Module @arizeai/phoenix-otel

@arizeai/phoenix-otel

A lightweight wrapper around OpenTelemetry for Node.js applications that simplifies sending traces to Arize Phoenix. @arizeai/phoenix-otel handles provider registration and OTLP export, then re-exports the full @arizeai/openinference-core helper surface from the same package path so you can register tracing and author manual spans from one import.

Note: This package is under active development and APIs may change.

Features

Simple Setup - One-line configuration with sensible defaults
Environment Variables - Automatic configuration from environment variables
Batch Processing - Built-in batch span processing for production use
OpenInference Helpers Included - Re-exports withSpan, the span-kind wrappers (traceChain, traceAgent, traceTool, traceLLM, traceRetriever, traceReranker, traceEmbedding, traceGuardrail, traceEvaluator, tracePrompt), observe, context setters, attribute builders, OITracer, and utility helpers
Provider-Swap Safe Wrappers - The re-exported OpenInference helpers resolve the default tracer when the wrapped function executes, so module-scoped wrappers continue following global provider changes
Agent-Friendly Local Docs - Ships curated docs and source in node_modules/@arizeai/phoenix-otel/

Installation

npm install @arizeai/phoenix-otel

Requires Node.js 18 or newer. Both ESM and CommonJS entry points are provided.

Quick Start

Basic Usage

The simplest way to get started is to use register() together with the built-in tracing helpers:

import { register, traceChain } from "@arizeai/phoenix-otel";

const provider = register({
  projectName: "my-app",
});

const answerQuestion = traceChain(
  async (question: string) => `Handled: ${question}`,
  { name: "answer-question" }
);

await answerQuestion("What is Phoenix?");
await provider.shutdown();

register() sets up the Phoenix exporter. The tracing helpers come from @arizeai/openinference-core, re-exported through @arizeai/phoenix-otel.

Production Setup

For production use with Phoenix Cloud:

import { register } from "@arizeai/phoenix-otel";

register({
  projectName: "my-app",
  url: "https://app.phoenix.arize.com",
  apiKey: process.env.PHOENIX_API_KEY,
});

Configuration

Environment Variables

The register function automatically reads from environment variables:

# For local Phoenix server (default)
export PHOENIX_COLLECTOR_ENDPOINT="http://localhost:6006"

# For Phoenix Cloud
export PHOENIX_COLLECTOR_ENDPOINT="https://app.phoenix.arize.com"
export PHOENIX_API_KEY="your-api-key"

When a setting is not provided by argument or environment variable, register also looks for a .env.phoenix file in the current working directory — walking up toward the filesystem root and stopping at the first match — and reads PHOENIX_-prefixed keys from it (dotenv format). Environment variables always take precedence over the file; set PHOENIX_DISCOVER_CONFIG=false to disable discovery entirely.

PHOENIX_API_KEY and PHOENIX_CLIENT_HEADERS are resolved as one credential group. Explicit Authorization headers are preserved case-insensitively. If an explicit or process credential is paired with PHOENIX_COLLECTOR_ENDPOINT from the file, the exporter warns once and continues without logging credential values.

Configuration Options

The register function accepts the following parameters:

Parameter	Type	Default	Description
`projectName`	`string`	`"default"`	The project name for organizing traces in Phoenix
`url`	`string`	`"http://localhost:6006"`	The URL to your Phoenix instance
`apiKey`	`string`	`undefined`	API key for Phoenix authentication
`headers`	`Record<string, string>`	`{}`	Custom headers for OTLP requests
`batch`	`boolean`	`true`	Use batch span processing (recommended for production)
`instrumentations`	`Instrumentation[]`	`undefined`	Array of OpenTelemetry instrumentations to register
`global`	`boolean`	`true`	Register the tracer provider globally
`diagLogLevel`	`DiagLogLevel`	`undefined`	Diagnostic logging level for debugging

