Kye Gomez
5 months ago
parent
0be196b39f
commit
0220190874
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from swarms import OpenAIFunctionCaller
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from swarms.structs.hiearchical_swarm import (
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HierarchicalAgentSwarm,
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SwarmSpec,
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HIEARCHICAL_AGENT_SYSTEM_PROMPT,
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)
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director = (
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OpenAIFunctionCaller(
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system_prompt=HIEARCHICAL_AGENT_SYSTEM_PROMPT,
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max_tokens=3000,
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temperature=0.4,
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base_model=SwarmSpec,
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parallel_tool_calls=False,
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),
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)
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# Initialize the hierarchical agent swarm with the necessary parameters
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swarm = HierarchicalAgentSwarm(
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name="Hierarchical Swarm Example",
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description="A swarm of agents to promote the swarms workshop",
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director=director,
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max_loops=1,
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create_agents_on=True,
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)
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# Run the swarm with a task
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agents = swarm.run(
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"""
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Create a swarm of agents for a marketing campaign to promote
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the swarms workshop: [Workshop][Automating Business Operations with Hierarchical Agent Swarms][Swarms Framework + GPT4o],
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create agents for twitter, linkedin, and emails, facebook, instagram.
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The date is Saturday, August 17 4:00 PM - 5:00 PM
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Link is: https://lu.ma/ew4r4s3i
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"""
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)
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import os
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from swarms import Agent, OpenAIChat
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from swarms.prompts.finance_agent_sys_prompt import (
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FINANCIAL_AGENT_SYS_PROMPT,
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)
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from swarms.structs.a_star_swarm import AStarSwarm
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# Set up the model as provided
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api_key = os.getenv("OPENAI_API_KEY")
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model = OpenAIChat(
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api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
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)
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# Heuristic example (can be customized)
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def example_heuristic(agent: Agent) -> float:
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"""
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Example heuristic that prioritizes agents based on some custom logic.
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Args:
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agent (Agent): The agent to evaluate.
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Returns:
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float: The priority score for the agent.
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"""
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# Example heuristic: prioritize based on the length of the agent's name (as a proxy for complexity)
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return len(agent.agent_name)
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# Initialize root agent
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root_agent = Agent(
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agent_name="Financial-Analysis-Agent",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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llm=model,
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max_loops=2,
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autosave=True,
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dashboard=False,
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verbose=True,
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streaming_on=True,
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dynamic_temperature_enabled=True,
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saved_state_path="finance_agent.json",
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user_name="swarms_corp",
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retry_attempts=3,
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context_length=200000,
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)
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# List of child agents
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child_agents = [
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Agent(
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agent_name="Child-Agent-1",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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llm=model,
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max_loops=2,
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autosave=True,
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dashboard=False,
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verbose=True,
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streaming_on=True,
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dynamic_temperature_enabled=True,
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saved_state_path="finance_agent_child_1.json",
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user_name="swarms_corp",
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retry_attempts=3,
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context_length=200000,
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),
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Agent(
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agent_name="Child-Agent-2",
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system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
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llm=model,
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max_loops=2,
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autosave=True,
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dashboard=False,
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verbose=True,
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streaming_on=True,
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dynamic_temperature_enabled=True,
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saved_state_path="finance_agent_child_2.json",
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user_name="swarms_corp",
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retry_attempts=3,
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context_length=200000,
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),
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]
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# Create the A* swarm
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swarm = AStarSwarm(
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root_agent=root_agent,
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child_agents=child_agents,
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heauristic=example_heuristic,
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)
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# Run the task with the heuristic
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result = swarm.run(
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"What are the components of a startups stock incentive equity plan",
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)
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print(result)
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# Visualize the communication flow
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swarm.visualize()
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import os
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from swarms import Agent, OpenAIChat
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from swarms.structs.dfs_search_swarm import DFSSwarm
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# Get the OpenAI API key from the environment variable
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api_key = os.getenv("OPENAI_API_KEY")
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# Create an instance of the OpenAIChat class for each agent
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model = OpenAIChat(
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api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
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)
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# Initialize multiple agents
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agent1 = Agent(
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agent_name="Agent-1",
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system_prompt="Analyze the financial components of a startup's stock incentives.",
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llm=model,
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# max_loops=2,
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# autosave=True,
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dynamic_temperature_enabled=True,
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verbose=True,
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streaming_on=True,
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user_name="swarms_corp",
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)
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agent2 = Agent(
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agent_name="Agent-2",
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system_prompt="Refine the analysis and identify any potential risks or benefits.",
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llm=model,
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# max_loops=2,
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# autosave=True,
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dynamic_temperature_enabled=True,
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verbose=True,
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streaming_on=True,
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user_name="swarms_corp",
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)
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# Add more agents as needed
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# agent3 = ...
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# agent4 = ...
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# Create the swarm with the agents
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dfs_swarm = DFSSwarm(agents=[agent1, agent2])
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# Run the DFS swarm on a task
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result = dfs_swarm.run(
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"Start with analyzing the financial components of a startup's stock incentives."
