How to Use the Browserhub MCP in LangChain

Q: Can LangChain see my Browserhub scraping history?

Yes, you use listscrapingjobs to pull recent logs back into your agent. This allows your code to reference past outcomes when planning new tasks.

Q: How do I check my credits with Browserhub in LangChain?

Simply trigger the getaccountbalance tool. It returns your current credit count so your agent can manage its own budget.

Build complex LangChain chains that scrape live web data directly into your reasoning pipelines.

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Connect Browserhub MCP to LangChain

Create your Vinkius account to connect Browserhub to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Browserhub with LangChain

Ask AI about this MCP

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Claude

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Chain Browserhub scraping in LangChain

Feed your agents live web data by linking `run_scraper` directly into your LangGraph workflows. Each output flows into your next chain segment without manual data handling. Monitor every step through LangSmith to verify exactly what your agent scraped. You get full visibility into tool latency and the exact inputs provided to the Browserhub MCP server.

Manage scraping jobs with LangChain

Control your scraping lifecycle by calling `list_scraping_jobs` and `get_scraping_job` within your agent logic. Your agents decide when to start a new job or check the status of a pending one. This keeps your application state clean and reactive. You don't need external scrapers when your chain handles the entire extraction process in one go.

Browse and audit your Browserhub setup

Query your available configurations using `list_scrapers` and `get_blueprint` to ensure your agents are using the right setup. It prevents hardcoded errors by letting the agent pick the correct scraper dynamically. Keep track of your usage by calling `get_account_balance` periodically. Your chain can even decide to pause operations if your credit threshold dips too low.

Setup guide

Set up Browserhub MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Browserhub tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "browserhub-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Browserhub transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Browserhub. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Browserhub MCP in LangChain

You connect the server as a tool resource within your LangGraph setup. The agent invokes specific functions like `run_scraper` as part of its decision-making chain.

Yes, you use `list_scraping_jobs` to pull recent logs back into your agent. This allows your code to reference past outcomes when planning new tasks.

We isolate all your scraping activity behind a private endpoint token. Your extracted web data remains local to your specific agent session and never leaks between different chains.

Simply trigger the `get_account_balance` tool. It returns your current credit count so your agent can manage its own budget.

The MCP server returns an error object to your chain. Your agent can catch this, review the `get_scraping_job` status, and decide whether to retry or try a different blueprint.

Use it with your favorite AI tools

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