Can my agent run a prediction against a specific Flowise chatflow?

Yes. Use the 'predict' tool. Provide the 'chatflow_id' and your question. The agent will command the Flowise backend to process the logic chain and return the AI-generated response directly in your chat.

How do I see the past conversational logs for a chatflow via chat?

Use the 'get_history' tool with the 'chatflow_id'. Your agent will retrieve the past execution traces and logs, helping you understand how users have interacted with that specific logic chain natively.

Can I list all custom tools configured in my Flowise instance through the agent?

Absolutely. Use the 'list_tools' tool. Your agent will retrieve custom tools and integrations configured in your environment, allowing you to audit available capabilities through natural conversation.

Flowise MCP Server for LangChain 7 tools — connect in under 2 minutes

Name: Flowise
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Flowise through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "flowise": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Flowise, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Flowise MCP Server

Connect your FlowiseAI instance to any AI agent and take full control of your low-code generative AI application development through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Flowise through native MCP adapters. Connect 7 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Chatflow Orchestration — List and retrieve detailed architectural nodes and edges for all deployed Chatflows within your Flowise instance natively
Agentic Workflow Control — Access compound Agentflows defining complex AI tasks and multi-step reasoning logic synchronously
Live AI Prediction — Commands the backend to submit user questions to specific Chatflows and retrieve generated AI responses in real-time
Execution History Auditing — Pull precise past execution traces and conversational logs to debug logic chains and monitor agent performance limitlessly
Tool & Integration Discovery — Retrieve custom tools and third-party integrations configured in your Flowise environment to verify available capabilities
Credential Oversight — Enumerate stored credential components used to authenticate your AI logic chains securely within the platform
System Health Monitoring — Verify instance status and available base endpoints to ensure your AI orchestration layer is operational

The Flowise MCP Server exposes 7 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Flowise to LangChain via MCP

Follow these steps to integrate the Flowise MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 7 tools from Flowise via MCP

Why Use LangChain with the Flowise MCP Server

LangChain provides unique advantages when paired with Flowise through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine Flowise MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Flowise queries for multi-turn workflows

Flowise + LangChain Use Cases

Practical scenarios where LangChain combined with the Flowise MCP Server delivers measurable value.

RAG with live data: combine Flowise tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Flowise, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Flowise tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Flowise tool call, measure latency, and optimize your agent's performance

Flowise MCP Tools for LangChain (7)

These 7 tools become available when you connect Flowise to LangChain via MCP:

get_chatflow

Get chatflow details

get_history

Get chat execution history

list_agentflows

List agentflows

list_chatflows

List chatflows

list_credentials

List credentials

list_tools

List available tools

predict

Run prediction on chatflow

Example Prompts for Flowise in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Flowise immediately.

"Ask chatflow 'abc-123': 'Summarize this document: [Context]'"

"List all active chatflows in my instance"

"Show me the execution history for chatflow 'Legal-Assistant'"

Troubleshooting Flowise MCP Server with LangChain

Common issues when connecting Flowise to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Flowise + LangChain FAQ

Common questions about integrating Flowise MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.