Graphify vs Codegraph vs Graft: Which Code Graph Helps Your Coding Agent Ship Better?

Graphify, Codegraph, and Graft all give AI coding agents a pre-built map of your repository, and all three report the same kind of gain: fewer tokens, fewer tool calls, faster answers.

They differ in what they prove. Graphify and Codegraph publish results on answering questions about code, while Graft is the only one of the three that publishes results on completing real code changes: 33 of 50 SWE-bench Verified issues resolved, against 27 for the same agent without it.

TL;DR

• Pick Graphify if your project knowledge lives beyond code. It puts code, docs, papers, images and SQL schemas into one graph.
• Pick Codegraph if you want fast structural lookups with no API key. It has the largest efficiency numbers and the most mature tooling.
• Pick Graft if you care whether multi-file changes come out right. It is the youngest of the three and the most promising on evidence, because it tests finished patches, not answers.
• Pick none if your agent already finds what it needs in under ten tool calls. On small repos, grep is enough.

Graphify vs Codegraph vs Graft at a glance

Graphify, Codegraph, and Graft differ in scope and delivery. Graphify builds one knowledge graph from code and non-code files. Codegraph builds a code-only symbol graph in local SQLite and serves it through a single MCP tool. Graft builds a code graph plus readable markdown nodes, and pushes the relevant ones into each Claude Code prompt automatically.

Graphify is the widest net. It parses about 40 languages with tree-sitter, then links code to docs, papers and diagrams. Every edge is tagged as extracted or inferred, so you can see what was read and what was guessed.

Codegraph is the specialist. A Rust kernel indexes every symbol, call edge and dependency, and one codegraph_explore call returns source, call paths and blast radius. It needs no API key.

Graft is the readable one. Its graph is a folder of linked markdown files that the agent opens like any other file, with no embeddings or database. In Claude Code, hooks inject matching nodes into each prompt and warn about blast radius after each edit.

Do they actually save tokens?

Yes. Graphify, Codegraph, and Graft each report that agents use fewer tokens and tool calls with a graph than without. The percentages cannot be compared across tools, because each vendor ran a different test. Graphify and Codegraph measured question answering. Graft measured both question answering and finished code changes.

Codegraph posts the largest percentages, on the kind of task where a graph helps most: open-ended exploration of a big repo. Graphify's headline numbers come from conversational memory benchmarks (LOCOMO and LongMemEval), a point one of its own users has raised.

The useful question is not which percentage is biggest. It is which test looks most like your work.

Do code graphs make coding agents more correct, or just cheaper?

Both, on the evidence so far. Graft is the only one of Graphify, Codegraph, and Graft to publish graded results on finished code changes. On 50 SWE-bench Verified issues, Claude Code with Graft resolved 33, against 27 without it. That is 12 points higher, with 23% fewer tokens and 32% less wall-clock time.

Tokens, cost and calls are counted over the issues both arms resolved. Source: Graft README.

A worked example. On the issue django-11532, the fix needs changes in 5 files. The agent without a graph patched 1 of them and broke 18 passing tests. Graft's report says every correctness win had this shape: the baseline fixes one file and misses its siblings.

This is why correctness and cost move together. A graph tells the agent what else depends on the code it is about to change. That is the thing grep is worst at finding.

Graphify's code result points the same way on a smaller scale: answer coverage rose from 70.8% to 82.0% across 6 questions. Codegraph has not published a correctness score.

Is grep enough? When do you not need Graft?

Grep is enough when your agent finds the answer in under about ten tool calls. Graphify, Codegraph, and Graft all pay off in proportion to how much discovery a task needs, not raw repo size. On small repos or narrow questions, the savings shrink to roughly zero.

Codegraph's own table shows the pattern:

The ten-call threshold is this article's reading of that table, not a vendor claim. The saving collapses somewhere between 14 and 7 baseline calls.

User reports agree. One Graphify adopter found that Claude Code alone gave similar answers at equal or lower token use for common queries. Each graph query cost about 2K tokens, so three or four of them outweighed a few greps.

Before installing anything, watch one typical session. If the agent wanders through 20 or more calls before it starts editing, a graph will help. If it goes straight to the right file, it will not.

How do they keep the graph up to date?

Graphify, Codegraph, and Graft each refresh differently. Graphify updates when you run graphify update or when its hooks fire. Codegraph runs a file watcher that re-syncs about two seconds after a save. Graft checks the working tree before every query, in about 3 ms, and rebuilds only what changed.

• Graphify relies on an update command, git hooks or watch mode. Reviewers note the graph can go stale between updates, so answers still need checking against source.
• Codegraph watches native file events and debounces for 2 seconds. During that window it flags pending files so the agent reads them directly.
• Graft has no daemon. Each query compares the tree to the last build, so results reflect uncommitted edits. The check never calls a model.

A stale graph is worse than no graph, because the agent trusts it. Codegraph and Graft both treat freshness as automatic; with Graphify it depends on how you wire the hooks.

What does they cost to set up?

Graphify, Codegraph, and Graft are all free, MIT-licensed, and build their core code graph without an LLM. Model costs appear only for the optional layers: Graphify's extraction from docs and images, and Graft's --deep summaries. Codegraph has no LLM layer and never needs an API key.

Graft's incremental builds are cheap enough to run constantly. On its own 124-file repo, a cold build took 0.74 seconds and a rebuild after one edit took 0.18 seconds.

Codegraph scales furthest. It reports indexing the Linux kernel, about 70k files, in under 12 minutes on a 2-core machine.

How strong is the evidence?

None of it is independent. Every number for Graphify, Codegraph, and Graft was produced by its own vendor, on its own choice of tasks. As of October 2026, this comparison found no neutral benchmark that runs all three on the same repository. Treat each result as a credible signal, not a settled ranking.

• Graphify. The code result rests on 6 questions on one repo, scored by an LLM judge. The headline benchmarks measure conversational memory, not code navigation.
• Codegraph. One question per repo, with efficiency measured and correctness not reported. The harness is careful, though: it blocks its own CLI in the control arm after finding 26 of 28 control runs contaminated.
• Graft. 50 of SWE-bench Verified's 500 issues, run by the vendor. The gap is 6 issues, so a rerun could narrow or widen it. The token and cost figures cover only issues both arms resolved.

Two more caveats apply to Graft's case. SWE-bench Verified itself has been criticised for weak tests; one 2026 paper reports defects in many unsolved instances. And on PocketBase, both arms reproduced all 5 merged pull requests, so that test shows a 21% cost saving, not a correctness gain.

Graft still has the strongest type of evidence of the three, because finished patches graded by maintainers' tests are harder to game than answers graded by a judge. It also has the smallest community and the shortest track record.

Does your code leave your machine?

Not for the core graph. Graphify, Codegraph, and Graft all parse code locally with tree-sitter. Code is sent to a model only when you use Graphify's document extraction or Graft's --deep summaries. Codegraph and Graft both collect anonymous usage statistics by default, without code or file paths, and both can be switched off.

Which should you choose: Graphify, Codegraph, or Graft?

Choose by the job. Graphify fits teams whose context spans code and documents. Codegraph fits developers who want fast, local, code-only lookups at any repo size. Graft fits teams whose agents make multi-file changes and need them to be correct. For that last job, Graft is the most promising of the three.

The verdict. Codegraph is the safe default today. Graft is the one to watch, and the one to trial first if your agents ship changes that touch several files.