@nanonets/graft weekly downloads statistics

<div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="assets/trail-code-banner-dark.png"> <img src="assets/trail-code-banner.png" alt="Trail-code (previously graft), by Trail. A map of a codebase, with one team decision pinned to it: keep retries at 4." width="100%"/> </picture>

The self-learning context layer for coding agents.

Trail-code gives Claude Code, Cursor, Codex and other coding agents a map of your code, and what your team has learned about it.

	Code map (previously graft)	Learnings
What is it?	An index of your code: every function and who calls it, so your agent sees what a change can break	What you, your agents and your team have learned and decided
What does it do for your agent?	Takes it straight to the right files	Lets it pick up where you and your team left off
Where does it come from?	Your code, read on your machine	Your conversations, as you work

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46% fewer tool calls, 60% less time, and more real issues solved.

	Cold agent	Agent with the graft map
Tool calls, 162 runs	baseline	46% fewer
Uncached input tokens, 162 runs	baseline	42% fewer
Time, 162 runs	baseline	60% less
SWE-bench Verified, 50 real issues	27 solved (54%)	33 solved (66%)

npm install -g @trailhq/trail-code && trail init

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[!NOTE] Graft is now Trail-code. Graft lives on as the graft map, and every graft command still works. What's new is learnings. What changed

<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="assets/trail-code-how-it-works-dark.webp"> <img src="assets/trail-code-how-it-works.webp" alt="Without Trail: each task starts a new session with an agent that has no memory. It searches blind, opens the wrong files, guesses how the code is wired, ships the task, and the context is lost. This repeats for every task and every teammate's agent. With Trail, your agent already knows your code: trail init builds the graft map, a hook points the agent to it before each session, the agent reads the graph with its impact radius, gets precise context, ships the task, then saves learnings. One graph, every agent, up to 4x tokens saved." width="880"/> </picture> </p>

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What changes in your agent

Without Trail-code	With Trail-code
Greps and opens file after file to find the code it needs	Gets the right files and functions with your prompt
Re-explores code a teammate already figured out last week	Starts from their learning: what they decided, what they ruled out, what to watch out for
Undoes a teammate's decision, because it never knew about it	trail check flags it before the PR, quoting who decided it and why
Finds out at merge time that a teammate changed the same code	Says so right after it edits code a teammate's open branch also changes, and whether the two clash
Has to be told the same things every session	Starts from what you've told it before: trail init finds what you said more than once in your past sessions
You write and update CLAUDE.md by hand	Learnings are saved from your conversations, in files you can read

Same model, more tasks solved. On SWE-bench Verified (real GitHub issues, graded by the maintainers' own tests), Claude Sonnet 5 solved 33 of 50 issues with the graft map and 27 of 50 without it, using 23% fewer tokens. Details

Cheaper and faster. Across 162 runs of the same agent on the same tasks: 32% lower cost, 42% fewer uncached input tokens, 60% less time, and the same share of correct answers. On PocketBase, 15 real tasks ran 21% cheaper and 14% faster. Details

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