How do I find my QingFlow Access Token?

Log in to QingFlow, go to the Qing Store (轻商城), install the 'OPEN API' plugin under Third-party Connections, and find your Access Token in the plugin configuration.

Can I check why a request is pending approval?

Yes. Using the `get_workflow_status` tool with the application and record IDs, you can retrieve the current approval node, the person responsible, and the status of the process.

How do I format the data for creating a record?

QingFlow uses a specific format for its fields. Use the `get_app_schema` tool first to see the `queId` for each field, then provide the data as a JSON string following the required structure.

QingFlow MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: QingFlow
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect QingFlow 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({
        "qingflow": {
            "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 QingFlow, 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

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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 QingFlow MCP Server

Empower your AI agent to orchestrate your business processes with QingFlow, the premier no-code BPM platform for digital transformation. By connecting QingFlow to your agent, you transform complex application management and data orchestration into a natural conversation. Your agent can instantly list your applications, retrieve form schemas, manage records (create, update, delete), and even monitor workflow approval statuses without you ever needing to navigate the technical dashboard. Whether you are managing procurement, HR approvals, or project tracking, your agent acts as a real-time process manager, ensuring your business logic is always executed and optimized.

LangChain's ecosystem of 500+ components combines seamlessly with QingFlow through native MCP adapters. Connect 10 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

Application Orchestration — List all accessible applications and browse their internal structures.
Data Management — Manage application records with full support for creating, listing, and granular updates.
Workflow Monitoring — Check the current status of automated workflows and approval processes for any record.
Schema Auditing — Retrieve application schemas to understand field structures and widget IDs.
User Coordination — Access workspace user lists to manage assignments and participation effectively.

The QingFlow MCP Server exposes 10 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 QingFlow to LangChain via MCP

Follow these steps to integrate the QingFlow 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 10 tools from QingFlow via MCP

Why Use LangChain with the QingFlow MCP Server

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

The largest ecosystem of integrations, chains, and agents — combine QingFlow 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 QingFlow queries for multi-turn workflows

QingFlow + LangChain Use Cases

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

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

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

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

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

QingFlow MCP Tools for LangChain (10)

These 10 tools become available when you connect QingFlow to LangChain via MCP:

create_record

Create a new application record

delete_record

Delete an application record

get_app_schema

Get application field schema

get_record_details

Get record detailed data

get_workflow_status

Get record workflow status

list_apps

List all QingFlow applications

list_data

List records in an application

list_users

List workspace users

list_workflows

List application workflows

update_record

Update an existing record

Example Prompts for QingFlow in LangChain

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

"List all applications in my QingFlow workspace."

"Show me the records for the 'Asset Management' application."

"What is the approval status for record 'req-9920' in 'Leave Request'?"

Troubleshooting QingFlow MCP Server with LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

QingFlow + LangChain FAQ

Common questions about integrating QingFlow 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.