Your site ranks on Google. Does ChatGPT cite it? The open-source Answer Engine Optimization (AEO) & Generative Engine Optimization (GEO) toolkit. GEO Optimizer is the open-source engine behind GeoReady, the hosted AI visibility audit at geoready.dev. Run it here from the terminal, or in the browser on geoready.dev. One command scores any website 0–100 on whether ChatGPT, Perplexity, Gemini, Claude, and Google AI…
GEO Optimizer is the open-source engine behind GeoReady, the hosted AI visibility audit at geoready.dev. Run it here from the terminal, or in the browser on geoready.dev.
One command scores any website 0–100 on whether ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews can crawl it, understand it, and cite it — then tells you exactly what to fix.
No install, no account, no API key. Runs in seconds.
No terminal? Paste your URL into the browser version instead. Same engine, free, no account:
The browser audit on geoready.dev: paste a URL, read the score, the nine category results and the free fixes. Live run on geoready.dev itself, 17 October 2026.
GEO Optimizer measures how visible a website is to AI answer engines — ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude — scores it 0–100, and generates the fixes. It is free, MIT-licensed, and runs as a CLI, a Python library, an MCP server, a GitHub Action, or an Astro integration.
Also called GEO, AI SEO, or LLM SEO — same problem: an answer engine gives one response and cites a handful of sources. Ranking on Google does not make you one of them. Being reachable, parseable, and quotable does.
Two runs of geo audit, September 2026. example.com has no robots.txt, no llms.txt, and no structured data. geoready.dev is built with this engine’s own Astro integration. Excerpts, condensed from the full text output.
example.com
geoready.dev
[█░░░░░░░░░░░░░░░░░░░] 8/100
❌ CRITICAL — Site is not visible
to AI search engines
📋 PRIORITY NEXT STEPS:
1. Create robots.txt with Allow rules
for AI bots (GPTBot, ClaudeBot,
PerplexityBot)
2. Create /llms.txt for AI indexing
3. Add WebSite JSON-LD schema
4. Add Organization JSON-LD schema
5. Add FAQPage schema with site FAQs
[██████████████████░░] 91/100
🏆 EXCELLENT
✅ 27/27 AI crawlers allowed
✅ llms.txt found (~3554 words)
✅ 7 schema types
✅ 0 prompt-injection patterns
✅ 1/4 Knowledge Graph pillars
⚠️ No dateModified signal
(Wikipedia, Wikidata, LinkedIn,
Crunchbase)
Across 1,800+ sites audited through GeoReady (latest audit per domain, as of 17 October 2026), the median score is 59/100, 58% publish an llms.txt and 76% ship JSON-LD structured data. 58% of them land in the Foundation band (36–67), 28% in Good, 12% in Critical and 2% in Excellent. Scores mix rubric v1 (engine 4.13 to 4.18) and v2 (4.19). The sample is self-selected (people who chose to audit their site), so treat it as directional, not a census of the web.
Where does your site land?Run the free audit and compare your score with the median, or run geo audit above.
Then it writes the fixes
geo fix turns the audit into files: robots.txt rules for all 27 AI bots, llms.txt, JSON-LD schema, AI discovery endpoints, and meta tags. It previews by default and writes only with --apply. The score after fixes is the tool’s own estimate, not a measured result.
Prefer no terminal? Run the same audit free at geoready.dev — no account needed.
Quick Start
# Zero install
uvx --from geo-optimizer-skill geo audit --url https://yoursite.com
# Or install it
pip install -U geo-optimizer-skill
geo audit --url https://yoursite.com # score 0–100 + prioritized fixes
geo fix --url https://yoursite.com # preview robots.txt, llms.txt, schema, meta (--apply to write)
geo audit --url https://yoursite.com --threshold 70 # exit 1 below 70 — use it as a CI gate
geo citations --brand "Acme" --domain acme.com # does AI cite you? (bring your own API key)
All 16 commands
# Audit a full sitemap and surface the weakest pages first
geo audit --sitemap https://yoursite.com/sitemap.xml --max-urls 25
# Compare before/after versions of a page
geo diff --before https://yoursite.com/page-old --after https://yoursite.com/page-new
