How to Use the Billit MCP in LangChain
Build multi-step accounting chains in LangChain by linking Billit invoice actions directly into your autonomous reasoning pipelines.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Billit MCP to LangChain
Create your Vinkius account to connect Billit to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Automate invoice generation in LangChain
Trigger `create_invoice` within your agentic chain to turn natural language requests into formal billing records. Your agent handles the logic of data extraction before hitting the endpoint. This setup removes manual entry from your pipeline. You define the sequence, and the tools handle the API communication.
Sync Billit data with LangChain chains
Feed the output of `list_invoices` or `list_parties` into subsequent chain links for automated reporting. The agent processes these lists as raw data inputs. Tracing your execution in LangSmith shows exactly how the model parses these lists. It turns unstructured requests into structured Billit API calls.
Manage company records via LangChain
Access `get_company_info` to provide your agent with context about your own business settings. This keeps your automated agents informed during complex financial workflows. Context remains consistent across your chain. Your agent uses this identity data to ensure every invoice it creates carries the correct business credentials.
Set up Billit MCP in LangChain
Prerequisites
- Python 3.10+ installed
-
langchain-mcp-adapters+langgraphpackages - Active Vinkius subscription with a valid endpoint token
- 1
Install dependencies
Run
pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChainBaseToolobjects. - 2
Connect via HTTP transport
Use
MultiServerMCPClientwith"transport": "http"pointing to your Vinkius endpoint. Replace[YOUR_TOKEN_HERE]with your token from cloud.vinkius.com. - 3
Create a ReAct agent
Pass the discovered tools to
create_react_agent()from LangGraph. The agent automatically routes Billit tool calls through the MCP protocol. - 4
Run with any LLM
Swap
ChatOpenAIforChatAnthropic,ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
async with MultiServerMCPClient({
"billit-mcp": {
"transport": "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,
)
result = await agent.ainvoke({
"messages": "List recent Billit transactions"
})
print(result["messages"][-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Billit. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
Why Choose Vinkius
Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.
Real-time monitoring
Live
visibility into every interaction
Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.
Built-in savings
60%
lower AI costs
Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.
Single dashboard
One
place for every integration
Every tool your AI connects to, managed from a single screen. One account, complete control.
Common questions about Billit MCP in LangChain
Use it with your favorite AI tools
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