DeepCode

DeepCode, billed as "Open Agentic Coding," is a project from HKU's Data Intelligence Lab that uses a system of cooperating AI agents to turn high-level inputs into working code. Its three named workflows are Paper2Code, Text2Web, and Text2Backend — so you can hand it a research paper and ask for an implementation, describe a website in natural language and have a frontend produced, or describe a backend service and have one scaffolded for you. The subtitle frames it as "advancing code generation with multi-agent systems."

The way it works is by coordinating several large language model agents that each play a role in the development process. The README mentions distinct Default, Planning, and Implementation phases, each of which can be paired with its own model. Through an OpenRouter integration, the settings UI can fetch the live model catalog, cache it, and let you pick specific model ids (z-ai/glm-5.1 is given as an example) for each phase without hand-editing configuration files. The project offers two ways to drive it: a terminal CLI for command-line workflows and CI integration, and a web interface dashboard for a more visual experience. The repository links a paper on arXiv that backs the approach and points to an introduction video on YouTube, plus Discord and WeChat community channels.

You would use DeepCode when you want an AI-assisted shortcut from an idea, specification, or research paper to a runnable codebase, and you want more structure than a single chat-with-an-LLM session — a multi-agent pipeline with planning and implementation broken apart. It is written in Python (the badges call out Python 3.13). The full README is longer than what was provided.