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)
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print("Final Result:", result)
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from typing import List, Any
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from loguru import logger
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from pydantic import BaseModel, Field
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from swarms.structs.base_swarm import BaseSwarm
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from swarms.structs.agent import Agent
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from swarms.structs.concat import concat_strings
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from swarms.structs.agent_registry import AgentRegistry
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from swarms.models.base_llm import BaseLLM
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from swarms.structs.conversation import Conversation
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# Example usage:
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HIEARCHICAL_AGENT_SYSTEM_PROMPT = """
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Here's a full-fledged system prompt for a director boss agent, complete with instructions and many-shot examples:
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---
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**System Prompt: Director Boss Agent**
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### Role:
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You are a Director Boss Agent responsible for orchestrating a swarm of worker agents. Your primary duty is to serve the user efficiently, effectively, and skillfully. You dynamically create new agents when necessary or utilize existing agents, assigning them tasks that align with their capabilities. You must ensure that each agent receives clear, direct, and actionable instructions tailored to their role.
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### Key Responsibilities:
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1. **Task Delegation:** Assign tasks to the most relevant agent. If no relevant agent exists, create a new one with an appropriate name and system prompt.
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2. **Efficiency:** Ensure that tasks are completed swiftly and with minimal resource expenditure.
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3. **Clarity:** Provide orders that are simple, direct, and actionable. Avoid ambiguity.
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4. **Dynamic Decision Making:** Assess the situation and choose the most effective path, whether that involves using an existing agent or creating a new one.
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5. **Monitoring:** Continuously monitor the progress of each agent and provide additional instructions or corrections as necessary.
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### Instructions:
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- **Identify the Task:** Analyze the input task to determine its nature and requirements.
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- **Agent Selection/Creation:**
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- If an agent is available and suited for the task, assign the task to that agent.
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- If no suitable agent exists, create a new agent with a relevant system prompt.
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- **Task Assignment:** Provide the selected agent with explicit and straightforward instructions.
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- **Reasoning:** Justify your decisions when selecting or creating agents, focusing on the efficiency and effectiveness of task completion.
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"""
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class AgentSpec(BaseModel):
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"""
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A class representing the specifications of an agent.
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Attributes:
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agent_name (str): The name of the agent.
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system_prompt (str): The system prompt for the agent.
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agent_description (str): The description of the agent.
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max_tokens (int): The maximum number of tokens to generate in the API response.
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temperature (float): A parameter that controls the randomness of the generated text.
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context_window (int): The context window for the agent.
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task (str): The main task for the agent.
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"""
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agent_name: str = Field(
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...,
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description="The name of the agent.",
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)
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system_prompt: str = Field(
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...,
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description="The system prompt for the agent. Write an extremely detailed system prompt for the agent.",
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)
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agent_description: str = Field(
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...,
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description="The description of the agent.",
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)
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task: str = Field(
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...,
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description="The main task for the agent.",
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)
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# class AgentTeam(BaseModel):
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# agents: List[AgentSpec] = Field(
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# ...,
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# description="The list of agents in the team",
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# )
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# flow: str = Field(
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# ...,
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# description="Agent Name -> ",
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# )
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# Schema to send orders to the agents
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class HierarchicalOrderCall(BaseModel):
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agent_name: str = Field(
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...,
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description="The name of the agent to assign the task to.",
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)
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task: str = Field(
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...,
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description="The main specific task to be assigned to the agent. Be very specific and direct.",
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)
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# For not agent creation
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class CallTeam(BaseModel):
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# swarm_name: str = Field(
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# ...,
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# description="The name of the swarm: e.g., 'Marketing Swarm' or 'Finance Swarm'",
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# )
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rules: str = Field(
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...,
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description="The rules for all the agents in the swarm: e.g., All agents must return code. Be very simple and direct",
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)
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plan: str = Field(
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...,
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description="The plan for the swarm: e.g., First create the agents, then assign tasks, then monitor progress",
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)
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orders: List[HierarchicalOrderCall]
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class SwarmSpec(BaseModel):
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"""
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A class representing the specifications of a swarm of agents.
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Attributes:
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multiple_agents (List[AgentSpec]): The list of agents in the swarm.
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"""
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swarm_name: str = Field(
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...,
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description="The name of the swarm: e.g., 'Marketing Swarm' or 'Finance Swarm'",
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)
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multiple_agents: List[AgentSpec]
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rules: str = Field(
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...,
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description="The rules for all the agents in the swarm: e.g., All agents must return code. Be very simple and direct",
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)
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plan: str = Field(
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...,
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description="The plan for the swarm: e.g., First create the agents, then assign tasks, then monitor progress",
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)
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class HierarchicalAgentSwarm(BaseSwarm):
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"""
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A class to create and manage a hierarchical swarm of agents.
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Methods:
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__init__(system_prompt, max_tokens, temperature, base_model, parallel_tool_calls): Initializes the function caller.
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create_agent(agent_name, system_prompt, agent_description, max_tokens, temperature, context_window): Creates an individual agent.
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parse_json_for_agents_then_create_agents(function_call): Parses a JSON function call to create multiple agents.
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run(task): Runs the function caller to create and execute agents based on the provided task.
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"""
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def __init__(
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self,
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name: str = "HierarchicalAgentSwarm",
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description: str = "A swarm of agents that can be used to distribute tasks to a team of agents.",
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director: Any = None,
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agents: List[Agent] = None,
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max_loops: int = 1,
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create_agents_on: bool = False,
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template_worker_agent: Agent = None,
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director_planning_prompt: str = None,
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template_base_worker_llm: BaseLLM = None,
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swarm_history: str = None,
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*args,
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**kwargs,
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):
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"""
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Initializes the HierarchicalAgentSwarm with an OpenAIFunctionCaller.
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Args:
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system_prompt (str): The system prompt for the function caller.
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max_tokens (int): The maximum number of tokens to generate in the API response.
