Guide

What is code RAG? Retrieval-augmented generation for coding agents

TL;DR: Code RAG retrieves only the relevant slices of your codebase for each request — using hybrid lexical + semantic search — so a coding agent is faster, cheaper, and more accurate than one that sends the whole repo.

Code RAG (retrieval-augmented generation for code) is the technique of fetching only the relevant pieces of your codebase for a given task, then giving those to the model — instead of dumping the whole repository into the prompt.

Why it matters

Large language models have a fixed context window and you pay per token. If a coding agent sends 50,000 tokens of code on every turn, it's slow, expensive, and the model's attention is spread thin. Code RAG solves all three by sending only what's relevant.

How good code RAG works

agentty's retrieval engine is a strong reference implementation:

  • Hybrid search — combines BM25 (lexical/keyword) with dense embeddings (semantic), so it matches both exact identifiers and conceptual queries.
  • Code-aware chunking — splits along function/class boundaries rather than fixed line windows, so retrieved context is coherent.
  • HNSW ANN — fast approximate nearest-neighbour search over the embedding index.
  • GraphRAG expansion — follows references to pull in related definitions the query didn't name directly.
  • A learning loop — retrieval quality improves the more you use it on a repo.

The payoff

  • Lower cost — often 80%+ fewer context tokens.
  • Faster responses — smaller prompts.
  • Better answers — the model sees relevant code, not noise.

Try it

curl -fsSL https://agentty.org/install.sh | sh

agentty runs retrieval locally, with zero external dependencies. See the retrieval docs.

Install agentty in one line — a single static binary.

curl -fsSL https://agentty.org/install.sh | sh
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