Build a browser agent with Zenrows

Give a model the Zenrows browser tools and it becomes a browser agent: it opens a page in Zenrows Browser, the browser for agents, decides each next step from what it sees, and clicks, types and reads until the task is done. The Anthropic and OpenAI APIs call the Zenrows MCP server for you, so the whole agent is one API request, with no framework and no browser to run. Uses: Browser through the Zenrows MCP server and its browser_* tools

Ask your AI agent

With the Zenrows MCP server connected to Claude, Cursor or any MCP client, paste this prompt. Your agent picks the right Zenrows tools and settings on its own.

Use the Zenrows browser to log in at https://www.scrapingcourse.com/login with
admin@example.com and password, and tell me the first three products on the page
you land on. Close the browser session when you're done.

How it works

You send the task to the model with the Zenrows MCP server attached as a tool source.
The model calls browser_navigate, which opens a cloud browser session and returns its session_id.
The model reads the page and acts on it with the other browser_* tools: browser_fill, browser_click, browser_press_key, browser_wait_for_selector, browser_get_text, browser_query_selector_all, browser_screenshot and more.
It repeats step 3 until it has the answer, then calls browser_close and replies.

The LLM provider runs this loop on its side and calls https://mcp.zenrows.com/mcp with your Zenrows API key. Your code sends one request and reads one response.

Build the agent

Set ANTHROPIC_API_KEY or OPENAI_API_KEY, and replace YOUR_ZENROWS_API_KEY with your key. The tool list limits the agent to the browser tools, so it browses instead of scraping.

import anthropic

ZENROWS_API_KEY = "YOUR_ZENROWS_API_KEY"
BROWSER_TOOLS = [
    "browser_navigate", "browser_click", "browser_fill", "browser_press_key",
    "browser_wait_for_selector", "browser_get_text", "browser_query_selector_all",
    "browser_screenshot", "browser_close",
]

client = anthropic.Anthropic()  # reads ANTHROPIC_API_KEY

response = client.beta.messages.create(
    model="claude-opus-5-5",
    max_tokens=16000,
    betas=["mcp-client-2025-11-20", "server-side-fallback-2026-07-01"],
    fallbacks="default",
    output_config={"effort": "high"},
    system=(
        "You control a cloud browser through the Zenrows tools. Open pages with "
        "browser_navigate, act with the other browser tools, and call browser_close "
        "on every session you open before you answer."
    ),
    mcp_servers=[
        {
            "type": "url",
            "url": "https://mcp.zenrows.com/mcp",
            "name": "zenrows",
            "authorization_token": ZENROWS_API_KEY,
        }
    ],
    tools=[
        {
            "type": "mcp_toolset",
            "mcp_server_name": "zenrows",
            "default_config": {"enabled": False},
            "configs": {name: {"enabled": True} for name in BROWSER_TOOLS},
        }
    ],
    messages=[
        {
            "role": "user",
            "content": "Log in at https://www.scrapingcourse.com/login with admin@example.com "
                       "and password, and tell me the first three products on the page you land on.",
        }
    ],
)

for block in response.content:
    if block.type == "mcp_tool_use":
        print("step:", block.name, block.input)
    elif block.type == "text":
        print(block.text)

import Anthropic from "@anthropic-ai/sdk";

const ZENROWS_API_KEY = "YOUR_ZENROWS_API_KEY";
const BROWSER_TOOLS = [
  "browser_navigate", "browser_click", "browser_fill", "browser_press_key",
  "browser_wait_for_selector", "browser_get_text", "browser_query_selector_all",
  "browser_screenshot", "browser_close",
];

const client = new Anthropic(); // reads ANTHROPIC_API_KEY

const response = await client.beta.messages.create({
  model: "claude-opus-5-5",
  max_tokens: 16000,
  betas: ["mcp-client-2025-11-20", "server-side-fallback-2026-07-01"],
  fallbacks: "default",
  output_config: { effort: "high" },
  system:
    "You control a cloud browser through the Zenrows tools. Open pages with " +
    "browser_navigate, act with the other browser tools, and call browser_close " +
    "on every session you open before you answer.",
  mcp_servers: [
    {
      type: "url",
      url: "https://mcp.zenrows.com/mcp",
      name: "zenrows",
      authorization_token: ZENROWS_API_KEY,
    },
  ],
  tools: [
    {
      type: "mcp_toolset",
      mcp_server_name: "zenrows",
      default_config: { enabled: false },
      configs: Object.fromEntries(BROWSER_TOOLS.map((name) => [name, { enabled: true }])),
    },
  ],
  messages: [
    {
      role: "user",
      content:
        "Log in at https://www.scrapingcourse.com/login with admin@example.com " +
        "and password, and tell me the first three products on the page you land on.",
    },
  ],
});