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temperature (float): The temperature setting for text generation.
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base_model (BaseModel): The base model for the function caller.
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parallel_tool_calls (bool): Whether to run tool calls in parallel.
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"""
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super().__init__(
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name=name,
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description=description,
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agents=agents,
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*args,
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**kwargs,
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)
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self.name = name
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self.description = description
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self.director = director
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self.agents = agents
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self.max_loops = max_loops
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self.create_agents_on = create_agents_on
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self.template_worker_agent = template_worker_agent
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self.director_planning_prompt = director_planning_prompt
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self.template_base_worker_llm = template_base_worker_llm
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self.swarm_history = swarm_history
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# Check if the agents are set
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self.agents_check()
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# Agent Registry
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self.agent_registry = AgentRegistry()
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# Add agents to the registry
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self.add_agents_into_registry(self.agents)
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# Swarm History
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self.conversation = Conversation(time_enabled=True)
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self.swarm_history = self.conversation.return_history_as_string()
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def agents_check(self):
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if self.director is None:
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raise ValueError("The director is not set.")
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if len(self.agents) == 0:
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self.create_agents_on = True
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if len(self.agents) > 0:
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self.director.base_model = CallTeam
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self.director.system_prompt = HIEARCHICAL_AGENT_SYSTEM_PROMPT
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if self.max_loops == 0:
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raise ValueError("The max_loops is not set.")
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def add_agents_into_registry(self, agents: List[Agent]):
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"""
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add_agents_into_registry: Add agents into the agent registry.
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Args:
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agents (List[Agent]): A list of agents to add into the registry.
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Returns:
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None
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"""
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for agent in agents:
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self.agent_registry.add(agent)
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def create_agent(
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self,
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agent_name: str,
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system_prompt: str,
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agent_description: str,
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task: str = None,
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) -> str:
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"""
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Creates an individual agent.
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Args:
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agent_name (str): The name of the agent.
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system_prompt (str): The system prompt for the agent.
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agent_description (str): The description of the agent.
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max_tokens (int): The maximum number of tokens to generate.
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temperature (float): The temperature for text generation.
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context_window (int): The context window size for the agent.
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Returns:
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Agent: An instantiated agent object.
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"""
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# name = agent_name.replace(" ", "_")
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logger.info(f"Creating agent: {agent_name}")
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agent_name = Agent(
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agent_name=agent_name,
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llm=self.template_base_worker_llm, # Switch to model router here later
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system_prompt=system_prompt,
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agent_description=agent_description,
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retry_attempts=1,
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verbose=False,
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dashboard=False,
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)
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self.agents.append(agent_name)
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logger.info(f"Running agent: {agent_name} on task: {task}")
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output = agent_name.run(task)
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self.conversation.add(role=agent_name, content=output)
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return output
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def parse_json_for_agents_then_create_agents(
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self, function_call: dict
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) -> List[Agent]:
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"""
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Parses a JSON function call to create a list of agents.
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Args:
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function_call (dict): The JSON function call specifying the agents.
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Returns:
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List[Agent]: A list of created agent objects.
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"""
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responses = []
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logger.info("Parsing JSON for agents")
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if self.create_agents_on:
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for agent in function_call["multiple_agents"]:
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out = self.create_agent(
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agent_name=agent["agent_name"],
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system_prompt=agent["system_prompt"],
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agent_description=agent["agent_description"],
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task=agent["task"],
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)
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responses.append(out)
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else:
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for agent in function_call["orders"]:
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out = self.run_worker_agent(
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name=agent["agent_name"],
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task=agent["task"],
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)
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responses.append(out)
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return concat_strings(responses)
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def run(self, task: str) -> str:
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"""
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Runs the function caller to create and execute agents based on the provided task.
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Args:
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task (str): The task for which the agents need to be created and executed.
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Returns:
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List[Agent]: A list of created agent objects.
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"""
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logger.info("Running the swarm")
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# Run the function caller to output JSON function call
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function_call = self.model.run(task)
|
||||
|
||||
# Add the function call to the conversation
|
||||
self.conversation.add(role="Director", content=str(function_call))
|
||||
|
||||
# Logging the function call with metrics and details
|
||||
self.log_director_function_call(function_call)
|
||||
|
||||
# # Parse the JSON function call and create agents -> run Agents
|
||||
return self.parse_json_for_agents_then_create_agents(function_call)
|
||||
|
||||
def run_new(self, task: str):
|
||||
"""
|
||||
Runs the function caller to create and execute agents based on the provided task.
|
||||
|
||||
Args:
|
||||
task (str): The task for which the agents need to be created and executed.
|
||||
|
||||
Returns:
|
||||
List[Agent]: A list of created agent objects.
|
||||
"""
|
||||
logger.info("Running the swarm")
|
||||
|
||||
# Run the function caller to output JSON function call
|
||||
function_call = self.model.run(task)
|
||||
self.conversation.add(role="Director", content=str(function_call))
|
||||
|
||||
# Logging the function call with metrics and details
|
||||
self.log_director_function_call(function_call)
|
||||
|
||||
if self.create_agents_on:
|
||||
# Create agents from the function call
|
||||
self.create_agents_from_func_call(function_call)
|
||||
|
||||
# Now submit orders to the agents
|
||||
self.director.base_model = CallTeam
|
||||
|
||||
orders_prompt = f"Now, the agents have been created. Submit orders to the agents to enable them to complete the task: {task}: {self.list_agents_available()}"
|
||||
orders = self.director.run(orders_prompt)
|
||||
self.conversation.add(
|
||||
role="Director", content=str(orders_prompt + orders)
|
||||
)
|
||||
|
||||
# Check the type of the response
|
||||
orders = self.check_agent_output_type(orders)
|
||||
|
||||
# Distribute the orders to the agents
|
||||
return self.distribute_orders_to_agents(orders)
|
||||
|
||||
def check_agent_output_type(self, response: Any):
|
||||
if isinstance(response, dict):
|
||||
return response
|
||||
if isinstance(response, str):
|
||||
return eval(response)
|
||||
else:
|
||||
return response
|
||||
|
||||
def distribute_orders_to_agents(self, order_dict: dict) -> str:
|
||||
# Now we need to parse the CallTeam object
|
||||
# and distribute the orders to the agents
|
||||
responses = []
|
||||
|
||||
for order in order_dict["orders"]:
|
||||
agent_name = order["agent_name"]
|
||||
task = order["task"]
|
||||
|
||||
# Find and run the agent
|
||||
response = self.run_worker_agent(name=agent_name, task=task)
|
||||
|
||||
log = f"Agent: {agent_name} completed task: {task} with response: {response}"
|
||||
self.conversation.add(role=agent_name, content=task + response)
|
||||
responses.append(log)
|
||||
logger.info(log)
|
||||
|
||||
return concat_strings(responses)
|
||||
|
||||
def create_single_agent(
|
||||
self, name: str, system_prompt: str, description
|
||||
) -> Agent:
|
||||
"""
|
||||
Create a single agent from the agent specification.
|
||||
|
||||
Args:
|
||||
agent_spec (dict): The agent specification.
|
||||
|
||||
Returns:
|
||||
Agent: The created agent.
|
||||
|
||||
"""
|
||||
# Unwrap all of the agent specifications
|
||||
# agent_name = agent_spec["agent_name"]
|
||||
# system_prompt = agent_spec["system_prompt"]
|
||||
# agent_description = agent_spec["agent_description"]
|
||||
|
||||
# Create the agent
|
||||
agent_name = Agent(
|
||||
agent_name=name,
|
||||
llm=self.template_base_worker_llm, # Switch to model router here later
|
||||
system_prompt=system_prompt,
|
||||
agent_description=description,
|
||||
max_loops=1,
|
||||
retry_attempts=1,
|
||||
verbose=False,
|
||||
dashboard=False,
|
||||
)
|
||||
|
||||
# Add agents into the registry
|
||||
self.agents.append(agent_name)
|
||||
|
||||
return agent_name
|
||||
|
||||
def create_agents_from_func_call(self, function_call: dict):
|
||||
"""
|
||||
Create agents from the function call.
|
||||
|
||||
Args:
|
||||
function_call (dict): The function call containing the agent specifications.
|
||||
|
||||
Returns:
|
||||
List[Agent]: A list of created agents.
|
||||
|
||||
"""
|
||||
logger.info("Creating agents from the function call")
|
||||
for agent_spec in function_call["multiple_agents"]:
|
||||
agent = self.create_single_agent(
|
||||
name=agent_spec["agent_name"],
|
||||
system_prompt=agent_spec["system_prompt"],
|
||||
description=agent_spec["agent_description"],
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Created agent: {agent.agent_name} with description: {agent.description}"
|
||||
)
|
||||
|
||||
self.agents.append(agent)
|
||||
|
||||
def plan(self, task: str) -> str:
|
||||
"""
|
||||
Plans the tasks for the agents in the swarm.
|
||||
|
||||
Args:
|
||||
task (str): The task to be planned.
|
||||
|
||||
Returns:
|
||||
str: The planned task for the agents.
|
||||
|
||||
"""
|
||||
logger.info("Director is planning the task")
|
||||
|
||||
self.director.system_prompt = self.director_planning_prompt
|
||||
|
||||
def log_director_function_call(self, function_call: dict):
|
||||
# Log the agents the boss makes\
|
||||
logger.info(f"Swarm Name: {function_call['swarm_name']}")
|
||||
# Log the plan
|
||||
logger.info(f"Plan: {function_call['plan']}")
|
||||
logger.info(
|
||||
f"Number of agents: {len(function_call['multiple_agents'])}"
|
||||
)
|
||||
|
||||
for agent in function_call["multiple_agents"]:
|
||||
logger.info(f"Agent: {agent['agent_name']}")
|
||||
# logger.info(f"Task: {agent['task']}")
|
||||
logger.info(f"Description: {agent['agent_description']}")
|
||||
|
||||
def run_worker_agent(
|
||||
self, name: str = None, task: str = None, *args, **kwargs
|
||||
):
|
||||
"""
|
||||
Run the worker agent.
|
||||
|
||||
Args:
|
||||
name (str): The name of the worker agent.
|
||||
task (str): The task to send to the worker agent.
|
||||
|
||||
Returns:
|
||||
str: The response from the worker agent.
|
||||
|
||||
Raises:
|
||||
Exception: If an error occurs while running the worker agent.
|
||||
|
||||
"""
|
||||
try:
|
||||
# Find the agent by name
|
||||
agent = self.find_agent_by_name(name)
|
||||
|
||||
# Run the agent
|
||||
response = agent.run(task, *args, **kwargs)
|
||||
|
||||
return response
|
||||
except Exception as e:
|
||||
logger.error(f"Error: {e}")
|
||||
raise e
|
||||
|
||||
def list_agents(self) -> str:
|
||||
logger.info("Listing agents available in the swarm")
|
||||
|
||||
for agent in self.agents:
|
||||
name = agent.agent_name
|
||||
description = agent.description or "No description available."
|
||||
logger.info(f"Agent: {name}, Description: {description}")
|
||||
|
||||
def list_agents_available(self):
|
||||
number_of_agents_available = len(self.agents)
|
||||
|
||||
agent_list = "\n".join(
|
||||
[
|
||||
f"Agent {agent.agent_name}: Description {agent.description}"
|
||||
for agent in self.agents
|
||||
]
|
||||
)
|
||||
|
||||
prompt = f"""
|
||||
There are currently {number_of_agents_available} agents available in the swarm.
|
||||
|
||||
Agents Available:
|
||||
{agent_list}
|
||||
"""
|
||||
|
||||
return prompt
|
||||
|
||||
def find_agent_by_name(self, agent_name: str = None, *args, **kwargs):
|
||||
"""
|
||||
Finds an agent in the swarm by name.
|
||||
|
||||
Args:
|
||||
agent_name (str): The name of the agent to find.
|
||||
|
||||
Returns:
|
||||
Agent: The agent with the specified name, or None if not found.
|
||||
|
||||
"""
|
||||
for agent in self.agents:
|
||||
if agent.name == agent_name:
|
||||
return agent
|
||||
return None
|
||||
@ -1,293 +0,0 @@
|
||||
import os
|
||||
from typing import List, Any
|
||||
|
||||
from loguru import logger
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from swarms import Agent, OpenAIChat
|
||||
from swarms.models.openai_function_caller import OpenAIFunctionCaller
|
||||
from swarms.structs.concat import concat_strings
|
||||
|
||||
api_key = os.getenv("OPENAI_API_KEY")
|
||||
|
||||
# Create an instance of the OpenAIChat class
|
||||
model = OpenAIChat(
|
||||
api_key=api_key, model_name="gpt-4o-mini", temperature=0.1
|
||||
)
|
||||
|
||||
|
||||
class AgentSpec(BaseModel):
|
||||
"""
|
||||
A class representing the specifications of an agent.
|
||||
|
||||
Attributes:
|
||||
agent_name (str): The name of the agent.
|
||||
system_prompt (str): The system prompt for the agent.
|
||||
agent_description (str): The description of the agent.
|
||||
max_tokens (int): The maximum number of tokens to generate in the API response.
|
||||
temperature (float): A parameter that controls the randomness of the generated text.
|
||||
context_window (int): The context window for the agent.
|
||||
task (str): The main task for the agent.
|
||||
"""
|
||||
|
||||
agent_name: str
|
||||
system_prompt: str
|
||||
agent_description: str
|
||||
task: str
|
||||
|
||||
|
||||
class AgentTeam(BaseModel):
|
||||
agents: List[AgentSpec] = Field(
|
||||
...,
|
||||
description="The list of agents in the team",
|
||||
)
|
||||
flow: str = Field(
|
||||
...,
|
||||
description="Agent Name -> ",
|
||||
)
|
||||
|
||||
|
||||
class SwarmSpec(BaseModel):
|
||||
"""
|
||||
A class representing the specifications of a swarm of agents.
|
||||
|
||||
Attributes:
|
||||
multiple_agents (List[AgentSpec]): The list of agents in the swarm.
|
||||
"""
|
||||
|
||||
swarm_name: str = Field(
|
||||
...,
|
||||
description="The name of the swarm: e.g., 'Marketing Swarm' or 'Finance Swarm'",
|
||||
)
|
||||
multiple_agents: List[AgentSpec]
|
||||
rules: str = Field(
|
||||
...,
|
||||
description="The rules for all the agents in the swarm: e.g., All agents must return code. Be very simple and direct",
|
||||
)
|
||||
plan: str = Field(
|
||||
...,
|
||||
description="The plan for the swarm: e.g., 'Create a marketing campaign for the new product launch.'",
|
||||
)
|
||||
|
||||
|
||||
class HierarchicalAgentSwarm:
|
||||
"""
|
||||
A class to create and manage a hierarchical swarm of agents.
|
||||
|
||||
Methods:
|
||||
__init__(system_prompt, max_tokens, temperature, base_model, parallel_tool_calls): Initializes the function caller.
|
||||
create_agent(agent_name, system_prompt, agent_description, max_tokens, temperature, context_window): Creates an individual agent.
|
||||
parse_json_for_agents_then_create_agents(function_call): Parses a JSON function call to create multiple agents.
|
||||
run(task): Runs the function caller to create and execute agents based on the provided task.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
director: Any = None,
|
||||
agents: List[Agent] = None,
|
||||
max_loops: int = 1,
|
||||
create_agents_on: bool = False,
|
||||
):
|
||||
"""
|
||||
Initializes the HierarchicalAgentSwarm with an OpenAIFunctionCaller.
|
||||
|
||||
Args:
|
||||
system_prompt (str): The system prompt for the function caller.
|
||||
max_tokens (int): The maximum number of tokens to generate in the API response.
|
||||
temperature (float): The temperature setting for text generation.
|
||||
base_model (BaseModel): The base model for the function caller.
|
||||
parallel_tool_calls (bool): Whether to run tool calls in parallel.
