How to Use the GroundX MCP in LangChain

Q: How do I handle slow document processing with GroundX in a LangChain agent?

Use a loop in your LangChain chain that calls getingeststatus after running ingestdocuments. Your agent can check the status field and pause execution until the platform finishes parsing.

Q: Can a LangChain agent query multiple GroundX buckets at once?

Yes, your LangChain agent can run searchcontent to query across all buckets, or use listbuckets to find specific IDs first. This lets the agent target its search dynamically based on user input.

Q: What happens if searchdocuments returns no results in my chain?

Your LangChain agent can fallback to searchcontent for a broader semantic search across all buckets. You can program this fallback logic directly into your chain's routing step.

Feed clean search results straight into your LangChain chains using the GroundX MCP Server.

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Connect GroundX MCP to LangChain

Create your Vinkius account to connect GroundX 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 GroundX with LangChain

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Build multi-step GroundX RAG chains in LangChain

The `create_bucket` tool lets your LangChain agent partition your source data before starting any ingestion. Your agent determines which bucket to target, runs `ingest_documents`, and polls `get_ingest_status` to ensure files are fully processed before moving to the next chain step. LangSmith tracks every single step of this process, showing you the exact inputs and outputs of `search_content`. You see precisely how your LangChain agent translates raw user queries into clean context without losing visibility.

Automate website ingestion for LangChain agents

The `ingest_website` tool lets your LangChain agent crawl and import live web pages on the fly through this MCP Server. Instead of hardcoding scrapers, you let the agent decide when a source is outdated and run the crawl command. Once the crawl completes, your LangChain chain uses `search_documents` to pull the fresh data. You get direct access to clean, parsed text chunks that fit perfectly into your prompt templates.

Map LangChain tool outputs to semantic search

The `search_content` tool serves as the core semantic retrieval engine for your conversational LangChain chains. Your agent calls this tool to query all buckets simultaneously, bypassing the need to manage vector databases manually. This MCP Server keeps your chain's context window small and prevents hallucination. If your LangChain agent needs to narrow its focus, it switches to `search_documents` to filter results by specific metadata.

Setup guide

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

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Common questions about GroundX MCP in LangChain

Use a loop in your LangChain chain that calls `get_ingest_status` after running `ingest_documents`. Your agent can check the status field and pause execution until the platform finishes parsing.

Yes, your LangChain agent can run `search_content` to query across all buckets, or use `list_buckets` to find specific IDs first. This lets the agent target its search dynamically based on user input.

Initialize the client with the Vinkius endpoint URL and call `get_tools` to retrieve the GroundX toolset. Pass these tools directly to your LangChain agent constructor to let it access search and ingestion.

Your LangChain agent can fallback to `search_content` for a broader semantic search across all buckets. You can program this fallback logic directly into your chain's routing step.

The Vinkius MCP gateway runs each session in an isolated, ephemeral V8 sandbox, meaning your raw document text and search queries are never exposed to other tenants. Your files are stored securely and accessed only when your specific LangChain client invokes search tools.

Use it with your favorite AI tools

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