Custom Span Processors

register() also accepts spanProcessors, which replaces the default Phoenix exporter setup. For these setups the package root re-exports OTLPTraceExporter, and the ESM-only @arizeai/phoenix-otel/vercel subpath re-exports @arizeai/openinference-vercel (OpenInferenceSimpleSpanProcessor, OpenInferenceBatchSpanProcessor, isOpenInferenceSpan, and types) — no need to install the underlying packages:

import {
  ensureCollectorEndpoint,
  OTLPTraceExporter,
  register,
} from "@arizeai/phoenix-otel";
import {
  isOpenInferenceSpan,
  OpenInferenceSimpleSpanProcessor,
} from "@arizeai/phoenix-otel/vercel";

register({
  projectName: "my-agent",
  spanProcessors: [
    new OpenInferenceSimpleSpanProcessor({
      exporter: new OTLPTraceExporter({
        url: ensureCollectorEndpoint("http://localhost:6006"),
      }),
      // Export only AI spans, re-rooting any left orphaned by the filter
      spanFilter: isOpenInferenceSpan,
      reparentOrphanedSpans: true,
    }),
  ],
});

Usage Examples

With the Vercel AI SDK (v7+)

AI SDK v7 telemetry registration is process-global, so applications configure it explicitly. Install ai and @ai-sdk/otel, then register the integration alongside the Phoenix provider. Request-header capture is disabled below because headers can contain authorization tokens and cookies.

// instrumentation.ts
import { OpenTelemetry } from "@ai-sdk/otel";
import { registerTelemetry } from "ai";
import { register } from "@arizeai/phoenix-otel";

const provider = register({
  projectName: "my-ai-app",
});

registerTelemetry(
  new OpenTelemetry({
    tracer: provider.getTracer("@arizeai/phoenix-otel/ai-sdk"),
    headers: false,
  })
);

// main.ts
import "./instrumentation.ts";
import { openai } from "@ai-sdk/openai";
import { generateText } from "ai";

const result = await generateText({
  model: openai("gpt-4o-mini"),
  prompt: "Write a short story about a cat.",
});

Note: AI SDK v6 and older emit a different span shape that is not supported by the bundled span processors. Use @arizeai/phoenix-otel 1.x (with @arizeai/openinference-vercel 2.x) for AI SDK v6.

With Auto-Instrumentation

Automatically instrument common libraries (works best with CommonJS):

import { register } from "@arizeai/phoenix-otel";
import { HttpInstrumentation } from "@opentelemetry/instrumentation-http";
import { ExpressInstrumentation } from "@opentelemetry/instrumentation-express";

register({
  projectName: "my-express-app",
  instrumentations: [new HttpInstrumentation(), new ExpressInstrumentation()],
});

Note: Auto-instrumentation via the instrumentations parameter works best with CommonJS projects. ESM projects require manual instrumentation.

With OpenAI (ESM)

For ESM projects, manually instrument libraries:

// instrumentation.ts
import { register, registerInstrumentations } from "@arizeai/phoenix-otel";
import OpenAI from "openai";
import { OpenAIInstrumentation } from "@arizeai/openinference-instrumentation-openai";

register({
  projectName: "openai-app",
});

// Manual instrumentation for ESM
const instrumentation = new OpenAIInstrumentation();
instrumentation.manuallyInstrument(OpenAI);

registerInstrumentations({
  instrumentations: [instrumentation],
});

// main.ts
import "./instrumentation.ts";
import OpenAI from "openai";

const openai = new OpenAI();

const response = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Hello!" }],
});

Tracing Helpers

The package includes withSpan plus span-kind shorthands — traceChain, traceAgent, and traceTool — for wrapping functions with OpenInference spans. Each helper automatically records inputs, outputs, errors, and span kind.

import {
  register,
  traceAgent,
  traceChain,
  traceTool,
  withSpan,
} from "@arizeai/phoenix-otel";

register({ projectName: "my-app" });

// traceTool — for tool calls and API lookups
const searchDocs = traceTool(
  async (query: string) => {
    const response = await fetch(`/api/search?q=${query}`);
    return response.json();
  },
  { name: "search-docs" }
);

// traceChain — for pipeline steps and orchestration
const summarize = traceChain(
  async (text: string) => `Summary of ${text.length} chars`,
  { name: "summarize" }
);

// traceAgent — for autonomous agent entry points
const supportAgent = traceAgent(
  async (question: string) => {
    const docs = await searchDocs(question);
    return summarize(JSON.stringify(docs));
  },
  { name: "support-agent" }
);

// withSpan — general purpose, specify kind explicitly
const retrieveDocs = withSpan(
  async (query: string) =>
    fetch(`/api/search?q=${query}`).then((r) => r.json()),
  { name: "retrieve-docs", kind: "RETRIEVER" }
);

These helpers resolve the default tracer when the wrapped function runs, so traced functions defined at module scope keep following global provider changes.