|
||||
"""
|
||||
self.director = director
|
||||
self.agents = agents
|
||||
self.max_loops = max_loops
|
||||
self.create_agents_on = create_agents_on
|
||||
|
||||
# Check if the agents are set
|
||||
self.agents_check()
|
||||
|
||||
def agents_check(self):
|
||||
if self.director is None:
|
||||
raise ValueError("The director is not set.")
|
||||
|
||||
# if self.agents is None:
|
||||
# raise ValueError("The agents are not set.")
|
||||
|
||||
if self.max_loops == 0:
|
||||
raise ValueError("The max_loops is not set.")
|
||||
|
||||
def create_agent(
|
||||
self,
|
||||
agent_name: str,
|
||||
system_prompt: str,
|
||||
agent_description: str,
|
||||
task: str = None,
|
||||
) -> str:
|
||||
"""
|
||||
Creates an individual agent.
|
||||
|
||||
Args:
|
||||
agent_name (str): The name of the agent.
|
||||
system_prompt (str): The system prompt for the agent.
|
||||
agent_description (str): The description of the agent.
|
||||
max_tokens (int): The maximum number of tokens to generate.
|
||||
temperature (float): The temperature for text generation.
|
||||
context_window (int): The context window size for the agent.
|
||||
|
||||
Returns:
|
||||
Agent: An instantiated agent object.
|
||||
"""
|
||||
# name = agent_name.replace(" ", "_")
|
||||
logger.info(f"Creating agent: {agent_name}")
|
||||
agent_name = Agent(
|
||||
agent_name=agent_name,
|
||||
llm=model,
|
||||
system_prompt=system_prompt,
|
||||
agent_description=agent_description,
|
||||
retry_attempts=1,
|
||||
verbose=False,
|
||||
dashboard=False,
|
||||
)
|
||||
self.agents.append(agent_name)
|
||||
|
||||
logger.info(f"Running agent: {agent_name}")
|
||||
output = agent_name.run(task)
|
||||
|
||||
# create_file_in_folder(
|
||||
# agent_name.workspace_dir, f"{agent_name}_output.txt", str(output)
|
||||
# )
|
||||
|
||||
return output
|
||||
|
||||
def parse_json_for_agents_then_create_agents(
|
||||
self, function_call: dict
|
||||
) -> List[Agent]:
|
||||
"""
|
||||
Parses a JSON function call to create a list of agents.
|
||||
|
||||
Args:
|
||||
function_call (dict): The JSON function call specifying the agents.
|
||||
|
||||
Returns:
|
||||
List[Agent]: A list of created agent objects.
|
||||
"""
|
||||
responses = []
|
||||
logger.info("Parsing JSON for agents")
|
||||
for agent in function_call["multiple_agents"]:
|
||||
out = self.create_agent(
|
||||
agent_name=agent["agent_name"],
|
||||
system_prompt=agent["system_prompt"],
|
||||
agent_description=agent["agent_description"],
|
||||
task=agent["task"],
|
||||
)
|
||||
responses.append(out)
|
||||
return concat_strings(responses)
|
||||
|
||||
def run(self, task: str) -> List[Agent]:
|
||||
"""
|
||||
Runs the function caller to create and execute agents based on the provided task.
|
||||
|
||||
Args:
|
||||
task (str): The task for which the agents need to be created and executed.
|
||||
|
||||
Returns:
|
||||
List[Agent]: A list of created agent objects.
|
||||
"""
|
||||
logger.info("Running the swarm")
|
||||
|
||||
# Run the function caller
|
||||
function_call = self.model.run(task)
|
||||
|
||||
# Logging the function call
|
||||
self.log_director_function_call(function_call)
|
||||
|
||||
# Parse the JSON function call and create agents -> run Agents
|
||||
return self.parse_json_for_agents_then_create_agents(function_call)
|
||||
|
||||
def log_director_function_call(self, function_call: dict):
|
||||
# Log the agents the boss makes\
|
||||
logger.info(f"Swarm Name: {function_call['swarm_name']}")
|
||||
# Log the plan
|
||||
logger.info(f"Plan: {function_call['plan']}")
|
||||
logger.info(
|
||||
f"Number of agents: {len(function_call['multiple_agents'])}"
|
||||
)
|
||||
|
||||
for agent in function_call["multiple_agents"]:
|
||||
logger.info(f"Agent: {agent['agent_name']}")
|
||||
# logger.info(f"Task: {agent['task']}")
|
||||
logger.info(f"Description: {agent['agent_description']}")
|
||||
|
||||
|
||||
# Example usage:
|
||||
HIEARCHICAL_AGENT_SYSTEM_PROMPT = """
|
||||
Here's a full-fledged system prompt for a director boss agent, complete with instructions and many-shot examples:
|
||||
|
||||
---
|
||||
|
||||
**System Prompt: Director Boss Agent**
|
||||
|
||||
### Role:
|
||||
You are a Director Boss Agent responsible for orchestrating a swarm of worker agents. Your primary duty is to serve the user efficiently, effectively, and skillfully. You dynamically create new agents when necessary or utilize existing agents, assigning them tasks that align with their capabilities. You must ensure that each agent receives clear, direct, and actionable instructions tailored to their role.
|
||||
|
||||
### Key Responsibilities:
|
||||
1. **Task Delegation:** Assign tasks to the most relevant agent. If no relevant agent exists, create a new one with an appropriate name and system prompt.
|
||||
2. **Efficiency:** Ensure that tasks are completed swiftly and with minimal resource expenditure.
|
||||
3. **Clarity:** Provide orders that are simple, direct, and actionable. Avoid ambiguity.