# Save history, detect regressions, and show the trend
geo audit --url https://yoursite.com --save-history --regression
geo history --url https://yoursite.com
# What changed since the last snapshot? (CI: --fail-on warning)
geo drift --url https://yoursite.com
# Passive AI visibility snapshot for a domain
geo monitor --domain yoursite.com
# Ask real AI engines whether your brand is mentioned and your domain cited.
# AI answers vary run to run — sample each query 5x for a confidence interval.
geo citations --brand "YourBrand" --domain yoursite.com --topic "your product category" --runs 5
# Save or query archived AI answer snapshots, and score citation quality in one
geo snapshots --query "best GEO tool" --from 2026-03-01 --to 2026-03-30
geo snapshots --quality --snapshot-id 12 --target-domain yoursite.com
# Recurring monitoring with an HTML trend report
geo track --url https://yoursite.com --report --output ./geo-track-report.html
# Generate llms.txt from the sitemap, or check an existing one for stale URLs (exit 1)
geo llms --base-url https://yoursite.com --output ./public/llms.txt
geo llms --base-url https://yoursite.com --check-drift
# Generate or analyze JSON-LD schema
geo schema --type website --name "Your Site" --url https://yoursite.com
geo schema --file index.html --analyze
# Site-level and diagnostic tools
geo coherence --sitemap https://yoursite.com/sitemap.xml # cross-page terminology consistency
geo authority --sitemap https://yoursite.com/sitemap.xml # topical authority: clusters, pillars, depth
geo logs --file access.log # AI crawler activity from server logs
geo access --url https://yoursite.com # browser vs AI-bot access simulation
geo perception --url https://yoursite.com # what an AI would extract from the page
Guides for most commands live in docs/. Provider setup for geo citations (Perplexity, OpenAI, Anthropic, Groq, Gemini, MiniMax, DeepSeek, and SerpBase for the real Google SERP + AI Overview): docs/llm-providers.md.
After the one-shot audit
geo audit is a snapshot. It can’t tell you that your score dropped after last Tuesday’s deploy, or whether citations started appearing once your fixes shipped. geo history, geo drift, and geo track help — but only when someone remembers to run them.
The hosted platform runs the same engine on a schedule, so the watching happens without you:
Free plan — audits, your last 5 reports, and one real AI answer check a week (Perplexity).
Pro / Agency, from $19/mo — weekly score monitoring, regression alerts, multi-site portfolios, client reporting. Pricing.
geo citations sends the questions your customers ask to real answer engines, then reports whether your brand is mentioned, whether your domain is cited as a source, and which competitors are cited instead.
Two honest caveats, both built into the tool:
Answers are not deterministic. The same question can cite different sources on each run. --runs 5 samples every query five times and reports a confidence interval instead of a single yes/no.
Not every API shows sources. Perplexity Sonar returns the real source URLs. OpenAI, Anthropic, and Groq only reveal what the model already knows about your brand. Google AI Overviews is a SERP feature, not a model, so --provider serpbase observes the real Google results page instead.
What it checks
Area
Points
What GEO Optimizer looks for
Google AI readiness
/20
Indexing, snippets, canonical, dates, byline, links, viewport, sitemap, and manual generative-AI control
Robots.txt
/14
Googlebot and citation-bot access; found 3 + citation bots allowed 11
Schema JSON-LD
/14
Validity, richness, FAQ, Article, Organization, WebSite, and visible name match
Content
/14
H1, statistics, links, word count, heading hierarchy, lists/tables, front-loading, image alt coverage
Title, description, and Open Graph; canonical is scored by G-CANONICAL
llms.txt
/6
Found, sections, links, and llms-full.txt; useful to other AI agents, ignored by Google Search
Signals
/6
<html lang>, RSS/Atom feed, valid freshness
AI Discovery
/3
ai.txt, summary.json, and markdown negotiation
Score bands: 86–100 Excellent · 68–85 Good · 36–67 Foundation · 0–35 Critical. Rubric v2 is the default; use geo audit --score-version 1 for legacy v1 comparisons. Every weight is in models/config.py. Full rubric: docs/scoring-rubric.md.
Beyond the score: 10 more checks
Negative Signals can deduct up to 5 points. The other checks are informational and do not change the score.
Plus a separate Citability Score (0–100) built from 47 content checks, 9 of them taken directly from the methods tested in the KDD 2024 GEO paper. Method list: docs/geo-methods.md.
Topic authority — answer engines map entities across a site, not single pages. geo authority clusters your pages by topic and scores depth, interlinking, and pillar pages:
Show your GEO score
A live badge for your README — like a coverage badge, for AI visibility:
Re-audited and cached hourly. No account needed. Colors: 86–100 green · 68–85 cyan · 36–67 yellow · 0–35 red.
Integrations
CI/CD — fail the build when AI-readiness drops
Treat AI visibility like test coverage. The GitHub Action scores your site, fails the build below a threshold, and uploads findings to the Security tab:
# .github/workflows/geo.yml
- uses: Auriti-Labs/[email protected]
with:
url: https://yoursite.com
min-score: 70 # fail the build below 70
format: sarif # findings in the GitHub Security tab
Any CI that runs Python works (GitLab, Jenkins, CircleCI). For longitudinal checks: geo drift --url … --fail-on warning. Output formats: text, json, rich, html, pdf, sarif, junit, github. The JSON format is the stable integration contract — see docs/json-contract.md.
