How to Use the Authing MCP in LangChain

Q: How do I connect the Authing MCP Server to LangChain?

Run pip install langchain-mcp-adapters langgraph first. Then initialize a MultiServerMCPClient pointing to your endpoint URL. Call client.gettools() and pass the resulting array into your agent constructor.

Build identity management pipelines by connecting Authing to LangChain ReAct agents for automated user and role provisioning.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Authing MCP to LangChain

Create your Vinkius account to connect Authing 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.

GDPR Free for Subscribers

Setup Authing with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chaining Authing MCP Server Operations

Your agent calls `get_user` to pull profile data before passing that exact output into a downstream database update. That is the core value of wiring this identity platform into your framework. You build multi-step reasoning pipelines where the system decides what to do next based on intermediate results. Grab the full list of permissions using `list_resources` and feed it directly into a prompt template. LangSmith tracks every tool invocation, showing you exactly how many tokens the authorization check consumed. You get full visibility into the execution path without writing custom logging logic.

Automated Security Audits

Fetching access records happens through the `get_audit_logs` tool. Your ReAct agent pulls the last 100 login attempts and evaluates them against known threat patterns. Instead of manually clicking through a dashboard, you write a chain that runs this check every hour. Combine this with `get_security_settings` to verify password complexity rules match your compliance requirements. If the agent detects a mismatch, it triggers an alert. The logic lives entirely in your code, while the execution relies on standard MCP connections.

Provisioning at Scale

Creating accounts manually wastes time, so you hand `create_user` to your autonomous script. The system parses an incoming CSV, maps the fields, and fires off the creation requests sequentially. You just watch the trace logs fill up with successful operations. Assigning permissions requires knowing what exists, which means calling `list_roles` and `list_groups` first. The agent checks the current directory structure, finds the right group ID, and attaches it to the new hire. Everything happens in one continuous execution loop.

Setup guide

Set up Authing 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 Authing 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({
    "authing-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 Authing transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Authing. 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.

Connect Authing now

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 Authing MCP in LangChain

Run `pip install langchain-mcp-adapters langgraph` first. Then initialize a `MultiServerMCPClient` pointing to your endpoint URL. Call `client.get_tools()` and pass the resulting array into your agent constructor.

Yes, LangSmith handles this automatically. Every call to the identity tools shows up in your tracing dashboard with exact latency and token metrics. You see the raw inputs and outputs for debugging.

The protocol gives your agent an immediate, typed understanding of the available operations. You skip writing boilerplate HTTP wrappers and schema definitions. The LLM knows exactly what parameters to send.

The connection starts stateless. If you need to remember context across multiple tool calls, wrap your execution in `client.session()`. That keeps the context window intact for complex provisioning workflows.

Vinkius runs this connection inside an ephemeral V8 Isolate Sandbox. When your chain pulls email addresses or audit logs, the execution environment destroys itself immediately after the run. Zero trust architecture means no persistent storage of your directory information outside your own environment.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript