Batch: send a list of URLs, collect every resultExplore Batch
Zenrows
Talk to sales Start free

Give your AI agents reliable access to the live web.

Give your AI workflows trusted answers from the live web.

Zenrows gives agents the tools to fetch clean context, extract structured data, and operate dynamic websites, including protected pages that normal tools cannot access.

your agent · live trace idle

    Agents cannot reason with data they cannot reach.

    without zenrows
    1. × 403
    2. × 200 (empty)
    3. × captcha
    4. × retry
    5. × retry
    6. × give up

    4,200 tokens. no data.

    with zenrows
    1. zenrows.fetch(url)
    2. ✓ 120ms

    8 tokens. clean markdown.

    Synthesize from live sources.

    zenrows.fetch(url, { format: 'markdown' })

    → clean markdown, source kept for citation.

    Fresh pages. LLM-ready.

    zenrows.fetch(url, { format: 'markdown', js_render: true })

    → clean markdown, chunked and vector-store ready.

    Domain in. Profile out.

    zenrows.fetch(domain, { autoparse: true })

    → hiring, stack, pricing, exec bios, wherever the page has them. (autoparse is Extract's parameter name.)

    Check it. Compare it yourself.

    zenrows.fetch(url, { autoparse: true })

    → structured fields, ready to compare run over run.

    Surface what you're tracking.

    zenrows.fetch(url, { autoparse: true })

    → pricing, hiring, velocity, ready to compare.

    Catalog at scale.

    zenrows.fetch(url, { autoparse: true })

    → full product record, every field, ready to index.

    Postings. Trends. Signals.

    zenrows.fetch(board, { autoparse: true })

    → structured rows across every board.

    Click. Login. Navigate.

    zenrows.browser.session(url)

    → persistent browser, multi-step state.

    Same primitives, every framework.

    Zenrows is the web data infrastructure layer. Consume it from MCP (open standard, runs everywhere), Vercel AI SDK, LangChain, or the raw API, typed schemas, streaming, structured outputs across all of them.

    ~/agent · mcp.json
    // Add Zenrows as an MCP server in any client (Claude Code,
    // Cursor, VS Code, Vercel AI SDK, OpenAI Agents, ...).
    {
      "mcpServers": {
        "zenrows": {
          "command": "npx",
          "args": ["-y", "@zenrows/mcp"],
          "env": { "ZENROWS_API_KEY": "sk_..." }
        }
      }
    }
    import { streamText } from 'ai';
    import { anthropic } from '@ai-sdk/anthropic';
    import { fetch, extract } from '@zenrows/ai-sdk';
    
    const result = streamText({
      model: anthropic('claude-opus-4-7'),
      tools: { fetch, extract },
      prompt: userTask,
    });
    from langchain.agents import create_agent
    from zenrows_langchain import fetch, extract
    
    agent = create_agent(
        model="anthropic:claude-opus-4-7",
        tools=[fetch, extract],
    )
    result = agent.invoke({"input": user_task})
    # Any language, any runtime — typed API.
    curl https://api.zenrows.com/v1/fetch \
      -H "Authorization: Bearer $ZENROWS_API_KEY" \
      -d '{"url":"https://example.com","format":"markdown"}'

    Cross it. Through one call.