Modal (Serverless AI Infrastructure) MCP Server for LangChain 7 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Modal (Serverless AI Infrastructure) 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({
"modal-serverless-ai-infrastructure": {
"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 Modal (Serverless AI Infrastructure), 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 Modal (Serverless AI Infrastructure) MCP Server
Connect your Modal account to any AI agent and take full control of your high-performance AI infrastructure, serverless GPU deployments, and persistent storage through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Modal (Serverless AI Infrastructure) 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
- App Orchestration — List isolated active and historical Modal app contexts to track function execution states and resource allocation directly from your agent
- Deployment Management — Enumerate promoted long-running deployments and retrieve detailed web endpoints and serving configurations securely
- Operational Control — Force stop actively running Modal app executions gracefully via App ID to prevent unnecessary billing cycles and manage system resources natively
- Security & Secret Audit — List stored secret dictionary references and verify environment variable mappings attached to your serverless functions securely
- Storage Visibility — Monitor persisted disk network block volumes and data mount directories used across your distributed compute instances
- Infrastructure Inspection — Deep-dive into specific App or Deployment IDs to retrieve precise JSON metadata representing your infrastructure's current state vectors
The Modal (Serverless AI Infrastructure) 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 Modal (Serverless AI Infrastructure) to LangChain via MCP
Follow these steps to integrate the Modal (Serverless AI Infrastructure) 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 Modal (Serverless AI Infrastructure) via MCP
Why Use LangChain with the Modal (Serverless AI Infrastructure) MCP Server
LangChain provides unique advantages when paired with Modal (Serverless AI Infrastructure) through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Modal (Serverless AI Infrastructure) 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 Modal (Serverless AI Infrastructure) queries for multi-turn workflows
Modal (Serverless AI Infrastructure) + LangChain Use Cases
Practical scenarios where LangChain combined with the Modal (Serverless AI Infrastructure) MCP Server delivers measurable value.
RAG with live data: combine Modal (Serverless AI Infrastructure) tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Modal (Serverless AI Infrastructure), synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Modal (Serverless AI Infrastructure) tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Modal (Serverless AI Infrastructure) tool call, measure latency, and optimize your agent's performance
Modal (Serverless AI Infrastructure) MCP Tools for LangChain (7)
These 7 tools become available when you connect Modal (Serverless AI Infrastructure) to LangChain via MCP:
get_app
Get static specifics of an exact Modal App ID
get_deployment
Get an explicitly tracked deployment detail mapped bound
list_apps
List isolated active/historical Modal Apps contexts
list_deployments
List strictly managed Modal platform explicitly promoted deployments
list_secrets
List static secret dictionary configuration references
list_volumes
List Modal persisted disk network block volumes
stop_app
Force stop an actively running explicit Modal App execution
Example Prompts for Modal (Serverless AI Infrastructure) in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Modal (Serverless AI Infrastructure) immediately.
"List all active Modal apps running in my account"
"Force stop Modal app ID 'ap-123'"
"Show me all persistent volumes configured in my workspace"
Troubleshooting Modal (Serverless AI Infrastructure) MCP Server with LangChain
Common issues when connecting Modal (Serverless AI Infrastructure) to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersModal (Serverless AI Infrastructure) + LangChain FAQ
Common questions about integrating Modal (Serverless AI Infrastructure) 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 Modal (Serverless AI Infrastructure) 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 Modal (Serverless AI Infrastructure) to LangChain
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.