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What is a code knowledge graph?

Graphify is a code knowledge graph. It reads your code, records the symbols in it and how they connect (calls, imports, inheritance, dependencies), and lets you or your agent walk those connections. Every edge says where it came from and how sure it is. See Provenance. Other tools help agents and people understand code too. They answer different questions, and they often work well together.
Try it on your own repo. Start with hosted Graphify, or build a graph locally with uv tool install graphifyy.

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At a glance

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Compared one by one

How it works: files are split into chunks, each chunk is turned into an embedding, and a question returns the chunks whose embeddings are closest.Where it shines: fuzzy, natural-language search over a lot of text, including docs and comments.The gap for code: similar text isn’t the same as connected code. A caller in another file can look nothing like the function it calls, so it may not come back. Graphify follows the actual call and import edges instead.May fit better when you mostly need to search prose, or you want “find something like this” across a large mixed corpus.
How it works: a tool writes pages about your repository, such as overviews, module summaries and diagrams, that people and agents read.Where it shines: onboarding. A good written overview is faster to read than any graph.The gap for code: a summary is a snapshot of what someone, or something, chose to describe. It can’t list every caller of a function, and it can drift from the code. Graphify answers from the extracted structure, with a source reference on each edge.May fit better when you want a readable tour of a codebase more than precise answers about it.
How it works: your editor talks to a language server using the Language Server Protocol, which offers go to definition, find references and call hierarchy for the files open in your workspace.Where it shines: exact, type-aware answers for one language in one project, as you type.The gap for code: a language server answers one lookup at a time, inside a running editor session, usually for one language. Graphify keeps the whole picture as a saved graph your agent can query, across languages and repositories, and can trace several hops at once with path and affected.May fit better when you need compiler-accurate navigation while editing. Many teams use both.
How it works: these tools connect code to the work around it, such as issues, reviews, pipelines and owners, so you can ask questions about the delivery process.Where it shines: questions like “which work item introduced this?” or “what’s blocking this release?”The gap for code: the code layer is one part of a wider model, and its depth varies, for example in which branches or languages it covers. Graphify goes deep on the code itself: symbol-level edges, confidence tags, and impact across repositories.May fit better when your main questions are about process and planning rather than how the code fits together.

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Use them together

Most teams don’t pick one. A common setup:
  • A language server in the editor for exact lookups while you type.
  • Search for finding prose, docs and “something like this”.
  • Graphify for the questions in between: what calls this, how two parts connect, and what a change could break, for both you and your agent.

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What Graphify is not

  • Not a chat memory. It doesn’t store your conversations. It describes your code.
  • Not a search index. It doesn’t rank text by similarity. It records connections.
  • Not a replacement for reading code. Each answer points back to the source it came from.

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FAQ

It depends on the question. A vector database finds text that reads like your question. A knowledge graph follows how code is actually connected. For “what calls this?” or “what breaks if I change this?”, you want the graph. For fuzzy search over docs, a vector index works well. Many setups use both.
No. Keep your language server for in-editor navigation. Graphify adds a saved graph your agent can query across languages and repositories.
Not for code. Code is read locally with tree-sitter. Only docs, PDFs and images go through a model, and you can skip them with --code-only. See Reduce token usage and cost.

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Next steps

Provenance

How to read an edge and how sure it is.

Check impact

See what a change could affect before you make it.

Try it in Graphify

We build and host the graph for you. The Free plan (up to 3 repositories) needs no card. Pro and Teams start with a 14-day trial, card required.