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To achieve the sophisticated Master Planner system you've envisioned, we'll need to extend and integrate the existing codebase you provided. Here's a comprehensive plan outlining the necessary components, modifications, and new implementations required to realize this functionality.

Table of Contents

  1. High-Level Architecture
  2. Key Components
  3. Detailed Implementation
  4. Integrating with Existing Code
  5. Example Code Snippets
  6. Workflow Overview
  7. Considerations and Best Practices

1. High-Level Architecture

The proposed system consists of several interacting agents, each responsible for different aspects of task management and execution:

  • Master Planner Agent: Orchestrates the overall task by breaking it down into steps, selecting appropriate tools, and managing sub-agents.
  • Prompt Creator Agent: Generates optimized prompts for each step, incorporating self-reflection and advanced techniques.
  • Step Agents: Execute individual steps of the task using assigned tools and prompts.
  • RAG (Retrieval-Augmented Generation) System: Stores and retrieves agent descriptions and metadata for reuse.
  • User Interface: Facilitates user interactions, approvals, and customizations.

2. Key Components

  1. Agent Management:

    • Creation, configuration, and storage of agent instances.
    • Reusability of agents through RAG.

  2. Tool Selection:

    • Based on task requirements, selecting appropriate tools from the available list.

  3. Prompt Generation:

    • Crafting effective prompts for each task step, utilizing self-reflection.

  4. Inter-Agent Communication:

    • Mechanism for agents to send messages to each other.

  5. User Interaction:

    • Approval processes for agent plans.
    • Customization of prompts and tool selections by the user.

3. Detailed Implementation

3.1 Master Planner Agent

Responsibilities:

  • Parse the high-level task.
  • Break down the task into actionable steps.
  • Select tools for each step.
  • Create or reuse step agents with appropriate prompts and tools.
  • Initiate and manage the execution flow.

3.2 Prompt Creator Agent

Responsibilities:

  • Generate effective prompts for each task step.
  • Incorporate self-reflection and other advanced techniques to optimize prompt quality.

3.3 Agent Creation and Management

Responsibilities:

  • Dynamically create agents with specified prompts and tools.
  • Store agent metadata in the RAG system for future reuse.
  • Manage the lifecycle of agents, including activation and deactivation.

3.4 Inter-Agent Communication

Responsibilities:

  • Facilitate messaging between agents.
  • Enable agents to send tasks, updates, or requests to other agents in the plan.

3.5 Retrieval-Augmented Generation (RAG) for Agent Metadata

Responsibilities:

  • Maintain a database of existing agents with their descriptions and metadata.
  • Retrieve relevant agents based on task similarity to promote reuse.

3.6 User Interaction and Approval Workflow

Responsibilities:

  • Present generated plans and agent configurations to the user for approval.
  • Allow users to customize prompts and tool selections before execution.
  • Finalize agent cohorts for autonomous task handling based on user satisfaction.

4. Integrating with Existing Code

Your existing codebase provides a solid foundation for tool management, dialog handling, and utility functions. We'll build upon these to implement the Master Planner system.

  • tools.py: Define new tools required for agent management and inter-agent communication.
  • leggo.py: Extend functionalities to include agent orchestration.
  • dialog.py: Utilize existing dialog structures for inter-agent and user communications.
  • vk.py: Manage tool queues and current tool states, which can be adapted for agent management.
  • func_utils.py: Leverage utility functions for dynamic function handling and type management.

5. Example Code Snippets

Below are example implementations to guide the development of the Master Planner system.

5.1 Adding Master Planner Methods

File Path: src/ayy/leggo.py

# Within leggo.py

def create_master_planner(valkey_client: Valkey, creator: Instructor | AsyncInstructor) -> Dialog:
    """
    Initializes the Master Planner agent.
    """
    master_planner_tool = Tool(
        chain_of_thought="Master Planner responsible for orchestrating tasks.",
        name="master_planner",
        prompt="You are the Master Planner. Break down the task into steps and assign tools.",
    )
    get_selected_tools(valkey_client, [master_planner_tool])
    dialog = Dialog(system="Master Planner initialized.")
    return run_selected_tool(valkey_client, creator, dialog, master_planner_tool)

5.2 Implementing Prompt Creator

File Path: src/ayy/tools.py

# Add Prompt Creator Tool

def create_prompt(step_description: str) -> str:
    """
    Generates a refined prompt for a given step.
    Incorporates self-reflection techniques.
    """
    prompt_creator_tool = Tool(
        chain_of_thought="Prompt Creator for generating optimized prompts.",
        name="prompt_creator",
        prompt=f"Generate an effective prompt for the following step: {step_description}",
    )
    return prompt_creator_tool

