How to Use the Flowise MCP in LangChain

Q: Is it possible to list specific chatflows in LangChain?

Absolutely. Use listchatflows to pull your available workflows into the agent's context, allowing it to select the correct flow based on user intent.

Chain Flowise workflows directly into your LangChain agents for modular, multi-step reasoning.

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Connect Flowise MCP to LangChain

Create your Vinkius account to connect Flowise 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 Flowise with LangChain

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Claude

Perplexity

Chain Flowise predictions in LangChain

Trigger complex logic by piping `predict` calls into your existing chains. Your agent treats every Flowise response as a new data point for the next step in the sequence. This keeps your application logic modular. You avoid hard-coding behavior by letting the agent decide when to run a specific chatflow based on the current chain state.

Audit LangChain tool execution

Hook into `get_history` to pull raw execution logs directly into your LangSmith traces. You get full visibility into how Flowise processed a request. You'll see exactly what happened during each turn of the conversation. This level of transparency helps you debug agent performance issues without digging through external dashboard logs.

Map Flowise tools to LangChain agents

Use `list_tools` to expose Flowise capabilities to your agent's decision-making loop. The agent identifies the right tool dynamically for the task at hand. It removes the need for manual orchestration. By exposing these tools, you allow your agent to pull configuration details from `get_chatflow` or list active `list_agentflows` on the fly.

Setup guide

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

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Common questions about Flowise MCP in LangChain

Install the necessary MCP adapters and initialize the MultiServerMCPClient. Point it to your Vinkius endpoint and pass the retrieved tools directly into your agent constructor.

Yes, that is the primary benefit of this setup. The output of any Flowise tool becomes valid input for subsequent nodes in your LangChain pipeline.

It does. You can ingest history and execution data from the server into your standard tracing tools to monitor throughput and error rates.

Absolutely. Use `list_chatflows` to pull your available workflows into the agent's context, allowing it to select the correct flow based on user intent.

All traffic is handled through a secure MCP transport layer. Credentials for your workflows are managed via `list_credentials` and never exposed in plain text within your agent code.

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