Setup OTEL

Configure OpenTelemetry tracing with Phoenix-aware defaults

The Phoenix OTEL SDK provides a lightweight wrapper around OpenTelemetry with sensible defaults for Phoenix.

Install

Python

pip install arize-phoenix-otel

TypeScript

npm install @arizeai/phoenix-otel

Configure

Set environment variables to connect to your Phoenix instance:

export PHOENIX_API_KEY="your-api-key"

# Local (default, no API key required)
export PHOENIX_COLLECTOR_ENDPOINT="http://localhost:6006"

# Phoenix Cloud
# export PHOENIX_COLLECTOR_ENDPOINT="https://app.phoenix.arize.com/s/your-space-name"

# Self-hosted
# export PHOENIX_COLLECTOR_ENDPOINT="https://your-phoenix-instance.com"

You can find your collector endpoint and API key in the Settings page of your Phoenix instance.

Register

Call register() to initialize tracing. The SDK automatically reads your environment variables.

Python

from phoenix.otel import register

tracer_provider = register(
    project_name="my-llm-app",
    auto_instrument=True,  # automatically instruments OpenAI, LangChain, etc.
)

Configuration options

Parameter	Description
project_name	Project name in Phoenix (or PHOENIX_PROJECT_NAME env var)
auto_instrument	Automatically instrument all supported libraries
batch	Process spans in batch (default: True, recommended for production)
endpoint	Custom collector endpoint URL
protocol	Transport protocol: "grpc" or "http/protobuf"
headers	Headers to send with each span payload

TypeScript

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

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

Configuration options

Parameter	Type	Default	Description
projectName	string	"default"	Project name in Phoenix
url	string	"http://localhost:6006"	Phoenix server URL
apiKey	string	—	API key for authentication
batch	boolean	true	Use batch span processing
headers	Record<string, string>	{}	Custom headers for OTLP requests
instrumentations	Instrumentation[]	—	Instrumentations to register
diagLogLevel	DiagLogLevel	—	Enable diagnostic logging

Instrument

Add instrumentation to capture traces from your LLM calls:

Python

With auto_instrument=True, Phoenix automatically discovers and activates all OpenInference instrumentor packages installed in your Python environment—no additional code required.

# Install instrumentors for your frameworks
pip install openinference-instrumentation-openai
pip install openinference-instrumentation-langchain
# ... any other OpenInference packages you need

Just pip install the instrumentation packages you need and set auto_instrument=True. Phoenix handles the rest.

See Integrations for all available packages, or use Tracing Helpers for manual instrumentation.

TypeScript

Install and register instrumentations for your framework:

npm install @arizeai/openinference-instrumentation-openai

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

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

const instrumentation = new OpenAIInstrumentation();
instrumentation.manuallyInstrument(OpenAI);

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

ESM projects require calling manuallyInstrument() on the client class. CommonJS projects can skip this step.

See Integrations for all available packages.

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Advanced Configuration

For more control over tracing behavior, see the SDK reference documentation:

Python arize-phoenix-otel

Batch processing, custom endpoints, gRPC/HTTP transport, sampling, and OTel primitives

TypeScript @arizeai/phoenix-otel

Diagnostic logging, custom headers, and full API reference

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Next Steps

Integrations

Browse auto-instrumentation packages

Tracing Helpers

Manual instrumentation with decorators