Claude Code from Source
This article provides a technical breakdown of the architecture behind Claude Code, an agentic AI tool for developers. It covers the system's design, including memory management, agent loops, and cost-saving strategies.
Why it matters
Understanding the architecture of production-grade AI agents is essential for engineers building scalable and efficient autonomous software systems.
When Claude Code shipped on npm, the source maps came with it. We read every file. This book distills the architecture, design decisions, and transferable patterns into 18 chapters you can learn from and apply to your own systems.
What you'll learn The agent loop How an async generator drives the entire system — streaming model output, executing tools, recovering from errors, and compressing context across 4 layers.
A 14-step pipeline from model request to tool result. Permission resolution, speculative execution, concurrent batching by safety classification.
How sub-agents share prompt cache prefixes to cut costs by 95%. Fork agents, coordinator mode, swarm teams with mailbox messaging.
File-based memory with an LLM-powered recall system. Four memory types, staleness warnings, and a Sonnet side-query that beats embedding search.
Startup in 240ms via parallel I/O. Slot reservation saving context in 99% of requests. Bitmap pre-filters for fuzzy search. Every millisecond accounted for.
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