Token reduction
Graphify glossary
The context-window savings from answering with graph structure instead of pasting whole files into an assistant's context.
Token reduction is the context-window savings you get when an assistant answers from a graph instead of from raw files. The two costs being compared: when an assistant greps, it opens the files that match and pastes them into context wholesale (every import block, every unrelated function, every license header) and pays for every token, on every question, in every session. When it queries a graph, it receives just the relevant nodes and edges: the callers of the function, not the full text of every file that mentions its name.
The savings compound for two reasons. First, structural answers are small: 'these 12 functions call processPayment, here are their locations' is a few hundred tokens, where the grep-and-read loop behind the same answer might pull in dozens of files. Second, the graph persists between sessions, so the assistant isn't re-deriving the architecture from scratch each time; the mapping cost is paid once at /graphify . time, then amortized over every question after.
Honest framing matters here: actual savings depend entirely on your repo, your questions, and how your assistant explores. One community user reported 71.5× fewer tokens on their workload, a community-reported figure from real usage, not a controlled benchmark, and your number will differ. The durable claim is directional: traversing structure sends less text to the model than pasting files, which means lower cost and more of the context window left for the actual task.
Related terms
- Traversal / hop: Following edges from node to node: one hop is a single edge, a traversal is a path of them. How graphs answer multi-step questions.
- RAG (retrieval-augmented generation): A pipeline that chunks documents, embeds them as vectors, and retrieves the most similar chunks at query time: lookup by resemblance, not structure.
- MCP server: A server implementing the Model Context Protocol, the open standard that lets AI assistants call external tools, like a code graph.