u/Remarkable-One9371

I have been experimenting with a local-first context service for coding agents that builds a repo graph from AST/LSP-style facts instead of making the agent start with broad file search.

The useful pattern so far:

• index files, symbols, imports, calls, definitions, containment, and dependency edges
• let the agent query the relevant subgraph first
• expand to raw files, search, or LSP only when evidence is weak
• measure not only token count, but also whether the retrieved context would increase hallucination risk

In one benchmark pass, graph context used about 90% fewer input tokens than broad snippets while keeping the answer grounded enough for the tested tasks. The important caveat is that graph-first cannot mean graph-only. If retrieval is too narrow, the agent has to fall back to source reads and validation.

I'm curious how others are handling this for coding agents: do you prefer LSP-first retrieval, embedding/RAG retrieval, graph retrieval, or a hybrid?