From Claude Code Source Leaked: Detailed AI Agent Architecture

You know that feeling when you're trying to understand how state-of-the-art AI coding agents actually work under the hood? The documentation tells you what they do, but not how they think, manage context, or orchestrate complex multi-step tasks. Building your own agent means guessing at architecture decisions that companies like Anthropic spent millions figuring out.

Claude Code is a terminal-based AI assistant built on Bun (not Node.js). When you type a message, a QueryEngine manages the conversation loop: it assembles context (git status, recent commits, CLAUDE.md), sends it to Claude's API with ~40 available tools (file editing, grep, bash execution), streams the response, and executes any tool calls the model requests. The 3-layer memory system uses MEMORY.md as an index, topic files loaded on demand, and searchable session transcripts. Subagents can be spawned in parallel using KV cache forking — meaning they share context without repeating work.

• 3-Layer Memory System — why YOU care: MEMORY.md index + on-demand topic files + searchable transcripts means the AI remembers your project context across sessions without re-reading everything
• KV Cache Subagent Forking — why YOU care: Parallel tasks are nearly free because spawned agents share the parent's context via cached tokens, not re-processing
• ~40 Built-in Tools — why YOU care: File operations, grep/glob with custom implementations, bash execution, web fetch, MCP integration — all permission-gated and streaming
• 5-Level Permission System — why YOU care: Fine-grained control over what the AI can do without asking, from read-only to full autonomous execution
• Multi-Agent Orchestration — why YOU care: Can spawn 'swarms' of workers in isolated git worktrees, collect results, and coordinate complex refactors
• Auto-Compaction — why YOU care: Automatically summarizes old context to stay within token limits while preserving key decisions and state

Who It Is For

If you're a developer who spends significant time in the terminal and wants an AI pair programmer that can actually execute changes (not just suggest them), this leak reveals the architecture behind one of the best implementations. Not useful if you need IDE integration (it's terminal-only) or if you're uncomfortable with an AI having write access to your codebase.

Worth Exploring?

The leaked source provides an unprecedented look at production-grade AI agent architecture. Multiple open-source projects are already emerging from analysis of the code. However, Anthropic has issued DMCA takedowns, and the legal status of using this code is unclear. Study the architecture patterns, but don't copy code directly.