“At 4 AM on March 31, 2026, I woke up to my phone blowing up with notifications. The Claude Code source had been exposed, and the entire dev community was in a frenzy. My girlfriend in Korea was genuinely worried I might face legal action from Anthropic just for having the code o...”
You know that feeling when you want to understand how a proprietary AI agent system wires its tools, orchestrates tasks, and manages context — but the source is closed? Claude Code users have built workflows around it without visibility into the harness architecture. This rewrite gives you a Python reference implementation that exposes the command routing, tool wiring, and runtime patterns that were previously opaque.
The Python port mirrors Claude Code's architecture through metadata shims. Load the manifest with `python3 -m src.main manifest` to see 70+ Python modules organized into subsystems like assistant, bootstrap, bridge, cli, commands, components, and tools. Run `python3 -m src.main commands` to list mirrored command entries. Run `python3 -m src.main tools` to see tool inventories. The CLI lets you route prompts across command/tool registries, bootstrap sessions, and run turn loops — but these are orchestration shims, not a working AI agent. You still need an LLM backend.
If you're a developer curious about agent harness architecture, tool wiring patterns, or how production AI systems orchestrate commands — this gives you a reference implementation to study. Not useful if you need a working AI agent today (you still need an LLM backend). Not production-ready — the README explicitly states it's 'not yet a complete one-to-one replacement for the original system.'
Worth exploring if you study agent architectures or want to understand Claude Code's design patterns. The Python code is readable and well-organized. However, understand the legal/ethical context: this is based on exposed proprietary source, and the creator acknowledges concerns about whether 'legal is the same as legitimate.' The project is hours old, experimental, and not production-ready. Check the included essay on AI reimplementation ethics before diving deep.
Deep-dive insight, Easy and Pro modes, plus action playbooks — the full breakdown is one tap away.