Deep Dive into Claude Code Architecture and a Multi‑Agent Orchestration Framework

The AI tooling landscape is exploding with coding assistants and agent frameworks, but few resources explain how they work under the hood. This article introduces two recent open‑source projects that fill that gap.

learn‑coding‑agent: Dissecting Claude Code

A TypeScript‑based educational repository (10,496 stars) curated by sanbuphy that documents Claude Code’s architecture, data telemetry, hidden model codenames, undercover mode, remote‑control interfaces, and the upcoming roadmap (e.g., Opus 4.7, Sonnet 4.8, KAIROS).

Telemetry & privacy analysis – what data is collected and why it cannot be disabled.

Hidden features & model codenames – meanings of Capybara, Tengu, Numbat and their feature toggles.

Undercover mode – how official staff hide AI author identity in public repos.

Remote control & emergency switch – hourly polling settings and dangerous‑change dialogs.

Future roadmap – 17 unreleased tools including fully autonomous agents.

The project also presents a 12‑layer progressive agent framework that shows how Claude Code evolves from a basic loop to a production‑grade system:

s01 Core loop – while‑true loop calling the Claude API.

s02 Tool scheduling – buildTool() factory provides safe defaults.

s03 Planning ability – EnterPlanModeTool + TodoWriteTool double completion rate.

s04 Sub‑agent – AgentTool + forkSubagent.ts splits large tasks.

s05 On‑demand knowledge – SkillTool + memdir/ lazy‑loads knowledge.

s06 Context compression – autoCompact + snipCompact three‑tier strategy.

s07 Persistent tasks – file‑based task graph.

s08 Background tasks – DreamTask + LocalShellTask.

s09 Agent team – TeamCreate/Delete for persistent teammates.

s10 Team protocol – SendMessageTool drives all negotiations.

s11 Autonomous agent – idle cycles + auto‑claim.

s12 Worktree isolation – EnterWorktreeTool.

It is aimed at developers who want to understand Claude Code, engineers designing their own agent frameworks, and technical enthusiasts interested in AI coding‑assistant architecture.

open‑multi‑agent: Production‑grade Multi‑Agent Orchestration

A model‑agnostic TypeScript library (2,080 stars, MIT license) that runs multiple agents in a single Node.js process, eliminating subprocess overhead while supporting team collaboration and DAG‑based task scheduling.

Multi‑agent team – mix roles, tools, and models via a message bus.

Task DAG scheduling – automatic topological sort, dependency waiting, parallel execution.

Model‑agnostic – Claude and GPT can coexist in the same team.

In‑process execution – lightweight, suitable for serverless, Docker, CI/CD.

How to use (single agent):

import { OpenMultiAgent } from '@jackchen_me/open-multi-agent'

const result = await orchestrator.runAgent(
  { name: 'coder', model: 'claude-sonnet-4-6', tools: ['bash', 'file_write'] },
  '写一个反转字符串的 TypeScript 函数'
)

How to use (three‑agent team):

const team = new Team({
  agents: [
    { name: 'researcher', role: 'researcher', tools: ['web_search'] },
    { name: 'coder', role: 'engineer', tools: ['bash', 'file_write'] },
    { name: 'reviewer', role: 'reviewer', tools: ['bash'] }
  ],
  tasks: [
    { id: 'task-1', description: '调研 React 18 新特性', assignedTo: 'researcher' },
    { id: 'task-2', description: '写 React 18 示例', assignedTo: 'coder', dependsOn: ['task-1'] },
    { id: 'task-3', description: '运行并检查错误', assignedTo: 'reviewer', dependsOn: ['task-2'] }
  ]
})

Install the library with: npm install @jackchen_me/open-multi-agent The intended audience includes developers who want to build multi‑agent systems, teams needing automated task flows, and engineers looking to experiment with agent collaboration.

Together, learn‑coding‑agent helps you understand the theory behind Claude Code, while open‑multi‑agent provides a practical implementation, offering a complete “learn‑then‑apply” pathway.