How to Use the Conduit MCP in LangChain

Use Conduit with LangChain to chain your data pipeline tasks directly into autonomous reasoning loops.

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Connect Conduit MCP to LangChain

Create your Vinkius account to connect Conduit 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 Conduit with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Conduit execution logs into LangChain workflows

Feed `get_run_status` and `list_workflow_runs` outputs directly into your agent's reasoning chain. You stop guessing why a sync failed when the agent sees the exact error code in the next step. This MCP Server provides the raw context your chain needs to make decisions. Your agent identifies a failure, checks the status, and acts before you even notice a break in the data.

Automate data movement inside LangChain agents

Trigger specific jobs using `trigger_workflow` based on logic defined in your LangGraph nodes. The agent evaluates current conditions and decides exactly when to push new data through your pipelines. Everything happens in one execution flow. You aren't context-switching between your terminal and the platform because the agent handles the heavy lifting.

Map out your entire Conduit infrastructure

Use `list_workflows` and `list_connections` to build a real-time graph of your data movement. Your agent keeps this map current without manual updates. LangChain parses these lists to understand your topology. Now your agent knows exactly which source and destination pairs are active before it tries to run a job.

Setup guide

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

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Common questions about Conduit MCP in LangChain

Install the MCP adapters via pip and initialize the client with your endpoint. Pass the result of the tool fetch directly into your agent definition.

Absolutely. You can aggregate this server with other tools in your agent's pool to let different nodes query your pipeline state.

Yes. Every tool call and response is visible in your LangSmith traces. You see the exact input the agent sent to the server.

Vinkius manages your authentication via a single token. Your API keys never touch your local code environment.

We enforce strict zero-trust isolation on the server side. Only your authorized agent can request workflow metadata or trigger run logs.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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