Additional Span-Kind Wrappers

The full set of OpenInference span kinds is covered by matching wrappers, each a shorthand for withSpan(fn, { ...options, kind }). They accept the same options as traceChain/traceAgent/traceTool (everything except kind, which is pre-set). These wrappers are marked @experimental in @arizeai/openinference-core.

Wrapper	Span kind	Use it for
`traceLLM`	`LLM`	Language-model invocations — chat/text completions and other inference calls
`traceRetriever`	`RETRIEVER`	Fetching documents from a knowledge base, vector store, or search index (RAG)
`traceReranker`	`RERANKER`	Reordering or scoring candidate documents by relevance
`traceEmbedding`	`EMBEDDING`	Converting text or data into vector representations
`traceGuardrail`	`GUARDRAIL`	Safety, validation, or policy checks (moderation, PII, compliance)
`traceEvaluator`	`EVALUATOR`	Scoring output quality — relevance, correctness, or LLM-as-a-judge
`tracePrompt`	`PROMPT`	Constructing, rendering, or templating a prompt before a model call

import {
  traceEmbedding,
  traceEvaluator,
  traceGuardrail,
  traceLLM,
  tracePrompt,
  traceReranker,
  traceRetriever,
} from "@arizeai/phoenix-otel";

const retrieveDocuments = traceRetriever(
  async (query: string) => vectorStore.similaritySearch(query, 5),
  { name: "vector-search" }
);

const evaluateAnswer = traceEvaluator(
  async (question: string, answer: string) => judge.score({ question, answer }),
  { name: "answer-evaluation" }
);

Custom Input And Output Processing

Use processInput and processOutput when you want richer OpenInference attributes than the default JSON-serialized input.value and output.value.

import {
  OpenInferenceSpanKind,
  getInputAttributes,
  getRetrieverAttributes,
  withSpan,
} from "@arizeai/phoenix-otel";

const retrieveDocs = withSpan(
  async (query: string) => [`Doc A for ${query}`, `Doc B for ${query}`],
  {
    name: "retrieve-docs",
    kind: OpenInferenceSpanKind.RETRIEVER,
    processInput: (query) => getInputAttributes(query),
    processOutput: (documents) =>
      getRetrieverAttributes({
        documents: documents.map((content, index) => ({
          id: `doc-${index}`,
          content,
        })),
      }),
  }
);

Context Attributes

Propagate session IDs, user IDs, metadata, and tags to all child spans using context setters:

import {
  context,
  register,
  setMetadata,
  setSession,
  setUser,
  traceChain,
} from "@arizeai/phoenix-otel";

register({ projectName: "my-app" });

const handleQuery = traceChain(async (query: string) => `Handled: ${query}`, {
  name: "handle-query",
});

// All spans inside context.with() inherit session, user, and metadata
await context.with(
  setMetadata(
    setUser(setSession(context.active(), { sessionId: "sess-123" }), {
      userId: "user-456",
    }),
    { environment: "production" }
  ),
  () => handleQuery("Hello")
);

Available setters: setSession, setUser, setMetadata, setTags, setAttributes, setPromptTemplate.

If you create spans manually with a plain OpenTelemetry tracer, copy the propagated context attributes onto the span explicitly:

import {
  context,
  getAttributesFromContext,
  register,
  trace,
} from "@arizeai/phoenix-otel";

register({ projectName: "my-app" });

const tracer = trace.getTracer("manual-tracer");
const span = tracer.startSpan("manual-span");
span.setAttributes(getAttributesFromContext(context.active()));
span.end();

Decorators

observe wraps class methods with tracing while preserving method this context. Use TypeScript 5+ standard decorators.

import { OpenInferenceSpanKind, observe } from "@arizeai/phoenix-otel";

class ChatService {
  @observe({ kind: OpenInferenceSpanKind.CHAIN })
  async runWorkflow(message: string) {
    return `processed: ${message}`;
  }

  @observe({ name: "llm-call", kind: OpenInferenceSpanKind.LLM })
  async callModel(prompt: string) {
    return `model output for: ${prompt}`;
  }
}

