🧠 URL Intelligence Agent

🔥 Already running inside the HORNO ecosystem — now open source

URL Intelligence Agent is one of the intelligence components used inside the HORNO ecosystem to understand public URLs, normalize entities, enrich profiles and listings, discover public relationships, evaluate web signals and prepare structured information for product workflows.

The complete agent architecture is available here as open source so developers can inspect it, self-host it, extend it and integrate the same evidence-first approach into their own applications and AI systems.

Related MCP server: seo-audit-mcp

⚡ Full Agent or lightweight standalone version?

The two repositories are complementary and can be used independently.

If you need the complete intelligence layer — multi-page crawling, entity resolution, confidence and provenance, monitoring, reports, MCP, HTTP API, domain intelligence, technology detection, security/SEO analysis and optional AI reasoning — use URL Intelligence Agent.

If your application only needs fast, deterministic URL enrichment without the complete agent stack, use the lighter standalone project:

🔗 URL Metadata & Social Profile Fetcher

https://github.com/vpicciuolo/url-metadata-social-fetcher

It is the lightweight standalone option for applications that mainly need to take a public URL and extract reusable metadata such as titles, descriptions, Open Graph data, canonical URLs, images, social links and profile information with safety-focused fetching.

It is especially useful for:

• link previews and URL unfurling

• listing/profile autofill

• directories and marketplaces

• smart links and digital identity pages

• creator/product/project cards

• social-link discovery

• metadata and Open Graph enrichment

• applications that do not need a full autonomous intelligence agent

Simple rule: if you only need to understand and enrich a URL, start with the lightweight repository. If you need to investigate, verify, connect, score, monitor and expose that intelligence to applications or AI agents, use this repository.

Navigation

Quick Start · How it works · Capabilities · CLI · MCP · HTTP API · Docker · SDK · Reports · Monitoring · Configuration · Security · Support

What is URL Intelligence Agent?

A metadata parser can tell you a page title.

A crawler can collect pages.

A language model can summarize text.

URL Intelligence Agent connects those layers into a single evidence-first workflow.

Give it a public URL and it can answer:

What is this URL? What person, company, product, creator or project does it represent? Which public signals support that conclusion? What else is connected to it? What changed? What can an application or AI agent safely do with the result?

It combines deterministic collection, structured extraction, entity resolution, confidence scoring, provenance, crawling, infrastructure intelligence, monitoring and optional AI reasoning.

Core principle

Do not ask an LLM to guess what deterministic public evidence can establish.

AI is an enhancement layer. It is not required for the main intelligence pipeline.

⚡ 5-minute quick start

1. Clone and install

git clone https://github.com/vpicciuolo/url-intelligence-agent.git
cd url-intelligence-agent
npm install
npm run build

2. Investigate a public URL

node dist/src/cli.js investigate https://example.com

Or during development:

npm run dev -- investigate https://example.com

3. Open the interactive console

npm run dev

You will get the full action menu with 39 interactive operations, including investigation, crawling, domain intelligence, social/contact discovery, SEO, security, monitoring, reports, MCP and API modes.

4. Install globally if you want url-agent everywhere

npm install -g github:vpicciuolo/url-intelligence-agent

Then:

url-agent investigate https://example.com
url-agent seo https://example.com
url-agent technologies https://example.com
url-agent mcp

🔄 How the agent works

flowchart LR
    A[Public URL] --> B[URL safety + DNS validation]
    B --> C[robots.txt + sitemap discovery]
    C --> D[Bounded crawl]
    D --> E[Optional JS render fallback]
    D --> F[Metadata + JSON-LD + links]
    E --> F
    F --> G[Entity / social / contact resolution]
    F --> H[Tech / brand / domain intelligence]
    G --> I[Evidence + confidence + provenance]
    H --> I
    I --> J[SEO / security / quality / trust]
    J --> K[Structured intelligence result]
    K --> L[CLI]
    K --> M[HTTP API]
    K --> N[MCP]
    K --> O[Reports / RAG / knowledge export]
    K --> P[Snapshots / monitoring / webhooks]
    K --> Q[Optional AI reasoning]

Every major resolved field can carry:

{
  "value": "Example Inc.",
  "confidence": 0.97,
  "method": "jsonld-name+og-site-name+page-title",
  "sources": [
    "https://example.com/",
    "https://example.com/about"
  ]
}

That provenance model is one of the central differences between this project and a basic scraper or model-only web summary.