for (const block of response.content) {
  if (block.type === "mcp_tool_use") console.log("step:", block.name, block.input);
  else if (block.type === "text") console.log(block.text);
}

from openai import OpenAI

ZENROWS_API_KEY = "YOUR_ZENROWS_API_KEY"
BROWSER_TOOLS = [
    "browser_navigate", "browser_click", "browser_fill", "browser_press_key",
    "browser_wait_for_selector", "browser_get_text", "browser_query_selector_all",
    "browser_screenshot", "browser_close",
]

client = OpenAI()  # reads OPENAI_API_KEY

response = client.responses.create(
    model="gpt-5",
    instructions=(
        "You control a cloud browser through the Zenrows tools. Open pages with "
        "browser_navigate, act with the other browser tools, and call browser_close "
        "on every session you open before you answer."
    ),
    tools=[
        {
            "type": "mcp",
            "server_label": "zenrows",
            "server_url": "https://mcp.zenrows.com/mcp",
            "authorization": ZENROWS_API_KEY,
            "allowed_tools": BROWSER_TOOLS,
            "require_approval": "never",
        }
    ],
    input="Log in at https://www.scrapingcourse.com/login with admin@example.com "
          "and password, and tell me the first three products on the page you land on.",
)

print(response.output_text)

Install the SDK first: pip install anthropic, npm install @anthropic-ai/sdk or pip install openai. The Anthropic examples also turn on server-side fallbacks, which hand a declined request to another Claude model automatically.

Example output

These are the browser steps behind the task, run against the Zenrows MCP server with the same tools the agent calls:

browser_navigate {"url": "https://www.scrapingcourse.com/login"} -> {"session_id": "851298be-...", "title": "Login Challenge to Learn Web Scraping - ScrapingCourse.com"}
browser_fill {"selector": "#email", "value": "admin@example.com"} -> {"ok": true}
browser_fill {"selector": "#password", "value": "password"} -> {"ok": true}
browser_press_key {"key": "Enter"} -> {"ok": true}
browser_wait_for_selector {"selector": ".product-name"} -> {"ok": true}
browser_query_selector_all {"selector": ".product-name"} -> {"elements": [{"text": "Chaz Kangeroo Hoodie", ...}, {"text": "Teton Pullover Hoodie", ...}, {"text": "Bruno Compete Hoodie", ...}, ...]}
browser_close {} -> {"ok": true}

The model picks its own selectors and order, so its steps can differ from this run. Its answer names the three products: Chaz Kangeroo Hoodie, Teton Pullover Hoodie and Bruno Compete Hoodie.

Customize it

Change	How
Browse from a country	Say it in the task, for example “as a visitor from Germany”. The model passes the country to `browser_navigate`, see Country
Read pages fast and browse only when needed	Add `scrape` and `extract` to the tool list, see Fetch first, open a Browser when a task needs interaction
Run steps in one call	Add `browser_batch` to the tool list. The model sends several actions in one request
Screenshots or PDFs in the answer	Keep `browser_screenshot`, add `browser_generate_pdf`, see Take screenshots and save pages as PDF
More tools	The full list is in the MCP tool reference: tabs, cookies, local storage, scrolling, drag and drop, and more
Your own loop and logging	Run the Zenrows MCP server from your code with any MCP client library, or write Playwright yourself, see Connect Playwright or Puppeteer to Zenrows Browser

Troubleshooting

The agent answers without opening a page: name the action in the task (“Log in at…”, “Open…”) and keep the system prompt that tells it to use the browser tools.
The answer ends mid-task: the task needs more steps. Raise max_tokens, or split the job into two requests.
401 from the Zenrows server: the authorization_token or authorization value isn’t a valid Zenrows API key. Copy it again from the dashboard.
A session stays open after an error: keep “call browser_close on every session you open” in the system prompt. Sessions also end on their own when their time runs out.

Let your AI assistant browse with Browser tools

The same tools from Claude, Cursor or any chat client.

Fetch first, open a Browser when a task needs interaction

Give your agent fast reading and a full browser.

Build a research agent with live web data

An agent that reads and cites live pages.

Connect Playwright or Puppeteer to Zenrows Browser

Drive the browser from your own code.

Start Here

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Production

Build a browser agent with Zenrows

Ask your AI agent

How it works

Build the agent

Example output

Customize it

Troubleshooting

Let your AI assistant browse with Browser tools

Fetch first, open a Browser when a task needs interaction

Build a research agent with live web data

Connect Playwright or Puppeteer to Zenrows Browser

​Ask your AI agent

​How it works

​Build the agent

​Example output

​Customize it

​Troubleshooting

​Related recipes

Let your AI assistant browse with Browser tools

Fetch first, open a Browser when a task needs interaction

Build a research agent with live web data

Connect Playwright or Puppeteer to Zenrows Browser

Ask your AI agent

How it works

Build the agent

Example output

Customize it

Troubleshooting

Related recipes