Attribute Helper APIs

Use the attribute helpers when you want to build OpenInference-compatible span attributes directly:

import { getLLMAttributes, trace } from "@arizeai/phoenix-otel";

const tracer = trace.getTracer("llm-service");

tracer.startActiveSpan("llm-inference", (span) => {
  span.setAttributes(
    getLLMAttributes({
      provider: "openai",
      modelName: "gpt-4o-mini",
      inputMessages: [{ role: "user", content: "What is Phoenix?" }],
      outputMessages: [{ role: "assistant", content: "Phoenix is..." }],
      tokenCount: { prompt: 12, completion: 44, total: 56 },
      invocationParameters: { temperature: 0.2 },
    })
  );
  span.end();
});

Available helpers include:

getLLMAttributes
getEmbeddingAttributes
getRetrieverAttributes
getToolAttributes
getMetadataAttributes
getInputAttributes / getOutputAttributes
defaultProcessInput / defaultProcessOutput

Trace Config And Redaction

OITracer wraps an OpenTelemetry tracer and can redact or drop sensitive OpenInference attributes before spans are written:

import {
  OITracer,
  OpenInferenceSpanKind,
  trace,
  withSpan,
} from "@arizeai/phoenix-otel";

const tracer = new OITracer({
  tracer: trace.getTracer("my-service"),
  traceConfig: {
    hideInputs: true,
    hideOutputText: true,
    hideEmbeddingVectors: true,
    hideLLMTools: true,
    base64ImageMaxLength: 8_000,
  },
});

const safeLLMCall = withSpan(
  async (prompt: string) => `model response for ${prompt}`,
  {
    tracer,
    kind: OpenInferenceSpanKind.LLM,
    name: "safe-llm-call",
  }
);

Supported environment variables include:

OPENINFERENCE_HIDE_INPUTS
OPENINFERENCE_HIDE_OUTPUTS
OPENINFERENCE_HIDE_INPUT_MESSAGES
OPENINFERENCE_HIDE_OUTPUT_MESSAGES
OPENINFERENCE_HIDE_INPUT_IMAGES
OPENINFERENCE_HIDE_INPUT_TEXT
OPENINFERENCE_HIDE_OUTPUT_TEXT
OPENINFERENCE_HIDE_EMBEDDING_VECTORS
OPENINFERENCE_BASE64_IMAGE_MAX_LENGTH
OPENINFERENCE_HIDE_PROMPTS
OPENINFERENCE_HIDE_LLM_TOOLS

Raw OpenTelemetry Spans

For full control over attributes and timing, use the OpenTelemetry API directly:

import { register, trace, SpanStatusCode } from "@arizeai/phoenix-otel";

register({ projectName: "my-app" });

const tracer = trace.getTracer("my-service");

async function processOrder(orderId: string) {
  return tracer.startActiveSpan("process-order", async (span) => {
    try {
      span.setAttribute("order.id", orderId);
      const result = await fetchOrderDetails(orderId);
      span.setAttribute("order.status", result.status);
      return result;
    } catch (error) {
      span.recordException(error as Error);
      span.setStatus({ code: SpanStatusCode.ERROR });
      throw error;
    } finally {
      span.end();
    }
  });
}

Utility Helpers

The package also re-exports small utilities from @arizeai/openinference-core:

withSafety({ fn, onError? }) wraps a function and returns null on error
safelyJSONStringify(value) and safelyJSONParse(value) guard JSON operations

Development vs Production

Development (with debug logging):

import { DiagLogLevel, register } from "@arizeai/phoenix-otel";

register({
  projectName: "my-app-dev",
  url: "http://localhost:6006",
  batch: false, // Immediate span delivery for faster feedback
  diagLogLevel: DiagLogLevel.DEBUG,
});

Production (optimized for performance):

import { register } from "@arizeai/phoenix-otel";

register({
  projectName: "my-app-prod",
  url: "https://app.phoenix.arize.com",
  apiKey: process.env.PHOENIX_API_KEY,
  batch: true, // Batch processing for better performance
});

Custom Headers

Add custom headers to OTLP requests:

import { register } from "@arizeai/phoenix-otel";

register({
  projectName: "my-app",
  url: "https://app.phoenix.arize.com",
  headers: {
    "X-Custom-Header": "custom-value",
    "X-Environment": process.env.NODE_ENV || "development",
  },
});

