Graph vs RAG

What RAG throws away.

Embedding your code flattens it into disconnected chunks. Graphify keeps the entities and the typed relationships between them, and hands your coding assistant a graph it can traverse.

A code knowledge graph is a structured representation of a codebase in which functions, classes, files, tables, and docs are nodes, and their real relationships — calls, imports, defines, references — are typed, directed edges. Where RAG retrieves chunks of text that resemble a question, a knowledge graph lets an assistant traverse those edges, answering multi-hop questions by following an explicit, inspectable path.

Vector / RAG

graphify

Returns

RAGFuzzy top-k text chunks

graphifyConnected entities & relationships

Reasoning

RAGModel guesses from snippets

graphifyTraversable paths the model follows

Multi-hop

RAGBreaks past 1–2 hops

graphifyNative multi-hop traversal

Freshness

RAGRe-embed on every change

graphifyUpdate a node, links stay intact

Explainability

RAGOpaque similarity score

graphifyEvery answer traces to a path

Trust

RAGOpaque similarity score

graphifyConfidence tag on every edge: EXTRACTED / INFERRED / AMBIGUOUS

Privacy

RAGChunks shipped to an embedding API

graphifyOn-device, no telemetry

Cost at scale

RAGRe-embedding + storage grows fast

graphifyIncremental; edges are cheap

Debugging

RAGGuess why a chunk matched

graphifyInspect the exact traversal

Setup

RAGChunk, embed, tune top-k

graphifyuv tool install graphifyy

Why relationships beat similarity

Retrieval-augmented generation — introduced by Lewis et al. (2020) for knowledge-intensive NLP over Wikipedia — splits your documents into chunks, embeds them, and pulls the top-k most similar chunks at query time. It works for lookup, but it throws away the one thing agents need to reason: how things connect. Graphify keeps that structure as entities and typed relationships, so an agent can follow a real path from question to answer.

The multi-hop failure isn't a tuning problem; it's architectural. A top-k lookup is a single round of nearest-neighbor search, but a two-hop question needs the answer to hop one before hop two can even be formulated — multi-hop reasoning is hard enough for retrieval systems that dedicated benchmarks like HotpotQA (Yang et al., 2018) were built to measure it. A graph sidesteps the problem: each hop is one edge, so a three-hop question is a three-edge traversal, and the third hop is exactly as reliable as the first. Notably, the RAG literature itself has been moving this direction — GraphRAG (Edge et al., 2024) builds a knowledge graph over the corpus precisely because flat chunk retrieval can't answer questions that span it. Graphify simply starts from the graph.

Freshness without re-embedding

When a source changes, RAG re-embeds: every stale chunk goes back through an embedding model before it can be retrieved again. Graphify's code edges come from a parser, not a model — tree-sitter walks the AST deterministically, with no API call — so a changed file means re-parsing that file and updating its nodes. The surrounding edges stay intact, and your memory stays current without a re-index tax.

Answers you can trace

Because every result is a traversal, every answer traces to a path through your real code. Every relationship on that path carries a confidence tag: EXTRACTED, INFERRED, or AMBIGUOUS. That's the difference between "the model probably saw the right chunk" and "here is the exact route it took, and how sure we are about each hop."

Common questions

Is graphify a replacement for my vector database?

For coding-assistant memory, usually. Teams that adopt graphify tend to retire the chunk-embed-retrieve pipeline entirely, since the graph handles retrieval and adds structure the pipeline never had. Keep a vector index if your main workload is fuzzy search over large volumes of prose; embeddings are genuinely good at that job, and graphify doesn't try to beat them at it.

Can I use both?

Yes. Some teams keep vectors for pure semantic recall and use graphify for everything that involves relationships between entities. graphify can also index embeddings as node attributes.

Does graphify use embeddings at all?

It can store embeddings as one signal on nodes. Retrieval itself is a graph traversal, so results come back connected and explainable instead of as a ranked list of lookalike chunks.

How hard is it to migrate off RAG?

Point graphify at the same sources you were embedding; it extracts entities and relationships automatically. There is no schema to design and no index to tune, so most of the work is deleting the old pipeline.

Start in one command

Try it on your own repo.

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