AirOps MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect AirOps 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({
"airops": {
"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 AirOps, 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 AirOps MCP Server
Connect your AirOps account to your AI agent to unlock professional AI workflow orchestration and agent management. From executing complex multi-step workflows synchronously or asynchronously to interacting with specialized chat agents and managing managed memory stores, your agent handles your AI operations through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with AirOps 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
- Workflow Orchestration — Execute and monitor AirOps apps and workflows, passing custom parameters and retrieving structured results
- Agent Interaction — Chat directly with your specialized AirOps agents to perform niche tasks or leverage unique agent instructions
- Memory Management — Search within managed memory stores (vector databases) and add documents to enrich your AI's domain knowledge
- File Orchestration — Upload and manage files to be used as inputs for your AI workflows and data extraction tasks
- Real-time Status — Monitor execution statuses and cancel long-running AI tasks directly from your chat interface
The AirOps 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 AirOps to LangChain via MCP
Follow these steps to integrate the AirOps 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 AirOps via MCP
Why Use LangChain with the AirOps MCP Server
LangChain provides unique advantages when paired with AirOps through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine AirOps 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 AirOps queries for multi-turn workflows
AirOps + LangChain Use Cases
Practical scenarios where LangChain combined with the AirOps MCP Server delivers measurable value.
RAG with live data: combine AirOps tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query AirOps, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain AirOps tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every AirOps tool call, measure latency, and optimize your agent's performance
AirOps MCP Tools for LangChain (10)
These 10 tools become available when you connect AirOps to LangChain via MCP:
add_memory_document
Enrich AI knowledge
cancel_execution
Stop a running task
chat_with_agent
Interact with AI agent
execute_workflow_async
Run workflow asynchronously
execute_workflow_sync
Best for quick tasks. Run workflow synchronously
get_app_details
Get app metadata
get_execution_status
Check execution progress
list_apps
List AI applications
search_memory_store
Search vector database
upload_file
Upload file for AI
Example Prompts for AirOps in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with AirOps immediately.
"List all AI apps in my AirOps workspace."
"Execute the 'Data Extractor' app (UUID: abc-123) with input 'Extract names from this text: John Doe visited London'."
"Search my 'Knowledge Base' memory store for 'API integration guides'."
Troubleshooting AirOps MCP Server with LangChain
Common issues when connecting AirOps to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersAirOps + LangChain FAQ
Common questions about integrating AirOps 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 AirOps 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 AirOps to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.