geo-score

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geo-score

geo-score is an open-source AI visibility (GEO) scorer for websites, by Jianrun Tech (见润科技). Formerly geo-watch: geo-watch merged into geo-score in 1.2.0 and lives on as its levels 2 and 3. Website, docs and a free online check: geo-score.dev.

geo-score in 60 seconds

One command, a 0–100 score, the three gates, the gaps worth fixing first, and why every point can be re-checked.

https://github.com/user-attachments/assets/2270314f-446a-4c2d-b27f-11e1f6d66ff8

*English · 60 seconds · Chinese version*

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Can AI engines cite your site? A free 0–100 score from one command. Do they? Check through their APIs with your own keys.

geo-score scores a website from 0 to 100 on whether AI answer engines can reach it, parse it, trust it and cite it, against a published, versioned rubric that anyone can recompute. Then, with your own API keys, it asks the engines the questions your buyers ask and records whom the answers cite. The score is free and needs nothing installed; the citation results are reported beside it and never added to it.

Quick start · Screenshots · What it checks · How accurate is it? · Docs · Website · Changelog · Public leaderboard

Version: the current release is geo-score==1.8.3 on PyPI, with its tag,

wheel and checksums on GitHub Releases; what changed is in the

changelog. Every install line on this page is pinned to that release (scripts/release.py --bump

moves them together), so the tool you run is the one these pages describe. The rubric is versioned separately, and

v1.1 is current. main can run ahead of the release: for CI and client work, use the pinned release.

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❤️ Sponsors

Want to appear here? · Sponsorship never affects the rubric, any score, the public

leaderboard or the technical recommendations in these docs.

Click to collapse

Thanks to CCSub for sponsoring this project! CCSub is an AI API relay platform: one API key for Claude, GPT, Gemini and DeepSeek models, compatible with Claude Code, Codex, Cursor and other coding tools. Register via this link .

Sponsors have not been tested for geo-score's citation measurements (levels 2 and 3). Those need each engine's own search-enabled API, and a relay may not pass web search and citations through unchanged.

What problem it solves

GEO means Generative Engine Optimization — getting cited by ChatGPT, Perplexity,

Google AI Overviews, Gemini and Copilot. Nothing to do with geography or maps.

An answer engine quotes passages from sources it can reach, parse and trust. The reasons a site is left out are often small and invisible from a browser: a robots.txt line that turns a retrieval crawler away, a page whose text only exists after JavaScript runs, figures with no source. geo-score finds which of these a site has and what each one is worth, then checks whether the engines cite it, in three levels:

Score (free, no key) → Ask one question (your API keys) → Watch a question set every week (your API keys).

Three levels, one tool

flowchart LR

S[Your site] --> L1["Level 1 · Score free, no key"] L1 --> R["0–100 readiness 5 pillars · 3 gates · biggest gaps"] Q[Questions your buyers ask] --> L2["Level 2 · Ask one question, your API keys"] Q --> L3["Level 3 · Watch a fixed question set, every week"] L2 --> C["Who the answers cite you · competitors · third-party pages"] L3 --> C R -. reported side by side, never summed .- C

Level 1 is the score. Levels 2 and 3 measure the outcome the rubric keeps out of the score on purpose (two scores, never one): they are reported next to the 100 and never summed into it. A site can score 90 and still lose every answer to a competitor with more third-party coverage, and the reverse also happens, which is why you want both.

Why this is a different question from SEO

Classic SEO asks *where do I rank*. Answer engines don't rank — they retrieve passages, decide whether a source is worth quoting, and cite it. Different question, different failure modes: a site can sit at position 3 on Google and never be quoted. Because engines retrieve passages, this rubric gives Content Citability the most weight. That is a design choice, not a measured effect: the evidence for how much passage shape matters is thin, and the evidence table says where.

Most of what the rubric checks is mechanical and cheap to fix — a robots.txt line, a JSON-LD block, a date in a template, a paragraph rewritten so it stands on its own. The hard part is knowing which of them you are missing, and what each one is worth.

Two guides start here: can ChatGPT read your website?, a check you can do by hand and then with geo-score, and how geo-score differs from a general AI SEO skill.

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Screenshots

Every image shows real output, captured for this page.

Level 1 in a terminal. geo_score.py jianruntech.com (the maintainer's own site), run with geo-score 1.8.3 on 2026-10-17; the output with its colours, rendered to an image. Cut before the observations outside the score.

The GitHub Action's job summary for the same report: the Markdown action/summary.py writes, rendered by GitHub's Markdown API. Cut after the check table.

The public leaderboard , captured 2026-10-17. Its figures come from the 2026-10-02 run with geo-score 1.5.0.

Every site on the leaderboard , sortable, with its band, sector and three biggest gaps by check id. Same page, same capture.

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Core capabilities

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What it checks

21 tiered checks totalling 100 points, plus 4 bonus checks worth up to +6 outside the denominator. Full specification: [rubric/v1.1.md](rubric/v1.1.md) · 简体中文

Content Citabil