Upzelo MCP Server for LangChainGive LangChain instant access to 10 tools to Get Customer, Get Flow, Get Flow Session, and more
LangChain is the leading Python framework for composable LLM applications. Connect Upzelo 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 Upzelo app connector for LangChain is a standout in the Sales Automation category — giving your AI agent 10 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({
"upzelo": {
"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 Upzelo, 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 Upzelo MCP Server
Connect your Upzelo churn management account to any AI agent and simplify how you retain customers and manage subscription lifecycles through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Upzelo through native MCP adapters. Connect 10 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
- Customer Management — List and search customer records, and update profile data for better segmentation and targeting.
- Retention Flows — List available flows and manually trigger retention sequences for customers at risk of cancelling.
- Subscription Tracking — Query all tracked subscriptions and update statuses or trial details programmatically.
- Flow Monitoring — Check the real-time status and outcomes of active flow sessions to verify retention success.
- External ID Sync — Link your internal system identifiers to Upzelo customer records for seamless integration.
The Upzelo 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.
All 10 Upzelo tools available for LangChain
When LangChain connects to Upzelo through Vinkius, your AI agent gets direct access to every tool listed below — spanning churn-reduction, subscription-management, customer-retention, 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.
Get details for a specific customer
Get details for a specific flow
Check the status of a flow session
Get details for a specific subscription
List all customers in Upzelo
List all retention flows
List all subscriptions
Used for segmentation and targeting. Create or update a customer record
Initialize a flow for a customer
Update subscription attributes
Connect Upzelo to LangChain via MCP
Follow these steps to wire Upzelo 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 Upzelo MCP Server
LangChain provides unique advantages when paired with Upzelo through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Upzelo 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 Upzelo queries for multi-turn workflows
Upzelo + LangChain Use Cases
Practical scenarios where LangChain combined with the Upzelo MCP Server delivers measurable value.
RAG with live data: combine Upzelo tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Upzelo, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Upzelo tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Upzelo tool call, measure latency, and optimize your agent's performance
Example Prompts for Upzelo in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Upzelo immediately.
"List all customers currently tracked in Upzelo."
"Trigger the 'Basic Retention' flow (ID: fl_8823) for customer 'cust_1029'."
"Show me the details for subscription 'sub_12903'."
Troubleshooting Upzelo MCP Server with LangChain
Common issues when connecting Upzelo to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersUpzelo + LangChain FAQ
Common questions about integrating Upzelo 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.