Competitor Analysis

Structured side-by-side comparison of competing products. Designed for search + scrape; no interact needed for typical marketing/pricing pages.

When to use

• User names 2+ companies or products: "compare Vercel, Netlify, Cloudflare Pages"
• User names a category only: "best CDNs for edge functions" — search to discover the top 3–5 players, then analyze
• User asks for alternatives: "what are the alternatives to X?"
• User wants a feature matrix or positioning summary

Do NOT use for single-vendor deep-dives — use deep-research or structured-extraction instead.

Strategy

1. Identify competitors.

   • If the user listed them, use that list.
   • Otherwise search once: "top <category> providers 2026" or "<product> alternatives". Pick the 3–5 most-cited.

2. For each competitor, gather three pages:

   • Homepage — one-line positioning, target audience
   • Pricing page (usually /pricing or /plans) — tiers, units, free tier, enterprise gate
   • Features or product page — top 5–10 capabilities, any standout differentiators

3. Fan out when scale warrants.

   • 2–3 competitors: stay in the orchestrator, scrape serially or with parallel tool calls.
   • 4+ competitors: use spawnAgents, one worker per competitor. Each worker gets the 3 URLs above and returns a normalized sub-object.

4. Normalize before formatting.

   • Align pricing tiers by role (Free / Pro / Team / Enterprise) even when vendors name them differently.
   • Call out where a competitor has a capability the others don't.
   • Flag anything missing (e.g. "Enterprise pricing is contact-sales only").

5. Call formatOutput once at the end with the full matrix.

Quick start

await agent.run({
  prompt: 'Compare Vercel, Netlify, and Cloudflare Pages on pricing, edge functions, and free tier generosity',
  skills: ['competitor-analysis'],
  format: 'json',
})

// User gave only a category — discover competitors first
await agent.run({
  prompt: 'Compare the top 4 vector databases for production RAG workloads',
  skills: ['competitor-analysis'],
  format: 'json',
})

Output schema

Every run should produce an object with this shape (add fields as the user's prompt demands):

{
  "category": "Edge hosting platforms",
  "competitors": [
    {
      "name": "Vercel",
      "url": "https://vercel.com",
      "positioning": "Frontend cloud for Next.js and React",
      "pricing": [
        { "tier": "Hobby", "price": 0, "unit": "month", "limits": {} },
        { "tier": "Pro", "price": 20, "unit": "seat/month", "limits": {} }
      ],
      "strengths": [],
      "weaknesses": [],
      "freeTier": true,
      "enterpriseContactOnly": false,
      "sources": []
    }
  ],
  "summary": "One-paragraph takeaway comparing the field.",
  "bestFit": {
    "budgetConscious": "",
    "enterprise": "",
    "developer": ""
  }
}

Tips

• Pricing pages lie by omission. Always look for overages, egress costs, and seat minimums that show up only in a footnote.
• Marketing copy is noise. Prefer the pricing page and docs over the homepage for factual claims.
• If a scrape returns 404 on /pricing, search "<vendor> pricing" before guessing another URL — vendors often move these pages.
• Populate strengths and weaknesses from evidence, not opinion. "Has a built-in KV store (competitor docs do not mention one)" is fair game; "better DX" is not.
• Always include sources: [...] on every competitor object with the URLs you actually scraped.

See also

• deep-research — multi-source validation for a single topic
• pricing-tracker — detail on pricing extraction when that's the only dimension
• structured-extraction — lower-level helper for arbitrary JSON schemas