Splitwise MCP Server for LangChainGive LangChain instant access to 10 tools to Create Expense, Delete Expense, Get Current User, and more
LangChain is the leading Python framework for composable LLM applications. Connect Splitwise 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 Splitwise app connector for LangChain is a standout in the Productivity category — giving your AI agent 10 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({
"splitwise": {
"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 Splitwise, 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 Splitwise MCP Server
What you can do
- Track Expenses: Give your AI agent the ability to check who owes who across your distinct groups.
- Log Bills: Ask your AI to calculate receipts and accurately add the expense directly into Splitwise with automatic percentage or custom splits.
- Manage Friends & Groups: Easily pull details of group members.
LangChain's ecosystem of 500+ components combines seamlessly with Splitwise 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.
The Splitwise 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.
All 10 Splitwise tools available for LangChain
When LangChain connects to Splitwise through Vinkius, your AI agent gets direct access to every tool listed below — spanning expense-tracking, bill-splitting, debt-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.
Expects a stringified JSON object containing details like cost, description, format details (e.g. users__0__user_id=123, users__0__paid_share=5.00, etc. or JSON). Create an expense
Delete an expense
Get current user details
Get a specific expense
List expenses
Get friend details
List all friends
Get group details
List all groups
Get user details by ID
Connect Splitwise to LangChain via MCP
Follow these steps to wire Splitwise 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 Splitwise MCP Server
LangChain provides unique advantages when paired with Splitwise through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Splitwise 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 Splitwise queries for multi-turn workflows
Splitwise + LangChain Use Cases
Practical scenarios where LangChain combined with the Splitwise MCP Server delivers measurable value.
RAG with live data: combine Splitwise tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Splitwise, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Splitwise tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Splitwise tool call, measure latency, and optimize your agent's performance
Example Prompts for Splitwise in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Splitwise immediately.
"Check how much I currently owe in the 'Paris Trip' group."
"Add a new $100 expense for Uber rides to Splitwise and split it equally with Jane."
"List all my friends connected on Splitwise."
Troubleshooting Splitwise MCP Server with LangChain
Common issues when connecting Splitwise to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersSplitwise + LangChain FAQ
Common questions about integrating Splitwise 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.