🧩 What it can do

The 1.0.0 runtime exposes 34 machine-callable core actions through the action registry. The interactive CLI combines them with reporting, watching, benchmarking, server and plugin operations for 39 menu choices. Plugins can extend the action surface further.

 1. Full URL investigation
 2. Safe URL probe / redirects
 3. Domain + DNS + mail + TLS intelligence
 4. Deep crawl + sitemap mapping
 5. Render JavaScript page / browser fallback
 6. Resolve entity + evidence graph
 7. Find social profiles
 8. Find public contacts + people
 9. Technology fingerprinting
10. Brand intelligence
11. SEO audit
12. Security-header posture audit
13. Quality/accessibility/performance audit
14. Trust/transparency signals
15. Entity relationship graph
16. Competitor/comparison intelligence
17. Structured-data inventory
18. API / OpenAPI / GraphQL discovery
19. Privacy/compliance public signals
20. Team / people extraction
21. Commerce / pricing intelligence
22. Content freshness signals
23. Link graph / external-domain intelligence
24. Broken-link / URL health check
25. Generate marketplace/directory listing
26. RAG-ready document export
27. Knowledge/fact export with provenance
28. Generate Markdown + HTML + JSON reports
29. Compare two URLs
30. Batch enrichment worker
31. Create monitoring snapshot
32. Diff current URL against stored snapshot
33. Continuous scheduled watch + webhook
34. Optional AI evidence reasoning
35. Run reliability benchmark
36. Plugins / extension SDK
37. HTTP API server
38. MCP server
39. About & credits
 0. Exit

💻 CLI command reference

Machine-oriented output:

url-agent investigate https://example.com --raw > result.json

Full usage reference: docs/USAGE.md

🤖 Use it as an MCP server

URL Intelligence Agent includes a native stdio MCP server.

Start MCP

From source:

npm install
npm run build
npm run mcp

Or after global installation:

url-agent mcp

Generic MCP client config

{
  "mcpServers": {
    "url-intelligence-agent": {
      "command": "url-agent",
      "args": ["mcp"],
      "env": {
        "URL_AGENT_MAX_PAGES": "30",
        "URL_AGENT_MAX_DEPTH": "3",
        "URL_AGENT_OBEY_ROBOTS": "true"
      }
    }
  }
}

If the MCP client cannot resolve your shell PATH, use an absolute Node/file path:

{
  "mcpServers": {
    "url-intelligence-agent": {
      "command": "node",
      "args": [
        "/absolute/path/url-intelligence-agent/dist/src/cli.js",
        "mcp"
      ]
    }
  }
}

The server supports:

• MCP initialization

• ping

• tool listing

• tool calls

• resource listing/read

• horno://about project/ecosystem resource

• structured tool results with attribution

The MCP tool registry is generated from the same core action registry used by the API.

Complete setup, tool list, JSON-RPC examples and troubleshooting:
👉 docs/MCP.md

🌐 Run it as an HTTP API

Start locally:

url-agent serve --host 127.0.0.1 --port 8787

Production authentication:

URL_AGENT_API_TOKEN=replace-with-a-long-random-token
URL_AGENT_API_RATE_LIMIT=60

Health:

curl http://127.0.0.1:8787/health

Investigate:

curl \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -G http://127.0.0.1:8787/investigate \
  --data-urlencode "url=https://example.com"

Call any registered action:

curl \
  -X POST \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  http://127.0.0.1:8787/action/audit_seo \
  -d '{"url":"https://example.com"}'

Endpoints:

GET  /health
GET  /actions
GET  /investigate?url=...
POST /investigate
GET  /action/:name
POST /action/:name

