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Vinkius

Arize AI Connector for AI agents.

6 live capabilities

Monitor LLM performance and track data drift in real-time.

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AI Agent

Why people use Arize AI

Arize AI for ML Observability and Drift Tracking

This Connector cuts that cycle. You can ask your agent to pull specific metrics or list your environments directly in your workspace. You get a real-time view of your model's health without ever opening a separate tab.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get a hands-off way to manage ML observability through natural conversation.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Detecting Sudden Drift

    An engineer notices weird outputs and asks the agent to check `get_metrics` for the production model to see if data drift is the cause.

  2. Real-world use case 02

    Safety and Toxicity Audits

    A PM asks the agent to `list_evals` to see if the latest batch of prompts passed the toxicity and hallucination checks.

  3. Real-world use case 03

    Automated Log Ingestion

    A developer wants to push a batch of mocked responses and uses `ingest_log` to send them to Arize without opening a browser.

Complete set · 6capabilities

The complete Arize AI capability set.

These are the exact actions your AI can choose when you ask it to work with Arize AI.

Capability set01 / 02

01—03

3 capabilities in this set.

Part of 6 available through Arize AI.

  1. 01 Capability

    List datasets

    List all static evaluation datasets available to you. This helps you see what benchmarks are ready for testing.

  2. 02 Capability

    Create dataset

    Create a dataset

  3. 03 Capability

    Get model

    Fetch details and metadata for a specific tracked model. You can see the inputs, outputs, and features for any model in your space.

Capability set02 / 02

04—06

3 capabilities in this set.

Part of 6 available through Arize AI.

  1. 04 Capability

    List experiments

    List experiments

  2. 05 Capability

    List projects

    List projects

  3. 06 Capability

    List spans

    List spans

Set up in minutes

One URL. Then ask Arize AI to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Arize AI from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_wKJSgGsb2EgJmks5PzM3V0jknb7nFZgbwB0CaRFP/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Arize AI, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Arize AI for the conversation.

Where the request belongs

Work Arize AI can move forward.

Built around the request

The ML engineer who's tired of manually pushing telemetry logs and checking for drift in a separate tab, or the PM who needs to monitor output toxicity across five different LLM integrations.

01

ML Engineer

Uses the Connector to push inference telemetry and check for performance degradation flags without leaving the terminal.

02

AI Product Manager

Monitors output toxicity, drift rates, and usage metrics across multiple LLM integrations to ensure safety.

03

Data Scientist

Manages baseline evaluation datasets and triggers custom scoring loops asynchronously during model development.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
Arize AI logo
01 6 capabilities

Arize AI

Monitor ML model performance, detect data drift, and troubleshoot prediction quality with real-time observability dashboards.

View Connector
DataRobot logo
02 6 capabilities

DataRobot

Manage AutoML via DataRobot. monitor projects and models, track deployments, and audit ML datasets directly from any AI agent.

View Connector
H2O.ai logo
03 6 capabilities

H2O.ai

Manage AI models via H2O.ai. track data frames, monitor machine learning models and training jobs, and audit cloud cluster status directly from any AI agent.

View Connector
Abacus AI (Enterprise AI Cloud) logo
04 8 capabilities

Abacus AI (Enterprise AI Cloud)

Manage the full machine learning lifecycle via Abacus AI. create projects, train models, and deploy real-time prediction endpoints.

View Connector
Comet ML logo
05 6 capabilities

Comet ML

Manage machine learning experiments via Comet. track model metrics, audit project workspaces, and inspect ML run parameters directly from any AI agent.

View Connector
Modelbit (ML Model Deployments) logo
06 1 capability

Modelbit (ML Model Deployments)

Deploy and call machine learning models directly from your AI agent using Modelbit's inference endpoints.

View Connector

Bring your own AI

Change the model, client or framework. Keep Arize AI connected.

  • Claude
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  • Continue
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  • Pieces
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  • TypingMind
  • Chorus
  • 5ire
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  • LangChain
  • LlamaIndex
  • CrewAI
  • Vercel AI SDK

Before you connect

Questions about Arize AI.

The practical details behind the request, access and result.

How does Arize AI help with LLM hallucination?

It allows you to run automated evaluations against your logs and static datasets. You can use your agent to trigger these checks and see if your model is producing accurate or hallucinated content.

Can I use Arize AI to monitor data drift?

Yes. You can ask your agent to fetch real-time metrics for any tracked model to see if your data quality or prediction distributions have shifted.

How do I push logs to Arize using an agent?

You can simply tell your agent to push specific telemetry logs or prediction data. It will structure the logs correctly and send them to your Arize platform for analysis.

Does Arize AI support multi-environment tracking?

Yes, it can list and manage different environments like production, training, and verification. This helps you keep your monitoring organized across different deployment stages.

Can my agent run automated evaluations?

Absolutely. You can trigger custom evaluation runs for things like toxicity, PII filtering, and hallucination checks directly through a natural language command.

How do I see which models are currently tracked?

Just ask your agent to list your models. It will pull a list of all ML models and LLMs currently being monitored in your Arize spaces.

Can my AI automatically trigger a hallucination evaluation on a new dataset?

Yes! You can ask your agent to retrieve the specific Ground Truth dataset ID, formulate a testing payload, and invoke the run_eval capability natively. Arize will process the asynchronous scoring internally and log the evaluation securely.

How can I quickly check if a production model is experiencing data drift?

Just tell your agent: 'Fetch the primary metrics for model X'. The AI uses the get_metrics query to immediately surface latency degradation, prediction drift flags, and incoming data quality indexes without opening the browser.

Is it possible to track telemetry simultaneously for both local development and production environments?

Absolutely. Arize enforces strict separation using Spaces and Environments. You can instruct your AI agent to query the list_environments capability, figure out the sandbox ID, and push manual test logs strictly to the sandbox scope during debugging sessions, keeping production metrics clean.

How do I find my Arize API Key?

Log in to your account, navigate to Settings > API, and generate or copy your unique secret key.

Can I track model drift via AI?

Yes! Use the list_experiments capability to retrieve data on active model evaluations and track performance variations programmatically.

How do I retrieve telemetry traces?

Use the list_spans capability to retrieve high-fidelity execution spans and traces for your ML projects directly from the platform.

One connection away

Give your agent a direct line to Arize AI.

Connect Arize AI once. Keep it beside 5,900+ managed Connectors when the next task needs more.

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