Bring Ml Observability
to LangChain

Connect your Arize AI observability platform to any AI agent and take full control of your Machine Learning and LLM telemetry workflows through natural conversation.

What you can do

• Model Monitoring & Metrics — List all tracked ML models, extract deep configuration schemas, and fetch real-time metrics (performance, data quality, and prediction drift)
• Evaluation & Alignment — Launch and list automated LLM evaluation runs (e.g., Toxicity, Hallucination, PII filtering) against static datasets and ground truth baselines
• Telemetry Ingestion — Push programmatic raw logs, predictions, and inferences straight into Arize for immediate visualization and tracking
• Space & Environment Management — Browse organizational spaces and segregated deployment environments (Production, Training, Verification)

How it works

1. Subscribe to this server
2. Enter your Arize API Key and Space ID Key
3. Start monitoring your prediction health from Claude, Cursor, or any MCP-compatible client

No more context-switching into heavily graphical dashboards to figure out why an LLM prompt hallucinated. Your AI acts as a dedicated ML Ops engineer.

Who is this for?

• Machine Learning Engineers — rapidly push inference telemetry and query performance degradation flags without leaving your terminal
• AI Product Managers — instantly monitor output toxicity, drift rates, and usage metrics across multiple LLM integrations
• Data Scientists — manage baseline evaluation datasets and trigger custom scoring loops asynchronously