[D] We reimplemented Claude Code entirely in Python — open source, works with local models

· Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

The Claw Code Agent has been released as a full Python reimplementation of the Claude Code agent architecture, initially reverse-engineered from an npm/TypeScript/Rust codebase. This open-source project aims to make the agent more accessible and extensible for Python developers, enabling local execution with open-source models. Key features include a complete agentic coding loop with tool calling, core tools for file operations (read/write/edit, glob, grep, shell), and slash commands like /help and /context. It also incorporates a context engine with CLAUDE.md discovery, session persistence, and tiered permissions ranging from read-only to unsafe. The agent is compatible with any OpenAI-compatible backend, including vLLM, Ollama, and LiteLLM Proxy, with Qwen3-Coder-30B-A3B-Instruct recommended for local, free operation.

Key takeaway

For AI Architects evaluating agentic coding solutions, the Claw Code Agent offers a compelling, open-source Python alternative to the original Claude Code. You can integrate this tool with existing OpenAI-compatible backends and run it locally with models like Qwen3-Coder-30B-A3B-Instruct, significantly reducing dependency on proprietary systems and enhancing customization. Consider exploring its tiered permissions and session persistence for secure and efficient development workflows.

Key insights

A Python-based, open-source Claude Code agent reimplementation enables local execution and extensibility.

Principles

Method

The agent uses a full agentic coding loop, tool calling for file and shell operations, and a context engine, supporting session persistence and tiered permissions.

In practice

Topics

Code references

Best for: AI Architect, AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.