ZenRows MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect ZenRows through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"zenrows": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using ZenRows, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About ZenRows MCP Server
Connect your ZenRows account to any AI agent and harness the power of industrial-grade web scraping through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with ZenRows through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Universal Scraping — Retrieve raw HTML from any website while ZenRows automatically rotates proxies and handles CAPTCHAs
- JavaScript Rendering — Scrape dynamic SPAs and complex web apps by using a headless browser to capture the full rendered state
- Anti-Bot Bypass — Effortlessly bypass sophisticated protections like Cloudflare, DataDome, and PerimeterX with specialized bypass technology
- Markdown Conversion — Automatically convert web pages into clean Markdown, ideal for LLM ingestion and RAG applications
- Structured Data — Use auto-parse to extract JSON data from major e-commerce, search, and social platforms without manual selectors
- Visual Previews — Generate real-time screenshots of target pages to verify rendering or monitor visual changes
- Geographic Targeting — Execute scrapes using high-anonymity residential proxies from specific countries for localized content
The ZenRows MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect ZenRows to LangChain via MCP
Follow these steps to integrate the ZenRows MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from ZenRows via MCP
Why Use LangChain with the ZenRows MCP Server
LangChain provides unique advantages when paired with ZenRows through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine ZenRows MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across ZenRows queries for multi-turn workflows
ZenRows + LangChain Use Cases
Practical scenarios where LangChain combined with the ZenRows MCP Server delivers measurable value.
RAG with live data: combine ZenRows tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query ZenRows, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain ZenRows tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every ZenRows tool call, measure latency, and optimize your agent's performance
ZenRows MCP Tools for LangChain (10)
These 10 tools become available when you connect ZenRows to LangChain via MCP:
get_screenshot
Generates a URL that returns a screenshot of the target page
scrape_antibot
Enables js_render and antibot=true. Scrape with full anti-bot bypass for heavily protected sites
scrape_autoparse
Scrape with automatic structured data extraction
scrape_custom
g. wait, css_extractor, session_id). Execute a scrape using advanced custom parameters
scrape_geo
g. "us", "gb") for localized content. Scrape using a proxy from a specific country
scrape_html
ZenRows automatically rotates proxies and handles CAPTCHAs. Scrape raw HTML using ZenRows anti-bot proxy pool
scrape_js
Enables js_render=true. Slower and more expensive than static scraping. Scrape JS-rendered HTML using ZenRows headless browser
scrape_markdown
Automatically removes boilerplate like navigation and ads. Scrape and convert page content to clean Markdown
scrape_premium
Sets premium_proxy=true for higher anonymity. Scrape using ZenRows premium residential proxies
scrape_wait
g. "#results") to wait for before capturing the HTML. Scrape with JS render waiting for a specific CSS selector
Example Prompts for ZenRows in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with ZenRows immediately.
"Scrape 'https://example.com' and return the content in Markdown."
"Bypass Cloudflare and scrape the rendered HTML of 'https://protected-site.com'."
"Get a screenshot of 'https://news-portal.com/breaking-news'."
Troubleshooting ZenRows MCP Server with LangChain
Common issues when connecting ZenRows to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersZenRows + LangChain FAQ
Common questions about integrating ZenRows MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect ZenRows with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect ZenRows to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.