6 posts carrying this tag.
We measured how coding agents search with grep on Home Assistant and Saleor: about 150 model runs and 13 million tokens. Grep finds the files, then buries them in noise, and it can't follow inheritance or code that never says the name. A bigger model doesn't fix it.
claude-context is Zilliz's open source code search MCP server, around 11,800 stars, chunking along AST boundaries and serving hybrid BM25 and vector retrieval with Merkle tree incremental reindexing. The engineering is competent. We cannot credibly write a takedown of it, because we shipped the same architecture in October 2025, watched it fail on accuracy, and threw it away. This is what that cost us to learn.
Every tool that feeds your codebase to an AI coding agent picks one of four representations. AST parsing, vector embeddings, code graphs, or a verifiable intermediate representation. Here is what each one keeps, what each one throws away, and why the first three are features of the fourth.
Semantic code search keeps getting better at finding the right code, and it keeps disappointing teams who expected better answers from their AI tools. The reason is that those are two different problems. Finding code is retrieval. Understanding code is representation. Better retrieval over a poor representation has a ceiling, and here is why a verifiable code IR raises it.
Vector embeddings treat code like english prose. AST parsers see structure but not meaning. Raw LLMs forget everything every session. Here is why the LLM compiler pattern with a persistent semantic graph is the only approach that actually works for cross-repository code intelligence, and why open source models at $7 per 1000 files make it practical today.
Move beyond keyword matching with semantic code search. Learn how embeddings, function-level understanding, and knowledge graphs transform code discovery—plus why citations matter for enterprise teams who can't afford hallucinated answers.
