Upstash Redis MCP Server for LangChain 7 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Upstash Redis 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 MCP SERVER
Vinkius supports streamable HTTP and SSE.
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({
"upstash-redis": {
"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 Upstash Redis, 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 Upstash Redis MCP Server
Connect your Upstash Redis serverless database securely to your conversational AI agent via their REST API. Activating this integration grants your AI the technical autonomy to function as a responsive database administrator, enabling it to scan live keys, read raw datastore strings, set temporal expiration values, and even debug in real-time straight from your chat or IDE terminal.
LangChain's ecosystem of 500+ components combines seamlessly with Upstash Redis through native MCP adapters. Connect 7 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
- Read & Write Values — Fetch the exact string configuration of a stored key (
get), or instruct the AI to inject a new value (set) complete with Time-To-Live (TTL) expiration limits. - Data Structure Discovery — Perform pattern-based scans across your database to track down dynamically generated keys and inspect their underlying structures or lifespans (
list_keys,get_key_info). - Manage Counters — Safely increment or decrement numerical keys dynamically, perfect for managing rate limits, operational counters, or user session metrics directly during troubleshooting flows.
- Maintain Health — Ping the cluster instance for responsive status checks and remove (
delete) specific keys or cache fragments completely.
The Upstash Redis 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 Upstash Redis to LangChain via MCP
Follow these steps to integrate the Upstash Redis 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 Upstash Redis via MCP
Why Use LangChain with the Upstash Redis MCP Server
LangChain provides unique advantages when paired with Upstash Redis through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Upstash Redis 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 Upstash Redis queries for multi-turn workflows
Upstash Redis + LangChain Use Cases
Practical scenarios where LangChain combined with the Upstash Redis MCP Server delivers measurable value.
RAG with live data: combine Upstash Redis tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Upstash Redis, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Upstash Redis tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Upstash Redis tool call, measure latency, and optimize your agent's performance
Upstash Redis MCP Tools for LangChain (7)
These 7 tools become available when you connect Upstash Redis to LangChain via MCP:
delete
Provide a comma-separated list of keys. Deletes one or more keys from Redis
get
Retrieves the string value stored at a key
get_key_info
Retrieves the data type and TTL of a key
increment
Use negative numbers to decrement. Increments or decrements a numeric counter at a key
list_keys
Avoid broad patterns like "*" on large databases. Scans for keys matching a glob pattern
ping
Pings the Redis instance to verify connectivity
set
You can specify expiry in seconds. Sets a string value at a key with an optional TTL
Example Prompts for Upstash Redis in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Upstash Redis immediately.
"List all active Redis keys associated with app sessions."
"Check the Time-To-Live duration limit configured for the 'cache:product_header' key."
"Delete all caching strings tagged as 'user_193_avatar' from the database immediately."
Troubleshooting Upstash Redis MCP Server with LangChain
Common issues when connecting Upstash Redis to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersUpstash Redis + LangChain FAQ
Common questions about integrating Upstash Redis 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.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Upstash Redis with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Upstash Redis to LangChain
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.