How to Use the Apify MCP in LangChain

Q: How do I run an Apify scraper inside a LangChain agent?

Use the runactor tool inside your agent's tool list. Your agent triggers the scraper, gets a run ID, and polls for completion using this MCP Server.

Q: Can LangChain handle long-running Apify actors?

Yes, you want runactor for long tasks and getrun to check the status. For short runs under five minutes, use runactorsync to block and get data immediately.

Q: How do I feed scraped data into other LangChain integrations?

Use getdatasetitems to pull the raw JSON. Once the data is in your chain, you can pass it to any database or vector store integration.

Feed clean web data straight into your LangChain reasoning loops with this managed MCP Server.

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Connect Apify MCP to LangChain

Create your Vinkius account to connect Apify 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 Apify with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Apify scraping tasks in LangChain

`run_actor` initiates web scraping jobs directly inside your agent's execution sequence. Your agent triggers the scrape, polls the progress, and feeds the output into the next chain step without manual intervention. If a job runs too long or costs too much, `abort_run` stops it instantly. This keeps your automated pipelines fast and prevents run-away cloud costs.

Extract and paginate web datasets

`get_dataset_items` pulls raw JSON data directly from finished scraping runs into your active context. The tool handles pagination parameters like limit and offset so your agent handles massive datasets in chunks. You get clean, structured data ready for your next prompt. No parsing HTML or cleaning up messy text files yourself.

Track actor usage and limits on the fly

`get_account_limits` keeps your autonomous chains from hitting billing walls unexpectedly. Your agent checks your current compute unit balance before kicking off heavy tasks. This metadata ensures your background runs execute smoothly. You avoid sudden failures during long-running scraping jobs.

Setup guide

Set up Apify 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 Apify 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({
    "apify-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 Apify transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Apify MCP in LangChain

Use the `run_actor` tool inside your agent's tool list. Your agent triggers the scraper, gets a run ID, and polls for completion using this MCP Server.

Yes, you want `run_actor` for long tasks and `get_run` to check the status. For short runs under five minutes, use `run_actor_sync` to block and get data immediately.

Yes, it does. Every tool call goes through your standard LangChain setup, giving you full visibility into inputs, outputs, and latency.

Use `get_dataset_items` to pull the raw JSON. Once the data is in your chain, you can pass it to any database or vector store integration.

Your JSON datasets and key-value store items stay in your Apify account. The Vinkius MCP Server runs in a secure, ephemeral sandbox, passing data directly to your local client without storing it.

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