---
type: source
title: "Introducing Archon - The Revolutionary Operating System for AI Coding"
description: "Archon is being reborn as an operating system and command center for AI coding assistants, giving them the two things they lack: RAG over your own knowledge and real-time project/task management, through a human-native UI and an LLM-native MCP server."
youtube_id: 8pRc_s2VQIo
url: https://www.youtube.com/watch?v=8pRc_s2VQIo
slug: introducing-archon-the-revolutionary-operating-system-for-ai-coding
published: 2025-08-13
duration: "0:28:46"
recency_rank: 76
raw: "../raw/introducing-archon-the-revolutionary-operating-system-for-ai-coding.md"
tags: [archon, context-engineering, mcp]
created: 2026-07-21
updated: 2026-07-21
---

# Introducing Archon - The Revolutionary Operating System for AI Coding

Cole relaunches Archon, his original agent that builds agents, as a beta operating system for AI coding assistants. His core argument: today's coding assistants like Claude Code and Cursor almost completely lack two pillars of context engineering, RAG over your own knowledge and documentation, and real project-management tools you can actually interact with. Archon fills both gaps by being two things at once: a sleek UI native to humans for managing knowledge, context, and tasks, and an MCP server native to the LLM, so human and agent collaborate on the same projects from their own natural interfaces.

The demo walks through setup (Docker, Supabase, an OpenAI/Gemini/Ollama key for LLMs and embeddings, with full local operation possible), copying IDE global rules to steer the assistant, and building a private configurable knowledge base by crawling URLs, sitemaps, and llms.txt or uploading PDFs, all chunked and embedded for RAG with real-time WebSocket crawl feedback via Crawl4AI. Because it is your own knowledge base with configurable RAG strategies and code-example extraction, Cole frames it as better than Context7. He then connects Claude Code to the Archon MCP and executes a PRP; the agent does agentic RAG (129 retrieved chunks plus a separate code-example search) and populates a Kanban task board in real time, moving tasks through backlog to in-process to review.

The key collaboration insight: because tasks live in a shared UI, a human can edit descriptions, reorder, or push work back to the backlog and the agent picks up the changes when it reaches that task, a far better loop than interrupting a coding assistant mid-thought, which causes hallucination. Maintainer Shawn Buck then discusses the vision: agents as co-pilots not replacements, visibility into where your codebase and sub-agents are, and a future of visually defining processes, sub-agent roles and hand-offs, and network views. Archon is positioned as congruent with, not a replacement for, frameworks like PRP, spec-driven development, and BMAD: a central command center that gives visibility across whatever workflow you use.

## Concepts covered

- [Context Engineering](../concepts/context-engineering.md)
- [Knowledge Bases](../concepts/knowledge-bases.md)
- [Agentic RAG](../concepts/agentic-rag.md)
- [Human in the Loop](../concepts/human-in-the-loop.md)
- [Planning with AI](../concepts/planning-with-ai.md)
- [Global Rules](../concepts/global-rules.md)
- [PRD-First Development](../concepts/prd-first-development.md)
- [Subagents Pattern](../concepts/subagents-pattern.md)
- [The AI Layer](../concepts/the-ai-layer.md)

## Entities

- [Archon](../entities/tools/archon.md)
- [MCP (Model Context Protocol)](../entities/tools/mcp.md)
- [Cole Medin](../entities/people/cole-medin.md)
- [Dynamous](../entities/organizations/dynamous.md)
- [Claude Code](../entities/tools/claude-code.md)
- [Crawl4AI](../entities/tools/crawl4ai.md)
- [Pydantic AI](../entities/tools/pydantic-ai.md)
- [LangGraph](../entities/tools/langgraph.md)
- [Mem0](../entities/tools/mem0.md)
- [Supabase](../entities/tools/supabase.md)
- [Ollama](../entities/tools/ollama.md)
- [OpenAI](../entities/organizations/openai.md)
- [GitHub](../entities/tools/github.md)

## Key moments

- **[0:00:00]** Recap: Archon was the first 'agent that builds agents'; now relaunching as an OS for AI coding
- **[0:00:33]** The problem: coding assistants lack RAG for knowledge and real project management tools
- **[0:02:10]** Archon is two things: a human-native UI and an LLM-native MCP server on the same projects
- **[0:03:14]** Quick-start setup: Docker, Supabase, OpenAI/Gemini/Ollama; fully local option
- **[0:05:24]** Copy IDE global rules to guide the coding assistant to use Archon effectively
- **[0:06:05]** Your own private, configurable knowledge base - the edge over Context7
- **[0:06:46]** Add knowledge: crawl URLs/sitemaps/llms.txt or upload PDFs, chunked and embedded for RAG
- **[0:07:30]** Real-time WebSocket crawl feedback via Crawl4AI (44 URLs found, batched)
- **[0:09:11]** Real-time project/task management: edit tasks in the UI while the agent runs
- **[0:11:04]** Executing a PRP; agent does agentic RAG (129 results) then populates the task board
- **[0:12:37]** Editing tasks in the UI beats interrupting the assistant, which causes hallucination
- **[0:16:41]** Shawn Buck joins; Cole builds backend/RAG, Shawn drives the UI/UX vision
- **[0:19:17]** Vision: a consistent command center for control and visibility across coding agents
- **[0:21:10]** Congruent with PRP, spec-driven development, and BMAD - not a replacement
- **[0:25:36]** Sub-agents as team roles with defined hand-offs; Archon gives visibility, not a new framework

## Transcript

[Raw transcript](../raw/introducing-archon-the-revolutionary-operating-system-for-ai-coding.md)

## Sources

- [Introducing Archon - The Revolutionary Operating System for AI Coding](./introducing-archon-the-revolutionary-operating-system-for-ai-coding.md) - "[0:02:10] Archon is two things: a human-native UI and an LLM-native MCP server on the same projects"
