From 312ba0dd1eca6780ea83523c0da701f69d16bd6d Mon Sep 17 00:00:00 2001 From: gdesai23 Date: Thu, 9 Apr 2026 00:10:29 +0530 Subject: [PATCH] docs: add graph.json + LLM workflow example to README --- README.md | 36 ++++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/README.md b/README.md index 54230f72e..2061f5ba6 100644 --- a/README.md +++ b/README.md @@ -101,6 +101,42 @@ The always-on hook surfaces `GRAPH_REPORT.md` — a one-page summary of god node Think of it this way: the always-on hook gives your assistant a map. The `/graphify` commands let it navigate the map precisely. +## Using `graph.json` with an LLM + +`graph.json` is not meant to be pasted into a prompt all at once. The useful +workflow is: + +1. Start with `graphify-out/GRAPH_REPORT.md` for the high-level overview. +2. Use `graphify query` to pull a smaller subgraph for the specific question + you want to answer. +3. Give that focused output to your assistant instead of dumping the full raw + corpus. + +For example, after running graphify on a project: + +```bash +graphify query "show the auth flow" --graph graphify-out/graph.json +graphify query "what connects DigestAuth to Response?" --graph graphify-out/graph.json +``` + +The output includes node labels, edge types, confidence tags, source files, and +source locations. That makes it a good intermediate context block for an LLM: + +```text +Use this graph query output to answer the question. Prefer the graph structure +over guessing, and cite the source files when possible. +``` + +If your assistant supports tool calling or MCP, use the graph directly instead +of pasting text. graphify can expose `graph.json` as an MCP server: + +```bash +python -m graphify.serve graphify-out/graph.json +``` + +That gives the assistant structured graph access for repeated queries such as +`query_graph`, `get_node`, `get_neighbors`, and `shortest_path`. +
Manual install (curl)