list(self, agent_name: str) -> Optional[Agent]:
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"""
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Find an agent by name in a list of agents.
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Args:
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agent_name (str): The name of the agent to find.
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Returns:
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Optional[Agent]: The agent object if found, otherwise None.
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"""
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for agent in self.agent_list:
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if agent.name == agent_name:
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return agent
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return None
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def route_task(self, task: str) -> dict:
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"""
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Routes a task to the appropriate agent and returns their response.
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Args:
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task (str): The task to be routed.
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Returns:
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dict: A dictionary containing the routing result, including the selected agent, reasoning, and response.
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"""
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try:
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self.conversation.add(role="user", content=task)
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start_time = datetime.now()
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# Get boss decision using function calling
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boss_response = self.function_caller.get_completion(task)
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boss_response_str = any_to_str(boss_response)
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self.conversation.add(
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role="assistant", content=boss_response_str
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)
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# Validate that the selected agent exists
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if boss_response.selected_agent not in self.agents:
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raise ValueError(
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f"Boss selected unknown agent: {boss_response.selected_agent}"
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)
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# Get the selected agent
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selected_agent = self.agents[boss_response.selected_agent]
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# Use the modified task if provided, otherwise use original task
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final_task = boss_response.modified_task or task
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# Execute the task with the selected agent if enabled
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execution_start = datetime.now()
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agent_response = None
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execution_time = 0
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if self.execute_task:
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# Use the agent's run method directly
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agent_response = selected_agent.run(final_task)
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self.conversation.add(
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role=selected_agent.name, content=agent_response
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)
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execution_time = (
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datetime.now() - execution_start
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).total_seconds()
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else:
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logger.info(
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"Task execution skipped (execute_task=False)"
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)
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total_time = (datetime.now() - start_time).total_seconds()
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result = {
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"id": str(uuid.uuid4()),
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"timestamp": datetime.now().isoformat(),
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"task": {
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"original": task,
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"modified": (
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final_task
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if boss_response.modified_task
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else None
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),
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},
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"boss_decision": {
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"selected_agent": boss_response.selected_agent,
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"reasoning": boss_response.reasoning,
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},
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"execution": {
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"agent_name": selected_agent.name,
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"agent_id": selected_agent.id,
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"was_executed": self.execute_task,
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"response": (
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agent_response if self.execute_task else None
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),
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"execution_time": (
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execution_time if self.execute_task else None
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),
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},
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"total_time": total_time,
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}
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logger.info(
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f"Successfully routed task to {selected_agent.name}"
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)
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return result
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except Exception as e:
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logger.error(f"Error routing task: {str(e)}")
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raise
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def run(self, task: str):
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"""Route a task to the appropriate agent and return the result"""
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return self.route_task(task)
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def __call__(self, task: str):
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"""Route a task to the appropriate agent and return the result"""
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return self.route_task(task)
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def batch_run(self, tasks: List[str] = []):
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"""Batch route tasks to the appropriate agents"""
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results = []
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for task in tasks:
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try:
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result = self.route_task(task)
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results.append(result)
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except Exception as e:
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logger.error(f"Error routing task: {str(e)}")
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return results
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def concurrent_batch_run(self, tasks: List[str] = []):
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"""Concurrently route tasks to the appropriate agents"""
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import concurrent.futures
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from concurrent.futures import ThreadPoolExecutor
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results = []
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with ThreadPoolExecutor() as executor:
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futures = [
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executor.submit(self.route_task, task)
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for task in tasks
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]
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for future in concurrent.futures.as_completed(futures):
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try:
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result = future.result()
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results.append(result)
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except Exception as e:
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logger.error(f"Error routing task: {str(e)}")
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return results
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# # Example usage:
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# if __name__ == "__main__":
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# # Define some example agents
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# agents = [
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# Agent(
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# agent_name="ResearchAgent",
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# description="Specializes in researching topics and providing detailed, factual information",
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# system_prompt="You are a research specialist. Provide detailed, well-researched information about any topic, citing sources when possible.",
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# model_name="openai/gpt-4o",
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# ),
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# Agent(
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# agent_name="CodeExpertAgent",
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# description="Expert in writing, reviewing, and explaining code across multiple programming languages",
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# system_prompt="You are a coding expert. Write, review, and explain code with a focus on best practices and clean code principles.",
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# model_name="openai/gpt-4o",
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# ),
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# Agent(
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# agent_name="WritingAgent",
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# description="Skilled in creative and technical writing, content creation, and editing",
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# system_prompt="You are a writing specialist. Create, edit, and improve written content while maintaining appropriate tone and style.",
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# model_name="openai/gpt-4o",
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# ),
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# ]
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# # Initialize routers with different configurations
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# router_execute = MultiAgentRouter(agents=agents, execute_task=True)
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# # router_no_execute = MultiAgentRouter(agents=agents, execute_task=False)
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# # Example task
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# task = "Write a Python function to calculate fibonacci numbers"
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# try:
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# # Process the task with execution
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# print("\nWith task execution:")
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# result_execute = router_execute.route_task(task)
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# print(
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# f"Selected Agent: {result_execute['boss_decision']['selected_agent']}"
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# )
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# print(
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# f"Reasoning: {result_execute['boss_decision']['reasoning']}"
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# )
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# if result_execute["execution"]["response"]:
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# print(
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# f"Response Preview: {result_execute['execution']['response'][:200]}..."
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# )
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# print(
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# f"Execution Time: {result_execute['execution']['execution_time']:.2f}s"
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# )
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# print(f"Total Time: {result_execute['total_time']:.2f}s")
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# except Exception as e:
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# print(f"Error occurred: {str(e)}")
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