Non-Global Provider

Use the provider explicitly without registering globally:

import { register } from "@arizeai/phoenix-otel";

const provider = register({
  projectName: "my-app",
  global: false,
});

// Use the provider explicitly
const tracer = provider.getTracer("my-tracer");

Docs And Source Code In `node_modules`

After install, a coding agent can inspect the exact versioned docs and implementation that shipped with the package:

node_modules/@arizeai/phoenix-otel/docs/
node_modules/@arizeai/phoenix-otel/src/

Because @arizeai/phoenix-otel re-exports @arizeai/openinference-core, the dependency docs are also useful local references:

node_modules/@arizeai/openinference-core/docs/
node_modules/@arizeai/openinference-core/src/

Coding Agent Skill

The Phoenix repo includes a phoenix-tracing skill that teaches coding agents (Claude Code, Cursor, etc.) how to instrument LLM applications with OpenInference tracing. Install it with:

npx skills add Arize-ai/phoenix --skill phoenix-tracing

Tracing helpers:

import {
  observe,
  traceAgent,
  traceChain,
  traceEmbedding,
  traceEvaluator,
  traceGuardrail,
  traceLLM,
  tracePrompt,
  traceReranker,
  traceRetriever,
  traceTool,
  withSpan,
} from "@arizeai/phoenix-otel";

Context attribute setters:

import {
  setAttributes,
  setMetadata,
  setPromptTemplate,
  setSession,
  setTags,
  setUser,
} from "@arizeai/phoenix-otel";

Attribute builders for rich span data:

import {
  defaultProcessInput,
  defaultProcessOutput,
  getEmbeddingAttributes,
  getLLMAttributes,
  getRetrieverAttributes,
  getToolAttributes,
} from "@arizeai/phoenix-otel";

Redaction and utility helpers:

import {
  OITracer,
  safelyJSONParse,
  safelyJSONStringify,
  withSafety,
} from "@arizeai/phoenix-otel";

The tracing helper wrappers resolve the default tracer when they run. That keeps spans attached to the current provider in experiments and in any workflow that swaps providers during process lifetime.

Documentation

Phoenix Documentation - Complete Phoenix documentation
@arizeai/phoenix-otel package docs - Curated website docs for this package
@arizeai/openinference-core package docs - Upstream helper and attribute-builder reference
OpenTelemetry JS - OpenTelemetry for JavaScript
OpenInference - OpenInference semantic conventions

Community

Join our community to connect with thousands of AI builders:

🌍 Join our Slack community
💡 Ask questions and provide feedback in the #phoenix-support channel
🌟 Leave a star on our GitHub
🐞 Report bugs with GitHub Issues
𝕏 Follow us on 𝕏
💼 Follow us on LinkedIn
🗺️ Check out our roadmap

Modules

config
createNoOpProvider
index
lazyOpenInferenceSpanProcessor
register
utils

Module @arizeai/phoenix-otel

@arizeai/phoenix-otel

Features

Installation

Quick Start

Basic Usage

Production Setup

Configuration

Environment Variables

Configuration Options

Custom Span Processors

Usage Examples

With the Vercel AI SDK (v7+)

With Auto-Instrumentation

With OpenAI (ESM)

Tracing Helpers

Additional Span-Kind Wrappers

Custom Input And Output Processing

Context Attributes

Decorators

Attribute Helper APIs

Trace Config And Redaction

Raw OpenTelemetry Spans

Utility Helpers

Development vs Production

Custom Headers

Non-Global Provider

Docs And Source Code In `node_modules`

Coding Agent Skill

Documentation

Community

Modules

Settings

On This Page

Module @arizeai/phoenix-otel

@arizeai/phoenix-otel

Features

Installation

Quick Start

Basic Usage

Production Setup

Configuration

Environment Variables

Configuration Options

Custom Span Processors

Usage Examples

With the Vercel AI SDK (v7+)

With Auto-Instrumentation

With OpenAI (ESM)

Tracing Helpers

Additional Span-Kind Wrappers

Custom Input And Output Processing

Context Attributes

Decorators

Attribute Helper APIs

Trace Config And Redaction

Raw OpenTelemetry Spans

Utility Helpers

Development vs Production

Custom Headers

Non-Global Provider

Docs And Source Code In node_modules

Coding Agent Skill

Documentation

Community

Modules

Settings

On This Page

Docs And Source Code In `node_modules`