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](#1-high-level-architecture)
2. [Key Components](#2-key-components)
3. [Detailed Implementation](#3-detailed-implementation)
    - [3.1 Master Planner Agent](#31-master-planner-agent)
    - [3.2 Prompt Creator Agent](#32-prompt-creator-agent)
    - [3.3 Agent Creation and Management](#33-agent-creation-and-management)
    - [3.4 Inter-Agent Communication](#34-inter-agent-communication)
    - [3.5 Retrieval-Augmented Generation (RAG) for Agent Metadata](#35-retrieval-augmented-generation-rag-for-agent-metadata)
    - [3.6 User Interaction and Approval Workflow](#36-user-interaction-and-approval-workflow)
4. [Integrating with Existing Code](#4-integrating-with-existing-code)
5. [Example Code Snippets](#5-example-code-snippets)
    - [5.1 Adding Master Planner Methods](#51-adding-master-planner-methods)
    - [5.2 Implementing Prompt Creator](#52-implementing-prompt-creator)
    - [5.3 Agent Management Utilities](#53-agent-management-utilities)
    - [5.4 RAG Integration](#54-rag-integration)
    - [5.5 User Approval Flow](#55-user-approval-flow)
6. [Workflow Overview](#6-workflow-overview)
7. [Considerations and Best Practices](#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`

```python: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`

```python: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`

```python: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`

```python: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`

```python: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.