Complete API reference:
👉 docs/API.md

🚀 Deploy it

Docker CLI

docker build -t url-intelligence-agent:1.0.0 .

docker run --rm \
  url-intelligence-agent:1.0.0 \
  investigate https://example.com

Docker API

docker volume create url_agent_data

docker run -d \
  --name url-intelligence-agent \
  --restart unless-stopped \
  --env-file .env \
  -p 8787:8787 \
  -v url_agent_data:/app/.url-agent \
  url-intelligence-agent:1.0.0 \
  serve --host 0.0.0.0 --port 8787

Docker Compose

A docker-compose.yml is included.

cp .env.example .env
docker compose up -d --build

Optional Redis service:

docker compose --profile redis up -d --build

Optional PostgreSQL service:

docker compose --profile postgres up -d --build

The deployment guide also includes a Linux systemd service example, Nginx reverse proxy, production authentication, data persistence, renderer setup and deployment verification checklist.

👉 docs/DEPLOYMENT.md

📦 Use it as a TypeScript library

The package exports the core runtime through src/index.ts.

import { investigate, runAction } from "url-intelligence-agent";

const intelligence = await investigate("https://example.com");

console.log(intelligence.entity.name.value);
console.log(intelligence.entity.name.confidence);
console.log(intelligence.entity.name.sources);

const tech = await runAction("detect_technologies", {
  url: "https://example.com"
});

console.log(tech);

Install as a GitHub dependency:

npm install github:vpicciuolo/url-intelligence-agent

or:

{
  "dependencies": {
    "url-intelligence-agent": "github:vpicciuolo/url-intelligence-agent"
  }
}

📄 Reports and exports

Generate all standard report formats:

url-agent report https://example.com --out company-intelligence

Output:

company-intelligence.md
company-intelligence.html
company-intelligence.json

Reports include project attribution and structured evidence from the investigation.

Other export workflows:

url-agent rag https://example.com
url-agent knowledge https://example.com
url-agent listing https://example.com

RAG output is designed around clean, citation-friendly documents and content checksums. Knowledge output is designed around provenance-aware structured facts.

👁️ Monitoring and change detection

Create a snapshot:

url-agent snapshot https://example.com

Compare later:

url-agent diff https://example.com --snapshot snapshot.json

Run continuously:

url-agent watch https://example.com --interval 300000

Configure webhooks:

URL_AGENT_WEBHOOK_URL=https://your-app.example/hooks/url-intelligence
URL_AGENT_WEBHOOK_SECRET=replace-with-a-secret

Monitoring can surface meaningful changes between normalized intelligence snapshots instead of forcing downstream systems to compare raw HTML.

🖥️ JavaScript rendering

Static HTTP collection is the default.

For JavaScript-heavy pages, install Playwright:

npm install playwright
npx playwright install chromium

Enable fallback rendering:

URL_AGENT_RENDER_MODE=auto
URL_AGENT_RENDER_TIMEOUT_MS=30000

Or use a compatible remote renderer:

URL_AGENT_RENDER_MODE=auto
URL_AGENT_RENDER_ENDPOINT=https://renderer.example.com/render
URL_AGENT_RENDER_API_KEY=your-key

Direct render action:

url-agent render https://example.com

Keep rendering disabled when you do not need it; browser automation is significantly heavier than deterministic HTTP collection.

🧠 Optional AI reasoning

The main agent does not require an AI API key.

Configure an OpenAI-compatible endpoint only when reasoning is needed:

AI_BASE_URL=https://api.openai.com/v1
AI_API_KEY=
AI_MODEL=gpt-5-mini
AI_MAX_TOKENS=3000
AI_TEMPERATURE=0.1
AI_TIMEOUT_MS=30000
URL_AGENT_AI_AUTO=false

Explicit reasoning:

url-agent reason https://example.com \
  --instruction "Summarize the entity and uncertainty using only supplied evidence and source URLs."

The reasoning layer is instructed not to invent unsupported facts.

⚙️ Configuration

Start from:

cp .env.example .env

The default settings intentionally bound network and crawl behavior. Increase them only when your workload requires it.

