Graft

<div align="center"> <img src="assets/graft-hero.png" alt="Graft — open-source context layer for large codebases" width="100%"/>

Turbocharge Claude Code, Cursor, Codex, Gemini & every coding agent: faster, cheaper, with contextual understanding specific to your codebase.

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Up to 4× cheaper and 3× faster, with better or no loss of correctness.

| Metric | Cold Claude Code | Claude Code with graft | |---|---|---| | Tool-call reduction | Baseline | +46% | | Token savings | Baseline | +42% | | Time savings | Baseline | +60% | | Correctness | 54% | 66% (+12 pts) |

</div> <p align="center"> <b>Stop repeating yourself to your coding agent.</b><br/> You correct it, and by the next session it has forgotten. Trail Brain manages your CLAUDE.md and AGENTS.md so it doesn't. </p> <p align="center"> &bull;&nbsp; Every correction you make becomes a rule your agent keeps.<br/> &bull;&nbsp; The ones that can't break get a hook that blocks it, not a note it ignores. </p> <p align="center"> <a href="https://app.trailhq.com/get-started?step=repo"><img src="https://img.shields.io/badge/Try%20it%20on%20your%20repo%20%E2%86%92-E5484D?style=for-the-badge" alt="Try it on your repo" height="34"/></a> </p> <p align="center"> <img src="assets/graft-comparison-demo.gif" alt="Side-by-side comparison of a coding agent working with and without graft" width="820"/> </p>

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Contents

• Quick start
• The problem
• What Graft does
• Benchmark
• SWE-bench Verified
• How the graph gets built
• Supported languages
• What's in a node
• What runs where
• Agent integration — MCP server · Claude Code (deep integration)
• CLI
• Search & orient (graft grep / graft map)
• Monorepos & multi-repo folders
• Visualize it (graft viz)
• Tested on your popular repos
• Development
• License

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Quick start

npm install -g @nanonets/graft   # install the CLI, once
graft init                       # build the graph + wire it into Claude Code

That is the whole setup. graft init asks which of your coding agents to wire up, builds graft/ from your code, and drops a statusline and hooks into .claude/, so from the next session on Graft rides along in Claude Code: it pulls the matching nodes into each prompt and rebuilds the graph in the background after every turn. No daemon, no re-indexing to remember, nothing to run or maintain by default — the graph is just files.

Nothing is written until you pick. Run graft init --dry-run to see every file it would touch first, or graft init --agents claude to skip the prompt and wire Claude Code alone.

graft build adds graft/ to your .gitignore automatically — the graph is a local, regenerable cache (like node_modules), not something you commit. What you share is the wiring init dropped into .claude/; each teammate runs graft build to generate their own graph:

git add .claude && git commit -m "wire in graft"

Prefer not to install globally? npx @nanonets/graft init works the same way.

<p align="center"> <img src="assets/graft-terminal.png" alt="Two commands — npm install and graft init — then Graft rides along in a Claude Code session, statusline synced" width="820"/> </p>

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The problem

Every task, your coding agent starts blind. Before it changes anything, it re-explores the repo: grep a term, open a file, follow an import, back out, try again. It is rebuilding a picture of a codebase it mapped an hour ago and threw away. That rediscovery burns most of a run's tool calls, tokens, and latency, and it is pure overhead:

• Repeated. Every task pays the exploration cost again, from zero.
• Discarded. Whatever the agent figured out dies with the session.
• Unshared. The next teammate, and their agent, start from scratch too.

Humans onboard to a codebase once. Agents onboard every single time.

<p align="center"> <img src="assets/graft-site-act-demo.gif" alt="A no-map agent's exploration trail wandering file to file before it finds what it needs" width="820"/> </p>

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What Graft does

Graft builds that understanding once and writes it into your repo as a folder of linked markdown files, one node per system, API, or concept.