|
||||
4. **Dynamic Decision Making:** Assess the situation and choose the most effective path, whether that involves using an existing agent or creating a new one.
|
||||
5. **Monitoring:** Continuously monitor the progress of each agent and provide additional instructions or corrections as necessary.
|
||||
|
||||
### Instructions:
|
||||
- **Identify the Task:** Analyze the input task to determine its nature and requirements.
|
||||
- **Agent Selection/Creation:**
|
||||
- If an agent is available and suited for the task, assign the task to that agent.
|
||||
- If no suitable agent exists, create a new agent with a relevant system prompt.
|
||||
- **Task Assignment:** Provide the selected agent with explicit and straightforward instructions.
|
||||
- **Reasoning:** Justify your decisions when selecting or creating agents, focusing on the efficiency and effectiveness of task completion.
|
||||
|
||||
"""
|
||||
|
||||
|
||||
director = (
|
||||
OpenAIFunctionCaller(
|
||||
system_prompt=HIEARCHICAL_AGENT_SYSTEM_PROMPT,
|
||||
max_tokens=3000,
|
||||
temperature=0.4,
|
||||
base_model=SwarmSpec,
|
||||
parallel_tool_calls=False,
|
||||
),
|
||||
)
|
||||
|
||||
# Initialize the hierarchical agent swarm with the necessary parameters
|
||||
swarm = HierarchicalAgentSwarm(
|
||||
director=director,
|
||||
max_loops=1,
|
||||
)
|
||||
|
||||
# # Run the swarm with a task
|
||||
# agents = swarm.run(
|
||||
# """
|
||||
# Create a swarm of agents for a marketing campaign to promote
|
||||
# the swarms workshop: [Workshop][Automating Business Operations with Hierarchical Agent Swarms][Swarms Framework + GPT4o],
|
||||
# create agents for twitter, linkedin, and emails, facebook, instagram.
|
||||
|
||||
# The date is Saturday, August 17 4:00 PM - 5:00 PM
|
||||
|
||||
# Link is: https://lu.ma/ew4r4s3i
|
||||
|
||||
|
||||
# """
|
||||
# )
|
||||
|
||||
|
||||
# Run the swarm with a task
|
||||
agents = swarm.run(
|
||||
"""
|
||||
Create a swarms of agents that generate the code in python
|
||||
to send an API request to social media platforms through their apis.
|
||||
Craft a single function to send a message to all platforms, add types and write
|
||||
clean code. Each agent needs to generate code for a specific platform, they
|
||||
must return the python code only.
|
||||
|
||||
"""
|
||||
)
|
||||
@ -1,241 +0,0 @@
|
||||
from typing import List
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from swarms.structs.agent import Agent
|
||||
from swarms.structs.concat import concat_strings
|
||||
from loguru import logger
|
||||
from swarms.structs.base_swarm import BaseSwarm
|
||||
from swarms.structs.conversation import Conversation
|
||||
|
||||
|
||||
class HierarchicalOrderCall(BaseModel):
|
||||
agent_name: str
|
||||
task: str
|
||||
|
||||
|
||||
class CallTeam(BaseModel):
|
||||
calls: List[HierarchicalOrderCall]
|
||||
|
||||
|
||||
class HiearchicalSwarm(BaseSwarm):
|
||||
def __init__(
|
||||
self,
|
||||
agents: List[Agent],
|
||||
director: Agent,
|
||||
name: str = "HierarchicalSwarm",
|
||||
description: str = "A swarm of agents that can be used to distribute tasks to a team of agents.",
|
||||
max_loops: int = 3,
|
||||
verbose: bool = True,
|
||||
create_agents_from_scratch: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
self.agents = agents
|
||||
self.director = director
|
||||
self.max_loops = max_loops
|
||||
self.verbose = verbose
|
||||
self.name = name
|
||||
self.description = description
|
||||
self.create_agents_from_scratch = create_agents_from_scratch
|
||||
|
||||
self.agents_check()
|
||||
self.director_check()
|
||||
|
||||
# Initialize the conversation
|
||||
self.conversation = Conversation(
|
||||
time_enabled=True,
|
||||
)
|
||||
|
||||
logger.info(f"Initialized {self.name} Hiearchical swarm")
|
||||
|
||||
def agents_check(self):
|
||||
if len(self.agents) == 0:
|
||||
raise ValueError(
|
||||
"No agents found. Please add agents to the swarm."
|
||||
)
|
||||
return None
|
||||
|
||||
def director_check(self):
|
||||
if self.director is None:
|
||||
raise ValueError(
|
||||
"No director found. Please add a director to the swarm."
|
||||
)
|
||||
return None
|
||||
|
||||
def run(self, task: str):
|
||||
# Plan
|
||||
# Plan -> JSON Function call -> workers -> response fetch back to boss -> planner
|
||||
responses = []
|
||||
responses.append(task)
|
||||
|
||||
for _ in range(self.max_loops):
|
||||
# Plan
|
||||
plan = self.planner.run(concat_strings(responses))
|
||||
logger.info(f"Agent {self.planner.agent_name} planned: {plan}")
|
||||
responses.append(plan)
|
||||
|
||||
# Execute json function calls
|
||||
calls = self.director.run(plan)
|
||||
logger.info(
|
||||
f"Agent {self.director.agent_name} called: {calls}"
|
||||
)
|
||||
responses.append(calls)
|
||||
# Parse and send tasks to agents
|
||||
output = self.parse_then_send_tasks_to_agents(
|
||||
self.agents, calls
|
||||
)
|
||||
|
||||
# Fetch back to boss
|
||||
responses.append(output)
|
||||
|
||||
return concat_strings(responses)
|
||||
|
||||
def run_worker_agent(
|
||||
self, name: str = None, task: str = None, *args, **kwargs
|
||||
):
|
||||
"""
|
||||
Run the worker agent.
