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Prerequisites

Before we begin, you’ll need OpenAI API key and Klavis API key.

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Installation

First, install the required packages: 
pip install langchain-mcp-adapters langgraph langchain-openai klavis

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Setup Environment Variables

import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"  # Replace
os.environ["KLAVIS_API_KEY"] = "YOUR_KLAVIS_API_KEY"  # Replace

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Step 1 - Create Strata MCP Server with Gmail and Slack

from klavis import Klavis
from klavis.types import McpServerName, ToolFormat
import webbrowser

klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))

response = klavis_client.mcp_server.create_strata_server(
    servers=[McpServerName.GMAIL, McpServerName.SLACK], 
    user_id="1234"
)

# Handle OAuth authorization for each services
if response.oauth_urls:
    for server_name, oauth_url in response.oauth_urls.items():
        webbrowser.open(oauth_url)
        print(f"Or please open this URL to complete {server_name} OAuth authorization: {oauth_url}")
OAuth Authorization Required: The code above will open browser windows for each service. Click through the OAuth flow to authorize access to your accounts.

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Step 2 - Create LangChain Agent with Strata MCP Server

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

# Initialize LLM
llm = ChatOpenAI(model="gpt-4o-mini", api_key=os.getenv("OPENAI_API_KEY"))

# Create MCP client with Strata server
mcp_client = MultiServerMCPClient({
    "strata": {
        "transport": "streamable_http",
        "url": response.strata_server_url
    }
})

# Get tools from Strata MCP server
tools = asyncio.run(mcp_client.get_tools())

# Create agent with MCP-based tools
agent = create_react_agent(
    model=llm,
    tools=tools,
    prompt="You are a helpful assistant that uses MCP tools to interact with Gmail and Slack."
)

print("🤖 LangChain agent created successfully!")

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Step 3 - Run!

response_message = asyncio.run(agent.ainvoke({
    "messages": [{"role": "user", "content": "Check my latest 5 emails and summarize them in a Slack message to #general"}]
}))

print(f"\n🤖 Final Response: {response_message['messages'][-1].content}")
Check out the full TypeScript code example on GitHub for a complete working implementation.
Perfect! You’ve integrated LangChain with Klavis MCP servers.

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Next Steps

Integrations

Explore available MCP servers

API Reference

REST endpoints and schemas

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Useful Resources

Happy building 🚀