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LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Autonomous "Raph Wiggum" Implementation Loop for VS Code Copilot

I tried a prompt in VC Code Copilot that triggers a "Ralph Wiggum" loop to implement a (hopefully) well crafted, 26 tasks PRD. This is ideal to trigger the implementation after an interactive Plan session with your favorite model.

This is a Proof of Concept on how to use an orchestrator agent that will trigger subagent to implement individual tasks and restart them until all tasks are implemented.

This is a Ralph Wiggum loop, but adapted to VS Code Copilot.

Orchestrator Agent Creation Guide

This document provides a comprehensive guide for creating Orchestrator Agents in OpenCode. Based on the reference implementation (@agent/orchestrator.md), this guide details the structure, patterns, and best practices for building intelligent routing agents.

1. Overview

An Orchestrator Agent serves as a central dispatch system. Unlike standard agents that execute tasks, an orchestrator's sole purpose is to analyze user requests and delegate work to specialized subagents.

Core Characteristics