(img is benzi demo on SQLite) Benzi compiles a repository into a resolved, queryable map of symbols, calls, references, inheritance and data flow before the model starts working. Current coding agents mostly discover program structure by repeatedly searching and reading source code, or by retrieving approximate chunks with embeddings. Benzi instead gives the model deterministic tools like “what calls this?”, “where did this value come from?”, and “what will this edit affect?” The model can still read source whenever necessary, but it does not have to rediscover the structure of the codebase from text every time. Benzi tracks three truth tiers: RESOLVED for relationships proven statically, CANDIDATE when static analysis cannot prove an edge, and OBSERVED for what actually happened at runtime. This lets the model use deterministic intelligence without pretending static analysis is infallible. (also keeps track of external/unresolved/unindexed for bookkeeping). This article explains it better honestly: https://ai.miraheze.org/wiki/Benzi It supports Python, JavaScript, TypeScript, Java, C#, C++, C, Go, Rust and Ruby, plus deterministic HTML/CSS/DOM resolution, complies locally, model agnostic, and free to use during beta. FAQ includes comparison with CodeGraph and other indexers so pls read that instant demo: https://varianttech.net/demo all benchmarks: https://varianttech.net/benchmark github: https://github.com/oooscoos/Benzi webstie: https://varianttech.net/ submitted by /u/DonkeyTheKing
Originally posted by u/DonkeyTheKing on r/ArtificialInteligence