Developer Builds Agentic AI Coding Assistant With RAG Memory and Multi-Agent Pipeline

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A developer has released Dev-Code, an open-source AI coding assistant designed to debug Python and JavaScript code using a structured multi-agent workflow. The tool runs broken code in a sandboxed environment, analyzes runtime errors, and searches a vector memory store for previously verified fixes before invoking Google's Gemini model. Dev-Code employs four specialist agents — Orchestrator, Analyzer, Fixer, and Verifier — following a ReAct-style pipeline to ensure fixes are validated rather than merely suggested. Verified fixes are stored in Supabase-backed vector memory using Gemini embeddings with cosine-similarity thresholding to filter out superficially similar but irrelevant past solutions. The project also uses tree-sitter to parse large files across hundreds of languages, sending only the relevant code section to the fixer agent before splicing the correction back into the full file.

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