• Real explanations, not a list of symbols. Each node says, in plain English, what a part of the system does and how it connects to the rest, the way a senior engineer would explain it. That is the part an agent actually needs so it can skip the exploration. It is not a dump of function names.
• A real graph you can read. No embeddings, no similarity search, no index to keep warm. The graph is a set of linked files your agent opens, greps, and follows, exactly the way it reads any other file in the repo.
• A local cache, not a committed artifact. graft build writes graft/ and adds it to .gitignore — it's a regenerable local cache, like node_modules. What you commit is the small wiring graft init drops in (.claude/, AGENTS.md, the MCP config); each teammate runs graft build to generate their own graph. No database, no server, no setup.
• Always fresh, automatically. Every query rebuilds the graph against the working tree first — structural, $0, ~3ms when nothing moved — so ask/grep/callers/skeleton/map describe the code as it is right now, including uncommitted edits. graft check is a local freshness signal; there's no stale index to babysit.
• Your provider, your key, your model. Summaries are written by any provider you choose — OpenAI, Anthropic (native), OpenRouter, Fireworks, Groq, OrcaRouter, a LiteLLM proxy, or a local model — under your own key. The structural code graph (graft build, graft check) is deterministic tree-sitter and never calls a model at all.

<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="assets/graft-cold-vs-graft-dark.png"> <img src="assets/graft-cold-vs-graft.png" alt="The same task, 'fix the auth bug', run two ways. A cold Claude Code session re-reads the repo and wanders file to file; Claude Code + graft loads its map once and rides the hooks to one clean pass. With Graft: 46% fewer tool calls, 42% fewer tokens, 60% less time, +22% more SWE-bench instances resolved." width="880"/> </picture> </p>

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Benchmark

An agent that reads the graph should be cheaper and faster without getting more answers wrong. That's the whole claim, so we measured it instead of asserting it.

The harness ran three variants of the same Claude Sonnet 5 agent with the same file tools: cold (explores from zero), Graft (a graft ask --source bundle pushed up front), and pull (graft_find_code/graft_file_api tools, nothing injected — context paid for only when asked). An Opus 4.8 judge scored correctness with a required-keyword floor, so a fast-but-wrong answer couldn't win by being fast. Cost is cache-aware: reads ≈0.1×, writes 1.25×, the billing model agents actually run under.

162 runs, two repos (graft itself and a real Node/Express auth service), 3 trials each, tasks split between single-file and multi-file questions.

| Metric (mean/task) | Cold Claude Code | Claude Code with graft | |---|---|---| | Cost savings ($) | 0.0429 | 0.0292 (+32%) | | Token savings | 8,070 | 4,650 (+42%) | | Tool-call savings | 4.2 | 2.3 (+46%) | | Latency savings (s) | 39.8 | 15.8 (+60%) | | Correctness | 93% | 93% (equal) |

Graft never answered worse than cold, on any corpus. The pull variant gave up most of that speed for something bigger: correctness jumped to 98%, +5 points over cold, the strongest single result in the sweep. Push when speed is what you need; pull when being right matters more.

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SWE-bench Verified

The sweep above is our harness measuring our mechanism. So we ran the industry-standard one too — SWE-bench Verified, real GitHub issues from real repos, graded by the official swebench harness. No judge model, no similarity score: your patch is applied, the maintainers' own tests are run, and you either flip the failing test without breaking the passing ones or you don't.

50 instances, same model on both arms — Claude Sonnet 5 — same Docker images, same turn limits. The only difference is whether graft is wired in.

| Correctness & efficiency | Cold Claude Code | Claude Code with graft | Improvement | |---|---|---|---| | Correctness | 27 / 50 (54%) | 33 / 50 (66%) | +12 pts | | Token savings | 142.0M | 109.4M | +23% | | Cost savings | $52.34 | $42.43 | +19% | | Tool-call savings | 1,370 | 1,031 | +25% | | API-request savings | 2,455 | 1,875 | +24% | | Wall-clock savings | 13,094s | 8,922s | +32% |

graft resolved 33 of 50 instances against Cold Claude Code's 27 — and got there with 25% fewer tool calls, 23% fewer tokens, and 32% less wall-clock time. Every correctness win has the same shape: the baseline patches one file and misses its siblings. On django-11532 it patched 1 of the 5 files the fix requires and broke 18 previously-passing tests, twice over. On django-16263 it patched 1 of 4 and scored 102 / 103. graft found the rest — and on django-16263 did it in half the tokens and half the time.

Two harnesses, two claims: the controlled sweep says graft is cheaper and faster, SWE-bench says it's also more correct.

<sub>Correctness over all instances; tokens, cost and calls over the instances both arms resolved, for a like-for-like comparison. Official SWE-bench V