Traditional RAG (Retrieval-Augmented-Generation) has a fundamental problem: it converts your documents into embeddings and stores them in a vector database. That means: - Stale indexes — embeddings go out of date silently. You never know if the agent is reading your latest documents or a six-month-old snapshot - Black-box retrieval — you can't see why a document was retrieved or not. Debugging poor answers is guesswork - Chunking anxiety — split too small and you lose context. Split too large and retrieval quality degrades. There's no right answer - Infrastructure overhead — a vector database is another service to run, maintain, and pay for Semantic drift — embeddings are sensitive to how questions are phrased. A question about "cloud expenditure" may never match a document that says "AWS spend" Local Search Agent takes a different approach: BM25 keyword search via Meilisearch, structured metadata, and a LangGraph agent loop with tools. The agent searches your document index the same way a developer searches Stack Overflow — with real queries, real results, and full transparency into what was retrieved and why. The result is deterministic, auditable, and fast. You can see exactly what the agent fetched for every answer.

Local Search Agent

A framework that replace traditional RAG pipelines

A framework that replace traditional RAG pipelines