Lamha MCP Server for LangChainGive LangChain instant access to 8 tools to Cancel Order, Check City Coverage, Create Order, and more
LangChain is the leading Python framework for composable LLM applications. Connect Lamha 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 Lamha app connector for LangChain is a standout in the Productivity category — giving your AI agent 8 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({
"lamha": {
"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 Lamha, 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 Lamha MCP Server
Connect your Lamha account to any AI agent and manage HR operations through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Lamha through native MCP adapters. Connect 8 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
- Employee Management — List employees, inspect profiles, and track status
- Attendance Tracking — Monitor check-in/out times and attendance records
- Department Browsing — Navigate organizational structure and departments
- Leave Management — Track leave requests, balances, and approvals
- Payroll Access — View payroll data and compensation details
The Lamha MCP Server exposes 8 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 8 Lamha tools available for LangChain
When LangChain connects to Lamha through Vinkius, your AI agent gets direct access to every tool listed below — spanning attendance-tracking, leave-management, payroll-management, 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.
Cancel an existing order
Check delivery coverage for a city
Create a new logistics order
Get details for a specific order
List delivery carriers
List product inventory
List Lamha orders
List warehouses
Connect Lamha to LangChain via MCP
Follow these steps to wire Lamha 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 Lamha MCP Server
LangChain provides unique advantages when paired with Lamha through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Lamha 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 Lamha queries for multi-turn workflows
Lamha + LangChain Use Cases
Practical scenarios where LangChain combined with the Lamha MCP Server delivers measurable value.
RAG with live data: combine Lamha tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Lamha, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Lamha tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Lamha tool call, measure latency, and optimize your agent's performance
Example Prompts for Lamha in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Lamha immediately.
"Show all departments and today's attendance."
"Show pending leave requests and employee leave balances."
"Show payroll summary and employee details for the Engineering team."
Troubleshooting Lamha MCP Server with LangChain
Common issues when connecting Lamha to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersLamha + LangChain FAQ
Common questions about integrating Lamha 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.