🛡️ Security model

Every submitted URL is treated as untrusted input.

The network layer includes protections such as:

• HTTP/HTTPS-only policy

• embedded credential rejection

• DNS resolution before collection

• localhost blocking

• private/reserved IPv4 blocking

• loopback/ULA/link-local IPv6 blocking

• redirect destination re-validation

• bounded redirects

• bounded response bytes

• timeouts

• explicit User-Agent

The crawler is designed for public web intelligence. It does not intentionally bypass authentication, CAPTCHAs or access controls.

Security/trust scores are public signal summaries — not penetration tests, legal opinions, fraud determinations or guarantees of security.

See SECURITY.md.

🏗️ Architecture

The codebase is intentionally modular:

src/
├── agent.ts        orchestration + action registry
├── net.ts          URL safety / fetching / probing
├── crawler.ts      robots / sitemap / bounded crawling
├── extract.ts      deterministic page extraction
├── render.ts       Playwright / remote render adapters
├── domain.ts       DNS / mail / TLS intelligence
├── analyzers.ts    SEO / security / quality / trust / tech / brand
├── extensions.ts   API / commerce / people / links / knowledge
├── monitor.ts      snapshots / diff / webhook
├── watch.ts        continuous monitoring
├── adapters.ts     cache / persistence / worker adapters
├── ai.ts           optional OpenAI-compatible reasoning
├── plugins.ts      plugin SDK/runtime
├── report.ts       terminal / Markdown / HTML reports
├── server.ts       HTTP API
├── mcp.ts          MCP stdio server
├── benchmark.ts    reliability benchmark runner
└── cli.ts          interactive + command-line experience

More detail: docs/ARCHITECTURE.md

📚 Documentation

🎯 Where this is useful

URL Intelligence Agent is designed for applications such as:

• AI assistants and agent toolchains

• MCP-based research agents

• startup/product directories

• marketplaces

• ranking and attention boards

• creator/influencer platforms

• profile/listing autofill

• CRM/lead enrichment

• public company/project research

• competitive intelligence

• website monitoring

• SEO tooling

• brand intelligence

• digital identity tools

• RAG ingestion

• knowledge pipelines

• public trust/transparency analysis

• broken-link and web quality workflows

The listing-generation and URL enrichment workflows are especially useful when a user pastes a URL and your product needs to turn it into an editable, evidence-backed profile instead of asking the user to fill every field manually.

🧪 Tests and benchmark

Typecheck:

npm run typecheck

Tests:

npm test

Benchmark:

npm run benchmark

The repository includes a benchmark fixture under benchmarks/urls.json for repeatable reliability testing.

🤝 Contributing

Contributions are welcome, particularly around:

• deterministic extraction

• entity resolution

• new evidence signals

• technology fingerprints

• MCP interoperability

• crawler correctness

• security hardening

• plugin actions

• report UX

• benchmark coverage

Please read CONTRIBUTING.md before submitting substantial changes.

💜 Support open-source development

If URL Intelligence Agent saves you engineering time, becomes part of your product, or you simply want to support more open-source tools from this ecosystem, you can support the work here:

Support page: https://hrn.ae/githubsupport

GitHub README files cannot safely execute Stripe JavaScript widgets, so the button links directly to the dedicated Stripe-enabled support page.

Credits

Created by Vincenzo Picciuolo
Founder & Lead Engineer — HRN Innovation Technologies Ltd

Built as part of the technology work behind the HORNO ecosystem and released openly for developers and builders.

• HORNO Network: https://horno.net

• Easy HORNO: https://easy.horno.net

• HORNO Space: https://space.horno.net

• URL Metadata & Social Profile Fetcher: https://github.com/vpicciuolo/url-metadata-social-fetcher

• X: https://x.com/vpicciuolo

• X: https://x.com/hornonetwork

• X: https://x.com/BeHotNow2026

If you use the project, ⭐ star the repository, open an issue with feedback, or show us what you build with it.

License

MIT © 2026 HRN Innovation Technologies Ltd — Vincenzo Picciuolo.