|
||||
|
||||
Args:
|
||||
name (str): The name of the worker agent.
|
||||
task (str): The task to send to the worker agent.
|
||||
|
||||
Returns:
|
||||
str: The response from the worker agent.
|
||||
|
||||
Raises:
|
||||
Exception: If an error occurs while running the worker agent.
|
||||
|
||||
"""
|
||||
try:
|
||||
# Find the agent by name
|
||||
agent = self.find_agent_by_name(name)
|
||||
|
||||
# Run the agent
|
||||
response = agent.run(task, *args, **kwargs)
|
||||
|
||||
return response
|
||||
except Exception as e:
|
||||
logger.error(f"Error: {e}")
|
||||
raise e
|
||||
|
||||
def find_agent_by_name(self, agent_name: str = None, *args, **kwargs):
|
||||
"""
|
||||
Finds an agent in the swarm by name.
|
||||
|
||||
Args:
|
||||
agent_name (str): The name of the agent to find.
|
||||
|
||||
Returns:
|
||||
Agent: The agent with the specified name, or None if not found.
|
||||
|
||||
"""
|
||||
for agent in self.agents:
|
||||
if agent.name == agent_name:
|
||||
return agent
|
||||
return None
|
||||
|
||||
def select_agent_and_send_task(
|
||||
self, name: str = None, task: str = None, *args, **kwargs
|
||||
):
|
||||
"""
|
||||
Select an agent from the list and send a task to them.
|
||||
|
||||
Args:
|
||||
name (str): The name of the agent to send the task to.
|
||||
task (str): The task to send to the agent.
|
||||
|
||||
Returns:
|
||||
str: The response from the agent.
|
||||
|
||||
Raises:
|
||||
KeyError: If the agent name is not found in the list of agents.
|
||||
|
||||
"""
|
||||
try:
|
||||
# Check to see if the agent name is in the list of agents
|
||||
if name in self.agents:
|
||||
agent = self.agents[name]
|
||||
else:
|
||||
return "Invalid agent name. Please select 'Account Management Agent' or 'Product Support Agent'."
|
||||
|
||||
response = agent.run(task, *args, **kwargs)
|
||||
|
||||
return response
|
||||
except Exception as e:
|
||||
logger.error(f"Error: {e}")
|
||||
raise e
|
||||
|
||||
def agents_list(
|
||||
self,
|
||||
) -> str:
|
||||
logger.info("Listing agents")
|
||||
|
||||
for agent in self.agents:
|
||||
name = agent.agent_name
|
||||
description = agent.description or "No description available."
|
||||
logger.info(f"Agent: {name}, Description: {description}")
|
||||
self.conversation.add(name, description)
|
||||
|
||||
return self.conversation.return_history_as_string()
|
||||
|
||||
def parse_then_send_tasks_to_agents(self, response: dict):
|
||||
# Initialize an empty dictionary to store the output of each agent
|
||||
output = []
|
||||
|
||||
# Loop over the tasks in the response
|
||||
for call in response["calls"]:
|
||||
name = call["agent_name"]
|
||||
task = call["task"]
|
||||
|
||||
# Loop over the agents
|
||||
for agent in self.agents:
|
||||
# If the agent's name matches the name in the task, run the task
|
||||
if agent.agent_name == name:
|
||||
out = agent.run(task)
|
||||
print(out)
|
||||
|
||||
output.append(f"{name}: {out}")
|
||||
|
||||
# Store the output in the dictionary
|
||||
# output[name] = out
|
||||
break
|
||||
|
||||
return output
|
||||
|
||||
|
||||
# # Example usage:
|
||||
# system_prompt = f"""
|
||||
# You're a director agent, your responsibility is to serve the user efficiently, effectively and skillfully.You have a swarm of agents available to distribute tasks to, interact with the user and then submit tasks to the worker agents. Provide orders to the worker agents that are direct, explicit, and simple. Ensure that they are given tasks that are understandable, actionable, and simple to execute.
|
||||
|
||||
|
||||
# ######
|
||||
# Workers available:
|
||||
|
||||
# {agents_list(team)}
|
||||
|
||||
|
||||
# """
|
||||
|
||||
|
||||
def has_sop(self):
|
||||
# We need to check the name of the agents and their description or system prompt
|
||||
# TODO: Provide many shot examples of the agents available and even maybe what tools they have access to
|
||||
# TODO: Provide better reasoning prompt tiles, such as when do you use a certain agent and specific
|
||||
# Things NOT to do.
|
||||
return f"""
|
||||
|
||||
You're a director boss agent orchestrating worker agents with tasks. Select an agent most relevant to
|
||||
the input task and give them a task. If there is not an agent relevant to the input task then say so and be simple and direct.
|
||||
These are the available agents available call them if you need them for a specific
|
||||
task or operation:
|
||||
|
||||
Number of agents: {len(self.agents)}
|
||||
Agents Available: {
|
||||
[
|
||||
{"name": agent.name, "description": agent.system_prompt}
|
||||
for agent in self.agents
|
||||
]
|
||||
}
|
||||
|
||||
"""
|
||||
Loading…
Reference in new issue