AI Code-Generation Software is a production-ready, self-hostable software application that transforms natural-language prompts into complete, multi-file, tested, and documented codebases. At its core is a five-agent pipeline — Planner, Coder, Reviewer, Test Generator, and Doc Generator — that handles the full development lifecycle from architectural planning through code review, self-healing, test generation, and documentation, all streamed live token-by-token in a Monaco (VSCode-grade) editor.
The software is built for developers, technical founders, and agencies who want to own and operate their own AI code-generation product, internal developer tooling, or SaaS offering. It ships as a complete, Docker Compose-ready application with a React 18 + TypeScript frontend, a FastAPI Python backend, and a supporting infrastructure stack of PostgreSQL, Redis, ChromaDB, and Nginx — everything required to deploy on your own infrastructure from day one.
Key capabilities include:
- Five-agent generation pipeline with per-agent reasoning streamed in real time through a browser-based Monaco editor
- RAG over your existing codebase using ChromaDB and sentence-transformers embeddings, injecting semantically relevant context into every generation call
- Self-healing code loop that executes generated code in a sandboxed environment and automatically patches failures for up to three iterations
- AST-aware code review powered by tree-sitter, detecting anti-patterns, unused imports, and style violations before delivery
- Multi-language support covering Python, TypeScript/JavaScript, Go, Rust, Java, C++, SQL, and Bash
- Project management with save, version, export as ZIP, and multi-project support
- JWT authentication with free and pro monthly token-tier enforcement
- GitHub Actions CI/CD pipelines, Alembic database migrations, and Nginx reverse proxy configuration included
- Training data pipeline that collects accepted generations, applies quality scoring and filtering, and exports fine-tuning-ready JSONL datasets in both chat and completion formats; the pipeline ships with pre-built synthetic example pairs specifically designed to improve prompt-to-code coverage and to protect real user information from appearing in training datasets
