How to Use the Arize AI MCP in LangChain

Q: Can I use this to compare models?

Yes. You can build a chain that calls listexperiments for two different models, then getmodel for each to get their performance metrics. Your agent can then compare the results and decide which model is better based on your criteria.

Build self-correcting ML observability chains in LangChain. Your agent can now monitor model performance and debug issues on its own.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Arize AI MCP to LangChain

Create your Vinkius account to connect Arize AI 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 Arize AI with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate Model Health Checks

Your LangChain agent can now run diagnostics on its own. Build a chain that starts by `list_projects` to find the right model, then uses `list_experiments` to check recent runs. If it finds a problem, it can dig deeper. The next step in the chain could be `get_model` to check its configuration, or `list_spans` to pinpoint specific prediction failures. Your agent isn't just fetching data; it's following a logical troubleshooting sequence you define.

Chain Together Root Cause Analysis for LangChain

Stop manually digging through dashboards. Create an agent that chains Arize AI tools to find the source of model degradation. It can `list_datasets`, compare them, and then use `create_dataset` to isolate a problematic data slice for further testing. Because LangChain passes outputs between steps, your agent can correlate a drop in performance from one tool's output with specific data drift identified by another. It's a direct line from problem to cause, executed in a single chain.

Create Dynamic Monitoring Workflows

This MCP Server gives your agents the tools to manage Arize AI directly. You can build agents that don't just read data, but also write it. For example, an agent can validate a new dataset and then formally register it using `create_dataset`. This makes your MLOps pipelines interactive. Your agent can decide, based on performance metrics fetched with `get_model` or `list_experiments`, whether to promote a model or flag it for review, all within the same execution chain.

Setup guide

Set up Arize AI 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 Arize AI 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({
    "arize-ai-alternative-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 Arize AI 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 Arize AI. 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 Arize AI 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 Arize AI MCP in LangChain

You give the Arize AI tools to your LangChain agent. The agent can then call tools like `list_projects` or `get_model` as steps in a chain to monitor your ML models. It connects your agent's logic directly to your observability platform.

Yes. You can build a chain that calls `list_experiments` for two different models, then `get_model` for each to get their performance metrics. Your agent can then compare the results and decide which model is better based on your criteria.

The main benefit is automated, sequential troubleshooting. A LangChain agent can run a whole diagnostic workflow on its own—listing projects, checking experiments, and inspecting model spans—linking each step together without manual intervention.

No, it's straightforward. You install the MCP adapter, point it to the server URL, and call `get_tools()`. The tools are then ready to be passed into your agent constructor, just like any other LangChain tool.

Your data, including model metadata, project names, and experiment details, is sent over a secure connection to the MCP Server. Vinkius runs each server in an isolated sandbox, and your unique token authenticates every request. The server itself is ephemeral and doesn't store your data after the operation is complete.

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