1jehuang/jcode

jcode solves the problem of high resource consumption when running multiple AI coding agents concurrently, which is common in complex development workflows. Unlike other tools that consume hundreds of MB per session, jcode achieves significantly lower RAM usage (e.g., 27.8 MB with local embedding off), enabling scalable multi-agent setups without hardware strain.

Developers can build multi-session coding workflows where multiple agents collaborate on different parts of a codebase simultaneously, such as parallel code review, automated refactoring, or simultaneous feature development. The harness supports infinite customizability, allowing integration with various LLMs and tools, and is optimized for performance, making it suitable for large-scale projects. Real-world use cases include CI/CD pipeline automation, pair programming at scale, and managing complex monorepos with multiple agents.