Radar check
What we found when we opened it. Every bar is one fact, with its date.
Last code pushed 22 Oct 2026, 4 days before this check.
Latest release v0.1.0-rc.14 on 1 Oct 2026.
Public since September 2026, 1 months.
219 stars and 172 forks.
92 commits and 35 new issues in the last 90 days.
2 contributors. 28 pull requests merged in 90 days. A small team carries it.
No security policy file says how to report a problem. No published security advisories.
How the bars are scored
- Alive. Days since the last code push: a week or less 100, a month 90, three months 70, six months 50, a year 30, eighteen months 15, older 0.
- Releases. Age of the latest GitHub release, on a similar scale; no bar when the project publishes no releases.
- Maturity. Age of the project: eight years 100, five 90, three 70, two 55, one 40, six months 25, younger 10.
- Adoption. Stars on a log scale: 100,000 stars is 100, 10,000 is 80, 1,000 is 60, 100 is 40.
- Activity. Commits in the last 90 days: 300 or more 100, 100 85, 30 65, 10 45, any 25.
- Community. People who have contributed: 100 or more 100, 30 80, 10 60, 5 40, 2 25, one 10.
- Security. A published security policy 80, none 40; published advisories are listed, not scored.
Checked by Repo Radar on 26 Oct 2026 from the repository on GitHub.
In simple English
This tool checks how often AI search engines mention your brand. It runs on your own computer or server and stores all data in a local file. You pay the AI providers directly for the searches it performs. It requires your own API keys for the engines you want to check. You can view the results in a web browser or through an assistant connected via MCP.
What it is
An open-source, self-hosted AI visibility tracker written in Go that uses a SQLite database. It queries major AI engines and search assistants to measure brand presence, competitor rankings, and citation sources.
What it is good for
Founders and marketers who want to track how artificial intelligence search engines talk about their product without sending their data to a third-party analytics service.
- Runs as a single binary file with no external database required.
- Includes a built-in server for AI assistants using MCP.
- All tracked answers and metrics stay in your own local SQLite database.
- Requires obtaining and paying for your own provider API keys or scraping accounts.
- Some tracked engines rely on third-party scrapers that may break if the target sites change.
- Pre-release software with active development and potential breaking changes.
manual brand searches in AI chat interfaces and commercial closed-source AI visibility SaaS platforms
Claude Code or Claude Desktop for reading visibility data through the built-in assistant server
- GoogleChrome/lighthouse-ci7.1k stars
Runs the standard page audit on every code change and fails the change if the page got slower.
- harlan-zw/nuxt-seo1.5k stars
Seven search-engine and answer-engine modules for Nuxt sites, installed as one package.
- Swetrix/swetrix1.2k stars
Website analytics with no cookies, plus performance monitoring and error tracking, which you can host yourself.
mkdir -p ~/limelit && cd ~/limelit curl -fsSL https://github.com/limelit-co/open/releases/latest/download/limelit_$(uname -s | tr A-Z a-z)_$(uname -m | sed 's/x86_64/amd64/;s/aarch64/arm64/').tar.gz | tar xz limelit ./limelit serve