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Arize Exporter

Arize provides observability platforms for AI applications through Phoenix (open-source) and Arize AX (enterprise). The Arize exporter sends AI traces using OpenTelemetry and OpenInference semantic conventions, compatible with any OpenTelemetry platform that supports OpenInference.

When to Use ArizeDirect link to When to Use Arize

Arize is ideal when you need:

  • OpenInference standards - Industry-standard semantic conventions for AI traces
  • Flexible deployment - Self-hosted Phoenix or managed Arize AX
  • OpenTelemetry compatibility - Works with any OTLP-compatible platform
  • Comprehensive AI observability - LLM traces, embeddings, and retrieval analytics
  • Open-source option - Full-featured local deployment with Phoenix

InstallationDirect link to Installation

npm install @mastra/arize

ConfigurationDirect link to Configuration

Phoenix SetupDirect link to Phoenix Setup

Phoenix is an open-source observability platform that can be self-hosted or used via Phoenix Cloud.

PrerequisitesDirect link to Prerequisites

  1. Phoenix Instance: Deploy using Docker or sign up at Phoenix Cloud
  2. Endpoint: Your Phoenix endpoint URL (ends in /v1/traces)
  3. API Key: Optional for unauthenticated instances, required for Phoenix Cloud
  4. Environment Variables: Set your configuration
.env
PHOENIX_ENDPOINT=http://localhost:6006/v1/traces  # Or your Phoenix Cloud URL
PHOENIX_API_KEY=your-api-key  # Optional for local instances
PHOENIX_PROJECT_NAME=mastra-service  # Optional, defaults to 'mastra-service'

Basic SetupDirect link to Basic Setup

src/mastra/index.ts
import { Mastra } from "@mastra/core";
import { ArizeExporter } from "@mastra/arize";

export const mastra = new Mastra({
  observability: {
    configs: {
      arize: {
        serviceName: process.env.PHOENIX_PROJECT_NAME || "mastra-service",
        exporters: [
          new ArizeExporter({
            endpoint: process.env.PHOENIX_ENDPOINT!,
            apiKey: process.env.PHOENIX_API_KEY,
            projectName: process.env.PHOENIX_PROJECT_NAME,
          }),
        ],
      },
    },
  },
});
info

Quick Start with Docker

Test locally with an in-memory Phoenix instance:

docker run --pull=always -d --name arize-phoenix -p 6006:6006 \
  -e PHOENIX_SQL_DATABASE_URL="sqlite:///:memory:" \
  arizephoenix/phoenix:latest

Set PHOENIX_ENDPOINT=http://localhost:6006/v1/traces and run your Mastra agent to see traces at localhost:6006.

Arize AX SetupDirect link to Arize AX Setup

Arize AX is an enterprise observability platform with advanced features for production AI systems.

PrerequisitesDirect link to Prerequisites

  1. Arize AX Account: Sign up at app.arize.com
  2. Space ID: Your organization's space identifier
  3. API Key: Generate in Arize AX settings
  4. Environment Variables: Set your credentials
.env
ARIZE_SPACE_ID=your-space-id
ARIZE_API_KEY=your-api-key
ARIZE_PROJECT_NAME=mastra-service  # Optional

Basic SetupDirect link to Basic Setup

src/mastra/index.ts
import { Mastra } from "@mastra/core";
import { ArizeExporter } from "@mastra/arize";

export const mastra = new Mastra({
  observability: {
    configs: {
      arize: {
        serviceName: process.env.ARIZE_PROJECT_NAME || "mastra-service",
        exporters: [
          new ArizeExporter({
            apiKey: process.env.ARIZE_API_KEY!,
            spaceId: process.env.ARIZE_SPACE_ID!,
            projectName: process.env.ARIZE_PROJECT_NAME,
          }),
        ],
      },
    },
  },
});

Configuration OptionsDirect link to Configuration Options

The Arize exporter supports advanced configuration for fine-tuning OpenTelemetry behavior:

Complete ConfigurationDirect link to Complete Configuration

new ArizeExporter({
  // Phoenix Configuration
  endpoint: "https://your-collector.example.com/v1/traces", // Required for Phoenix

  // Arize AX Configuration
  spaceId: "your-space-id", // Required for Arize AX

  // Shared Configuration
  apiKey: "your-api-key", // Required for authenticated endpoints
  projectName: "mastra-service", // Optional project name

  // Optional OTLP settings
  headers: {
    "x-custom-header": "value", // Additional headers for OTLP requests
  },

  // Debug and performance tuning
  logLevel: "debug", // Logging: debug | info | warn | error
  batchSize: 512, // Batch size before exporting spans
  timeout: 30000, // Timeout in ms before exporting spans

  // Custom resource attributes
  resourceAttributes: {
    "deployment.environment": process.env.NODE_ENV,
    "service.version": process.env.APP_VERSION,
  },
});

Batch Processing OptionsDirect link to Batch Processing Options

Control how traces are batched and exported:

new ArizeExporter({
  endpoint: process.env.PHOENIX_ENDPOINT!,
  apiKey: process.env.PHOENIX_API_KEY,

  // Batch processing configuration
  batchSize: 512, // Number of spans to batch (default: 512)
  timeout: 30000, // Max time in ms to wait before export (default: 30000)
});

Resource AttributesDirect link to Resource Attributes

Add custom attributes to all exported spans:

new ArizeExporter({
  endpoint: process.env.PHOENIX_ENDPOINT!,
  resourceAttributes: {
    "deployment.environment": process.env.NODE_ENV,
    "service.namespace": "production",
    "service.instance.id": process.env.HOSTNAME,
    "custom.attribute": "value",
  },
});

OpenInference Semantic ConventionsDirect link to OpenInference Semantic Conventions

This exporter implements the OpenInference Semantic Conventions for generative AI applications, providing standardized trace structure across different observability platforms.