Zenrows
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Solution · AI Agents

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

    Where agents actually break

    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.

    01 · Research

    Synthesize from live sources.

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

    → clean markdown, source kept for citation.

    02 · RAG ingestion

    Fresh pages. LLM-ready.

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

    → clean markdown, chunked and vector-store ready.

    03 · Company enrichment

    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.)

    04 · Market monitoring

    Check it. Compare it yourself.

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

    → structured fields, ready to compare run over run.

    05 · Competitive intel

    Surface what you're tracking.

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

    → pricing, hiring, velocity, ready to compare.

    06 · Catalog enrichment

    Catalog at scale.

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

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

    07 · Job data

    Postings. Trends. Signals.

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

    → structured rows across every board.

    08 · Browser workflows

    Click. Login. Navigate.

    zenrows.browser.session(url)

    → persistent browser, multi-step state.

    One layer, every surface

    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"}'

    Past the threshold

    Cross it. Through one call.