Trail-Code

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.

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

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

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

What changes in your agent

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

Contents

• How it works

• Quick start

• Part 1: The graft map, your code

• Part 2: Learnings, what you, your agents and your team know

  • Four kinds, three places · One decision, two teammates · A learning is a file · Your agent does the saving

• Your team

• What stays on your machine

• Benchmark · SWE-bench Verified · Tested on popular repos

• FAQ

• Reference: How the graph gets built · Supported languages · What's in a node · What runs where · Agent integration · CLI · Search & orient · Monorepos · Visualize it · Development

How it works

Trail-code has two parts. Both run on your machine.

1. The graft map: your code. trail init parses your repo with tree-sitter into a map of every file, function and who calls what. No model, no API key. It refreshes itself when the code changes, uncommitted edits included. Your agent gets pointers to the matching files and functions with each prompt, and sees what depends on a file right after it edits it.

2. Learnings: what you, your agents and your team know. When a session works something out, or you correct how something is done, your agent saves it as a learning. The next session on that code, yours or a teammate's, starts from it instead of digging again. Learnings about the code are committed with the code; learnings about you stay on your machine. On day one, trail init starts you off with what you told Claude more than once in your past sessions, and what reviewers flagged on more than one of the repo's merged PRs.

The graft map tells your agent where things are. Learnings tell it why they are that way, and how the work gets done here.

Quick start

npm install -g @trailhq/trail-code   # install the CLI, once
trail init                      # in your repo: build the graft map, set up .trail/, wire your agents

That is the whole setup. trail init asks which of your coding agents to wire up, builds the graft map from your code, creates .trail/ for the repo's learnings, and adds a statusline and hooks to Claude Code. It also reads your past Claude Code sessions in the repo, once, for what you told Claude more than once, and saves those as learnings. Your own Claude Code does that reading, in about a minute. Then work the way you always do. No daemon, no server, nothing to babysit: it is all files.

Commit the setup so your teammates get it too. The graft map itself, in graft/, is a local cache like node_modules and is added to .gitignore, so it stays out of the commit:

git add .claude .mcp.json .trail .gitignore .ignore && git commit -m "set up Trail-code"

Teammates' Claude Code reads the learnings in .trail/ even before they install anything, and tells them once how to get the rest.

trail init --dry-run shows the agent wiring it would write; trail init --agents claude wires Claude Code alone.

Coming from graft?

Nothing breaks. The code map you know is now called the graft map, and it is one of Trail-code's two parts.

graft and @nanonets/graft run the same code as trail, and print what they always printed.

Already have graft? graft upgrade adds the trail command next to it.