--format html writes a self-contained report you can hand to a client. This one is the real report for geoready.dev:
MCP Server
Use GEO Optimizer from Claude, Cursor, Windsurf, or any MCP client:
pip install "geo-optimizer-skill[mcp]"
claude mcp add geo-optimizer -- geo-mcp
Then ask: “audit my site and fix what’s missing.”
Claude Code plugin
Installs the skill and the MCP server in one go, no files to copy:
On Claude Code 2.1.275 or later one step is enough: /plugin install geo-optimizer --marketplace Auriti-Labs/geo-optimizer-skill (the shell form, claude plugin install ..., needs 2.1.292). The plugin starts geo-mcp with uvx, so uv must be on your PATH. It does not install the geo CLI the skill runs: the skill’s preflight asks for it.
12 MCP tools
Tool
Purpose
geo_audit
Full audit with score + recommendations
geo_fix
Generate fix files
geo_llms_generate
Generate llms.txt
geo_citability
Content citability analysis (47 checks)
geo_schema_validate
Validate JSON-LD
geo_compare
Compare multiple sites
geo_gap_analysis
Explain the gap between two sites and prioritize fixes
geo_ai_discovery
Check AI discovery endpoints
geo_check_bots
Check bot access via robots.txt
geo_trust_score
5-layer trust signal aggregation
geo_negative_signals
8 anti-citation signal detection
geo_factual_accuracy
Audit unsourced claims, contradictions, and broken citations
Python API
from geo_optimizer import audit # or audit_async
result = audit("https://example.com")
result.score # int, 0–100
result.band # "critical" | "foundation" | "good" | "excellent"
result.citability.total_score # int, 0–100
result.score_breakdown # {"robots": ..., "llms": ..., ...}
result.recommendations # list of prioritized fixes
Astro Integration
Generates llms.txt, /.well-known/ai.txt, and /ai/summary.json from your built routes at build time, and never overwrites hand-curated files. geoready.dev builds with it. Not yet published to npm — copy integrations/astro-geoready/ to integrations/astro-geoready/ in your project root:
9 methods, evaluated on GEO-bench (10,000 queries). The best (citing sources, adding quotations, adding statistics) raised source visibility by up to 40%. Gains were largest for lower-ranked sources.
Most content-rewriting tricks do not work. Ranking the source matters more than rewriting it.
That finding is consistent with how the tool is weighted: GEO Optimizer puts most of its weight on whether crawlers can reach and parse a page (robots.txt, llms.txt, schema, meta) and treats content rewriting as the smaller lever. It measures readiness signals; it cannot guarantee that any engine will cite you, and it says so.
The full reasoning behind each signal and weight is written up in the book below.
The book behind the engine: AI Search Engineering
AI Search Engineering — A Technical Guide to Generative Engine Optimization (GEO), AI Citations, and Agent-Ready Websites
Juan Camilo Auriti · 992 pages · English · 10 September 2026 · ISBN 979-8172934568
The book explains the reasoning; this engine runs it. The scoring weights, the eight signal categories, and the evidence classification described in print are rubric v1, still implemented in this repository and available with --score-version 1. Rubric v2, the default, adds a Google AI readiness category built on Google’s own documentation.
It treats GEO as an engineering discipline rather than a collection of tricks, organized around the path a page travels before it can be cited. Each stage maps to something you can run here:
It covers how answer engines discover and use web content — keeping documented behaviour separate from research findings and inference — auditing crawler access, rendering, and entity signals, engineering content for extraction without “AI-friendly” formulas, and operational GEO workflows for teams and agencies. It is written for developers, technical SEOs, consultants, agencies, product teams, and site owners who want to know what can actually be measured, implemented, and defended in AI search — no guarantees, no invented ranking factors, no hype presented as fact. Full stage-by-stage map of the book against the engine:
GEO Optimizer scores input-side readiness — the signals that make a site reachable, parseable, and citable. Outside the lightweight geo citations check, it does not observe what engines say about a brand, and it does not cover Chinese answer engines such as Doubao, Qwen, or Yuanbao (DeepSeek is available as a geo citations provider).
OpenGEO is a complementary open standard focused on output-side observation: whether engines actually mention or cite a brand, with a published interchange format. The split — readiness vs. results — is deliberate; see #534.
FAQ
What is Answer Engine Optimization (AEO)?
AEO is the practice of structuring a website so AI answer engines — ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude — can find, understand, and cite it as the direct answer to a user’s question. It is also called Generative Engine Optimization (GEO), AI SEO, LLM SEO, or AI Search Optimization.
How do I check if ChatGPT or Perplexity cites my website?
