FlowiseAI MCP Server for LangChainGive LangChain instant access to 12 tools to Execute Chatflow Prediction, Get Chatflow Details, Get Server Version, and more
LangChain is the leading Python framework for composable LLM applications. Connect FlowiseAI through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
Ask AI about this App Connector for LangChain
The FlowiseAI app connector for LangChain is a standout in the Friends Mcp category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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({
"flowiseai": {
"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 FlowiseAI, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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 FlowiseAI MCP Server
Connect your FlowiseAI (self-hosted) instance to any AI agent and take full control of your LLM orchestration and RAG workflows through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with FlowiseAI through native MCP adapters. Connect 12 tools via 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
- Prediction Orchestration — Trigger specific chatflows and retrieve LLM-generated responses programmatically using natural language inputs
- Chatflow Management — List all orchestration flows and retrieve detailed technical structures and metadata to monitor your AI agents
- Vector Intelligence — Programmatically upsert documents or raw data into the vector stores linked to your chatflows to ensure high-fidelity context
- Component Oversight — Access server-wide credentials, custom tools, and global variables to manage your complete Flowise ecosystem
- Operational Visibility — Monitor user feedback, leads, and assistant profiles directly through your agent for instant reporting
The FlowiseAI MCP Server exposes 12 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.
All 12 FlowiseAI tools available for LangChain
When LangChain connects to FlowiseAI through Vinkius, your AI agent gets direct access to every tool listed below — spanning llm-workflows, rag-pipelines, chatbot-development, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Trigger an LLM flow prediction
Get details for a specific chatflow
Get Flowise server version
List OpenAI-style assistants
List user feedback for a chatflow
List all LLM orchestration flows
List custom tools
List captured leads
List global variables
List configured credentials
List chatflow templates
Push data into a vector store
Connect FlowiseAI to LangChain via MCP
Follow these steps to wire FlowiseAI into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the FlowiseAI MCP Server
LangChain provides unique advantages when paired with FlowiseAI through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine FlowiseAI 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 FlowiseAI queries for multi-turn workflows
FlowiseAI + LangChain Use Cases
Practical scenarios where LangChain combined with the FlowiseAI MCP Server delivers measurable value.
RAG with live data: combine FlowiseAI tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query FlowiseAI, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain FlowiseAI tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every FlowiseAI tool call, measure latency, and optimize your agent's performance
Example Prompts for FlowiseAI in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with FlowiseAI immediately.
"List all my chatflows in Flowise."
"Execute chatflow 'cf_1' with question: 'How do I reset my password?'"
"Upsert this data into vector store for chatflow 'cf_2': [data]"
Troubleshooting FlowiseAI MCP Server with LangChain
Common issues when connecting FlowiseAI to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersFlowiseAI + LangChain FAQ
Common questions about integrating FlowiseAI MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.