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code-review-graph vs Trail-code

Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.

code-review-graph

Open source · MIT

Builds a local map of your codebase so AI coding tools read only the files a change touches.

Full review Website
Trail-code

Open source · MIT

A code map and shared learnings that help coding agents like Claude Code find the right files faster.

Full review Website

The short answer

Choose code-review-graph if you need…

  • Developers who use an MCP-compatible AI coding assistant on a large repository
  • Teams that want code reviews to focus on the files a change affects
  • People who want the code map kept local rather than sent to a hosted service
  • Users of supported tools such as Claude Code, Cursor or Codex who want automatic setup

Choose Trail-code if you need…

  • Developers who use Claude Code, Cursor, Codex or similar agents on a medium or large repository
  • Teams who want agents to remember past decisions about the same code
  • Developers who want fewer tool calls and lower token use from their agent

Side-by-side

code-review-graphTrail-code
TypeDev toolDev tool
PriceFree and open sourceFree and open source
LicenceMITMIT
GitHub stars32.6k11k
Setup difficultyBeginnerBeginner
How you get itInstall with a package managerInstall with a package manager
Main languagePythonTypeScript
Last code update8 days ago4 days ago

What each one is

code-review-graph

code-review-graph is a Python tool that scans a code repository with Tree-sitter and stores a graph of its functions, classes, imports and calls in a local database. AI coding assistants connected to it through MCP can then ask for a small set of relevant files instead of reading the whole project. The README says this is aimed at reducing the context an assistant needs during code reviews and on large repositories.

Trail-code

Trail-code is a command-line tool that builds an index of your codebase, mapping files, functions and which code calls what. It also saves notes on decisions and lessons from your work, so coding agents such as Claude Code, Cursor and Codex start with that context instead of searching from scratch. The README says both parts run on your machine.

Best for

code-review-graph

  • Developers who use an MCP-compatible AI coding assistant on a large repository
  • Teams that want code reviews to focus on the files a change affects
  • People who want the code map kept local rather than sent to a hosted service
  • Users of supported tools such as Claude Code, Cursor or Codex who want automatic setup

Trail-code

  • Developers who use Claude Code, Cursor, Codex or similar agents on a medium or large repository
  • Teams who want agents to remember past decisions about the same code
  • Developers who want fewer tool calls and lower token use from their agent

Strengths

code-review-graph

  • Runs locally and stores the graph in a local database (SQLite, per the README's pipeline diagram)
  • One install command configures many AI coding tools and reports which were detected
  • Incremental updates re-parse only files whose SHA-256 hash changed, per the README

Trail-code

  • Index is built locally with tree-sitter and needs no model or API key, per the README
  • Learnings are saved as readable files in the repo
  • Maintainers report benchmark results with stated run counts, such as 162 runs and 50 SWE-bench issues

Not ideal for

code-review-graph

  • Projects in languages outside the supported list, since parsing coverage is limited to the listed languages
  • Users who do not use an MCP-compatible AI tool, since the main interface is through MCP
  • Anyone expecting the token savings to hold exactly, since the README's numbers come from one 2026-08-02 snapshot and it says the repository has grown since

Trail-code

  • People who do not use coding agents, since the main value is in agent integration
  • Projects that need a fully specified, audited benchmark: the performance figures come from the maintainers' own tests and are not independently verified in the README
  • Users on older Node versions, because the README requires Node 22.3 or newer

Trade-offs

code-review-graph

  • The token-reduction figures are from a single snapshot and the README says current numbers are larger
  • Parsing is limited to the listed languages, with fallbacks where no Tree-sitter grammar exists
  • Requires Python 3.10 or newer and depends on MCP-compatible tools to be useful

Trail-code

  • Performance claims come from the project's own benchmarks and are not independently checked here
  • Setup wires hooks and a statusline into Claude Code, which some users may not want by default
  • Learnings depend on the agent saving them, so quality depends on the agent and the workflow

Getting started

code-review-graph

Install from PyPI

pip install code-review-graph

Configure detected AI coding tools

code-review-graph install
Step-by-step guide: how to use code-review-graph

Trail-code

Install the CLI globally

npm install -g @trailhq/trail-code

Build the map and set up the repo

trail init
Step-by-step guide: how to use Trail-code

Frequently asked questions

What is the difference between code-review-graph and Trail-code?

code-review-graph: Builds a local map of your codebase so AI coding tools read only the files a change touches. Trail-code: A code map and shared learnings that help coding agents like Claude Code find the right files faster. code-review-graph suits developers who use an mcp-compatible ai coding assistant on a large repository, while Trail-code suits developers who use claude code, cursor, codex or similar agents on a medium or large repository.

Are code-review-graph and Trail-code free?

code-review-graph: Free and open source. Trail-code: Free and open source.

Is code-review-graph or Trail-code more popular?

code-review-graph has more GitHub stars (32.6k) than Trail-code (11k). Popularity isn't the same as fit — check which one matches your use case above.

What are other alternatives to code-review-graph and Trail-code?

Other options people compare them with include codebase-memory-mcp, ECC, wshobson/agents, claude-context, CodeBurn.

Keep comparing

code-review-graph alternativesTrail-code alternativescode-review-graph vs codebase-memory-mcpcode-review-graph vs ECCclaude-context vs code-review-graphcode-review-graph vs CodeBurnECC vs Trail-codeAgentic Awesome Skills vs Trail-codecodebase-memory-mcp vs Trail-codebook-to-skill vs Trail-codeGitHub MCP Server vs Trail-codeSkill Seekers vs Trail-code

Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.