How to Use the HiFlow MCP in LangChain

Q: How can a LangChain agent use HiFlow to find unpaid invoices for a specific customer?

First, the agent calls listcustomers to find the customer ID. Then, it uses that ID to call listinvoices and filters the results for any with a 'due' or 'overdue' status. The agent chains these two MCP tools together to complete the task.

Q: Can I build a LangChain agent with HiFlow to automatically generate weekly project summaries?

Yes, that's a perfect use case. The agent would call listjobs to get active projects, then loop through them, calling getjob, listtimesheets, and getinvoice to gather all the data needed for a summary report.

Build autonomous agents with LangChain that run your business on HiFlow, from creating customers to chasing invoices.

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Connect HiFlow MCP to LangChain

Create your Vinkius account to connect HiFlow 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.

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Setup HiFlow with LangChain

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Chain HiFlow Tools Together

Your agent can use `list_customers` to get a client list, then decide on its own to call `list_invoices` for a specific customer who's overdue. This isn't a pre-written script. It's a chain of reasoning your agent builds on the fly. You give it a goal, like 'find our highest value clients with unpaid bills.' The agent uses the tools it has—like `get_customer` to check client data and `get_invoice` to check payment status—to figure out the steps and get you an answer.

Give Your Agent a To-Do List

Set up a LangChain agent to watch your business. It can poll `list_jobs` for new projects, then use the job details to draft an initial quote with `get_estimate`. It just gets it done. Here's the thing: you don't have to map out every single click. The agent has access to the HiFlow MCP Server and knows its goal. It connects the dots between `get_job` and `create_customer` for you, acting like a project coordinator that never takes a coffee break.

Trace Every Agent Decision

When your agent calls `list_timesheets`, you see exactly what it sent and what it got back. With LangSmith tracing, there's no black box. You have a full, transparent log of every tool call. This is how you fix things when they go wrong. If an invoice looks weird, you can trace the agent's calls to `get_job` and `get_customer` to see the exact data it used. It makes debugging complex chains simple.

Setup guide

Set up HiFlow MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
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 LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "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 HiFlow tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

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

async with MultiServerMCPClient({
    "hiflow-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 HiFlow transactions"
    })
    print(result["messages"][-1].content)

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Common questions about HiFlow MCP in LangChain

First, the agent calls `list_customers` to find the customer ID. Then, it uses that ID to call `list_invoices` and filters the results for any with a 'due' or 'overdue' status. The agent chains these two MCP tools together to complete the task.

Calling the API directly means you write the logic. Using a LangChain agent means you give it a goal, and the agent writes its own logic by choosing which HiFlow tools to call in what order. It's about autonomy, not just automation.

Yes, that's a perfect use case. The agent would call `list_jobs` to get active projects, then loop through them, calling `get_job`, `list_timesheets`, and `get_invoice` to gather all the data needed for a summary report.

No. Vinkius handles the auth. You get a single endpoint token for your agent, and that's it. Your code stays clean and doesn't need to manage API keys or refresh tokens.

Your agent only interacts with your HiFlow data, like customer names, invoice details, and job descriptions. All calls run through Vinkius's ephemeral sandboxes, meaning the compute environment that processes your data is destroyed after each request. Nothing is stored.

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