OpenAgent Registry

The open registry, scanner, and leaderboard for agent-ready websites and services.

OpenAgent Registry (OAR) does three things in one place:

1. Scans any URL for the full stack of agent-access signals - llms.txt, agents.txt / agents.json, AGENTS.md, .well-known/agent-card.json, robots.txt + Content Signals, OpenAPI, OAuth discovery, RSL licensing, MCP/A2A registry presence - and aggregates upstream registries instead of re-doing their work.
2. Scores each site against a transparent, public rubric and ranks it within its industry category.
3. Tells you what to do about it - a concrete approval-path checklist with deep links to the actual developer applications, OAuth signups, partner programs, and standards-adoption steps. Lead with the checklist; the score is supporting evidence.

Sources of authoritative scoring credit (registries / standards bodies):

• Official MCP Registry - the MCP working group's catalog of MCP servers (+25 if your domain ships one)
• Standards: llms.txt, agents.txt / agents.json, A2A Protocol, Web Bot Auth, RSL

Community resources used for domain discovery only (no score credit; we use these to know which sites to scan):

• thedaviddias/llms-txt-hub - one maintainer's community list of sites publishing llms.txt
• Agent Friendly Directory - sklivvz's curated agent-usable services
• A2A Registry - community directory of A2A agents
• LLMTEXT - llms.txt validation toolkit

What OAR adds that none of the above does today:

• Comparative leaderboards by industry category (rentals, shopping, content, social, developer)
• Approval-path generator - concrete next steps, not just "you have OAuth"
• Aggregation across the full stack in one report
• Change monitoring over time
• A public, free API so agents (including your own) can query it before calling external services

Status

Pre-alpha but live. Public deploy: https://openagent-registry.vercel.app.

After the v0.1 import, the registry tracks 1,400+ services across 6 industry categories, sourced from the original seed plus an import of llms-txt-hub. Every service has a live score, a label, and a generated approval-path checklist. Looking for contributors - especially for approval-path data per service (the moat).

Stack

• Next.js 15 (App Router) + TypeScript + Tailwind CSS
• No backend service in v0 - Next.js API routes do the scanning
• Local JSON cache for MVP; Postgres or Vercel KV for production scale
• Deploys natively to Vercel

Quickstart

pnpm install
pnpm dev

Then open http://localhost:3000.

How scoring works

See docs/rubric.md. Every signal is weighted, every weight is published, every score is reproducible from the JSON profile. No black-box scoring.

The current rubric (summary):

Labels:

Categories

• Rentals & Travel - Airbnb, Booking, Hertz, Avis, Enterprise, Expedia, Vrbo, AutoRentals
• Shopping & Commerce - Amazon, Shopify, Stripe, Target, Walmart, BestBuy, Etsy
• Content & News - NYT, WaPo, Bloomberg, Reuters, Substack, Medium
• Social & Community - Reddit, X, LinkedIn, Bluesky, Mastodon, Discord
• Developer Platforms - GitHub, Vercel, Cloudflare, npm, AWS, plus 800+ dev tools from the upstream import
• AI & ML Platforms - Anthropic, OpenAI, and 500+ AI/ML platforms publishing llms.txt

Within each category, services are ranked by their AgentRank score. The category page is the SEO-friendly editorial surface: "Top agent-ready rental sites, [month] [year]".

Contributing

See CONTRIBUTING.md. The three highest-leverage contributions today:

1. Add services to data/seed.json (a category, a homepage URL, optional notes) - opens a PR; CI runs a scan against it.
2. Tighten parsers for the well-known files. The MVP parsers are intentionally conservative - better parsers raise the data quality for everyone.
3. Approval-path data - concrete next-step instructions per service (developer signup URL, OAuth scopes needed, partner-program form). This is the killer feature; quality is the moat.

License

Apache 2.0.