The Tool Desk
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What Graft is described as doing
The current Graft repository describes a tool that builds a graph of a codebase, integrates that context with coding agents, and keeps the graph aligned with the working tree. It says graph construction is deterministic and does not call a model; summaries may use a provider chosen by the user and the user’s own key. Graft project repository
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That description differs materially from SitePoint’s August 20, 2026 article, which presents Graft as a Claude Code hook framework that intercepts tool execution and deduplicates or trims outputs. The repository is the stronger source for the project’s current identity. The available sources do not establish that the article’s hook-based explanation accurately describes Graft, so it should not be treated as a verified account of its implementation. SitePoint article
What the 42% figure measures
In its 2026 repository documentation, the Graft project reports a 162-run efficiency sweep across two repositories, with three trials per task grouping. It compared cold Claude Code with two Graft variants: a context bundle supplied up front and context retrieved on demand. The project says the same agent and file tools were used across variants and that cost accounting was cache-aware. The reported mean was 8,070 tokens per task for cold Claude Code and 4,650 with Graft, a 42% reduction. These are project-reported results, not an independent evaluation. Graft benchmark documentation
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| Measure | Cold Claude Code | With Graft | Project-reported change |
|---|---|---|---|
| Mean tokens per task | 8,070 | 4,650 | 42% lower |
| Mean cost per task | $0.0429 | $0.0292 | 32% lower |
| Mean tool calls per task | 4.2 | 2.3 | 46% fewer |
| Mean latency per task | 39.8 seconds | 15.8 seconds | 60% lower |
| Correctness | 93% | 93% | No difference reported |
All values in this table are from the Graft project’s 162-run efficiency sweep, as described in its 2026 repository documentation. They are averages for that evaluation, not expected outcomes for a particular developer, task, or codebase. The documentation’s description of the sweep is not a substitute for raw run data or independent replication.
Why the SWE-bench numbers are a separate result
The same repository documents a distinct comparison on 50 SWE-bench Verified instances. It says both arms used the same model and Docker images and that results were assessed with the official grader. The project reports more resolved instances with Graft, but its aggregate token, cost, and call totals in this comparison cover only instances resolved by both arms; correctness is calculated over all 50. That difference in denominators matters when interpreting the resource totals. Graft benchmark documentation
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| Measure | Without Graft | With Graft | Project-reported difference |
|---|---|---|---|
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| Tokens | 142.0M | 109.4M | 23% fewer |
| Cost | $52.34 | $42.43 | 19% lower |
| Tool calls | 1,370 | 1,031 | 25% fewer |
| API requests | 2,455 | 1,875 | 24% fewer |
| Wall-clock time | 13,094 seconds | 8,922 seconds | 32% less |
These figures are Graft project results for its separate 50-instance comparison, not a second measurement of the 162-run efficiency sweep. The two evaluations have different designs and should not be combined into a single claim about savings.
How to interpret the claim for your work
- Treat 42% as a benchmark average. It does not mean every task will use 42% fewer tokens. Task type, repository structure, model, and setup can affect the result.
- Distinguish the product from the article’s explanation. The repository describes codebase graphs and agent integration; the SitePoint article describes interception and output trimming. The available sources do not resolve that conflict in favor of the hook account.
- Read correctness alongside efficiency. The project reports equal 93% correctness in the efficiency sweep. In its separate SWE-bench comparison, it reports 33 of 50 instances resolved with Graft versus 27 of 50 without it.
- Keep attribution visible. Both benchmark sets are reported by Graft itself. The sources do not establish independent replication or provide a basis for treating the figures as universal performance guarantees.
The repository summarizes its goal this way: “An agent that reads the graph should be cheaper and faster without getting more answers wrong.” That is the project’s stated proposition; the figures above describe its own evaluations rather than independent confirmation. Graft project repository
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