Run geo citations --brand "Your Brand" --domain yoursite.com with a Perplexity or OpenAI API key. It asks the engine customer-style questions and reports whether your brand is mentioned, whether your domain is cited as a source, and which competitors are cited instead. Perplexity returns real source URLs; OpenAI and Anthropic reveal what the model knows about your brand. No account needed for the free web version.
How do I improve my site’s GEO score?
Run geo audit --url https://yoursite.com and read the priority list, then geo fix --url https://yoursite.com to preview the robots.txt rules, llms.txt, JSON-LD schema and meta tags it would write (--apply writes them), and audit again. Start where the audit shows the most missing points: among the audited sites, the largest readiness gap is AI discovery files, and the median score is 59/100 (17 October 2026). To see your own list without installing anything, run the free web audit, or read the free 160-page GEO Readiness Manual.
Does GEO Optimizer guarantee that AI engines will cite my site?
No. It measures the signals that make a site reachable, parseable, and quotable, and it tells you which ones are missing. Whether an engine cites you also depends on the question, the competition, and the engine itself — and answers change from run to run, which is why geo citations has --runs.
Is this an llms.txt generator and checker?
Yes. geo llms generates an llms.txt from your sitemap, --check-drift flags stale URLs, the audit scores its presence and depth, and the Astro integration creates one at build time. There’s also a free online llms.txt generator.
What is llms.txt, and does it improve AI citations?llms.txt is a Markdown file at the root of a site (/llms.txt) that gives language models a curated index of its most important pages. It is an organizational signal, not a proven ranking factor: no engine has documented that it improves citations. GEO Optimizer scores it because it is cheap to add and is intended to help AI systems read a site’s structure.
Is it free and open source?
Yes — MIT licensed, free forever via pip install geo-optimizer-skill or uvx. The hosted GeoReady platform runs the same engine and adds monitoring, score history, and team features.
Which AI engines does it cover?
27 AI crawlers, including ChatGPT (GPTBot, OAI-SearchBot), Perplexity (PerplexityBot), Claude (ClaudeBot, Claude-SearchBot), and Google (Googlebot, Google-Extended), plus per-platform readiness profiles for ChatGPT, Perplexity, and Google AI Overviews.
Is it free for commercial use?
Yes. The MIT license permits commercial use, modification, and redistribution with no fee and no attribution beyond the license notice. Agencies can run client audits and CI gates with the CLI without any paid plan. The hosted platform is optional and separate.
Does the CLI send my data anywhere?
No. The CLI fetches only the URL you audit and any AI provider you configure with your own API key (geo citations). There is no telemetry, no phone-home, and no account requirement — the audit runs entirely on your machine.
How long does an audit take?
A single-URL audit runs in a few seconds. A sitemap batch depends on size and the --max-urls cap (default 25). The full test suite of 2,000+ tests runs in CI on every push across Python 3.10–3.14.
Can I use it in CI/CD?
Yes. The GitHub Action fails the build below a score threshold and can emit SARIF findings for the GitHub Security tab. Any CI that runs Python works (GitLab, Jenkins, CircleCI), and geo drift --fail-on warning catches regressions between deploys.
What is the difference between GEO and AEO?
Nothing in practice — they are two names for the same discipline. AEO (Answer Engine Optimization) emphasizes the answer engine as the target; GEO (Generative Engine Optimization) is the term used in the peer-reviewed research (KDD 2024). This tool uses both terms because its audience searches for both.
How is it different from a traditional SEO tool?
Traditional SEO tools optimize for Google rankings and backlinks. GEO Optimizer measures AI citation readiness — whether answer engines can crawl, parse, and quote your content — using signals (llms.txt, AI-bot access, schema richness, citability, topical authority) that classic SEO tools don’t score. Standard technical SEO checks (meta tags, canonical URLs, Open Graph) are audited alongside them.
What research is GEO Optimizer based on?
The GEO: Generative Engine Optimization paper (KDD 2024), the C-SEO Bench benchmark, the AutoGEO preprint (arXiv 2025), plus industry studies for the remaining content checks. Every check lists its source in docs/geo-methods.md.
Is there a book on Generative Engine Optimization?
Yes. AI Search Engineering: A Technical Guide to Generative Engine Optimization (GEO), AI Citations, and Agent-Ready Websites is a 992-page book by Juan Camilo Auriti, the author of GEO Optimizer, published 10 September 2026 (ISBN 979-8172934568). The scoring model it describes is the one implemented in this engine. See the book.
Roadmap
Focused release waves, not noisy patches: 96 releases since February 2026, currently v4.19.10. Next up is the v5.0 cycle — signal architecture, retrieval surface analysis, and scoring recalibration. Calendar and direction: docs/ROADMAP.md.
Security: every URL input is validated against private IP ranges (RFC 1918, loopback, link-local, cloud metadata) with DNS pinning before any request. Report vulnerabilities via SECURITY.md.
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