5.3 Agent Management Utilities

File Path: src/ayy/vk.py

# Add functions to manage agent metadata in RAG

def store_agent_metadata(valkey_client: Valkey, agent_metadata: dict) -> None:
    """
    Stores agent metadata in the RAG system.
    """
    rag_key = "agent_rag"
    existing_rag = valkey_client.get(rag_key)
    rag = dill.loads(existing_rag) if existing_rag else {}
    rag[agent_metadata["id"]] = agent_metadata
    valkey_client.set(rag_key, dill.dumps(rag))


def retrieve_matching_agents(valkey_client: Valkey, task_description: str) -> list[dict]:
    """
    Retrieves agents from RAG that match the task description.
    """
    rag_key = "agent_rag"
    existing_rag = valkey_client.get(rag_key)
    rag = dill.loads(existing_rag) if existing_rag else {}
    # Simple matching based on keyword overlap; can be enhanced with embeddings
    matches = [
        metadata for metadata in rag.values()
        if any(keyword.lower() in task_description.lower() for keyword in metadata.get("keywords", []))
    ]
    return matches

5.4 RAG Integration

File Path: src/ayy/dialog.py

# Extend Dialog model if necessary to include agent metadata

class Dialog(BaseModel):
    system: Content = ""
    messages: Messages = Field(default_factory=list)
    model_name: str = MODEL_NAME
    creation_config: dict = dict(temperature=TEMPERATURE, max_tokens=MAX_TOKENS)
    memory_tags: list[Literal["core", "recall"]] = Field(default_factory=list)
    agent_id: str | None = None  # Optional field to link to agent metadata

5.5 User Approval Flow

File Path: src/ayy/leggo.py

# Add method for user approval

def get_user_approval(valkey_client: Valkey, creator: Instructor | AsyncInstructor, dialog: Dialog, proposed_agents: list[dict]) -> bool:
    """
    Presents proposed agents to the user for approval.
    Allows customization of prompts and tools.
    """
    approval_prompt = "Please review the proposed agents for this task:\n"
    for agent in proposed_agents:
        approval_prompt += f"Agent ID: {agent['id']}\nDescription: {agent['description']}\nPrompt: {agent['prompt']}\nTools: {agent['tools']}\n\n"
    approval_prompt += "Do you approve these agents? (yes/no)"
    
    approval_tool = Tool(
        chain_of_thought="User approval for agent setup.",
        name="ask_user",
        prompt=approval_prompt,
    )
    dialog = run_selected_tool(valkey_client, creator, dialog, approval_tool)
    
    # Check last user message for approval
    if dialog.messages and dialog.messages[-1]["role"] == "user":
        response = dialog.messages[-1]["content"].strip().lower()
        return response in ["yes", "y"]
    return False

6. Workflow Overview

  1. Task Initiation:

    • User provides a high-level task.
    • Master Planner Agent receives the task and begins orchestration.

  2. Task Breakdown:

    • Master Planner decomposes the task into discrete steps.
    • For each step, it interacts with the Prompt Creator to generate optimized prompts.

  3. Agent Assignment:

    • For each step, Master Planner selects or creates a Step Agent.
    • Checks RAG to reuse existing agents if applicable.
    • If reusing, retrieves agent configurations; otherwise, creates new agents and stores their metadata in RAG.

  4. User Approval:

    • Before executing the plan, presents the proposed agents and their configurations to the user.
    • User reviews and approves or customizes the agents.

  5. Execution:

    • Once approved, the Master Planner initiates the Step Agents.
    • Agents execute their assigned steps using the selected tools and prompts.
    • Agents can communicate with each other as needed via inter-agent communication tools.

  6. Autonomy and Learning:

    • After successful execution and user satisfaction, the agent cohort is stored in RAG.
    • Future similar tasks can leverage this cohort for faster and more efficient execution without recreating agents.

7. Considerations and Best Practices

  • Scalability: Ensure that agent creation and management are efficient to handle multiple concurrent tasks.
  • Security: Validate and sanitize all inputs, especially those involving user interactions and external tool integrations.
  • Extensibility: Design the system to easily incorporate new tools and agent types as needed.
  • User Experience: Streamline the approval and customization processes to be intuitive and non-intrusive.
  • Error Handling: Implement robust error handling to manage failures in agent creation, tool execution, or inter-agent communication.
  • Logging and Monitoring: Maintain detailed logs to monitor agent activities and diagnose issues.

By following this plan and integrating the provided code snippets, you can develop a robust Master Planner system that orchestrates complex tasks, leverages reusable agents, and incorporates user feedback for continuous improvement.