openinference-instrumentation-openai-agents 1.6.2

OpenInference OpenAI Agents Instrumentation

OpenInference OpenAI Agents Instrumentation

Python auto-instrumentation library for OpenAI Agents python SDK.

The traces emitted by this instrumentation are fully OpenTelemetry compatible and can be sent to an OpenTelemetry collector for viewing, such as Arize Phoenix or Arize AX.

Installation

pip install openinference-instrumentation-openai-agents

Quickstart

In this example we will instrument a small program that uses OpenAI and observe the traces via arize-phoenix.

Install packages.

pip install openinference-instrumentation-openai-agents arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp

Start the phoenix server so that it is ready to collect traces. The Phoenix server runs entirely on your machine and does not send data over the internet.

phoenix serve

In a python file, set up the OpenAIAgentsInstrumentor and configure the tracer to send traces to Phoenix.

from agents import Agent, Runner
from openinference.instrumentation.openai_agents import OpenAIAgentsInstrumentor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor

endpoint = "http://127.0.0.1:6006/v1/traces"
tracer_provider = trace_sdk.TracerProvider()
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))
# Optionally, you can also print the spans to the console.
tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))

OpenAIAgentsInstrumentor().instrument(tracer_provider=tracer_provider)


agent = Agent(name="Assistant", instructions="You are a helpful assistant")
result = Runner.run_sync(agent, "Write a haiku about recursion in programming.")
print(result.final_output)

Since we are using OpenAI, we must set the OPENAI_API_KEY environment variable to authenticate with the OpenAI API.

export OPENAI_API_KEY=your-api-key

Now simply run the python file and observe the traces in Phoenix.

python your_file.py

Realtime audio

OpenAIAgentsInstrumentor().instrument(...) also traces agents.realtime.RealtimeSession (the OpenAI Agents SDK's voice/audio runtime) when the realtime extras are installed. No additional setup is required — instrument(...) applies the realtime patches whenever agents.realtime is importable.

For each turn the instrumentor produces this span tree:

AUDIO   "conversation.turn"     ← parent; aggregated input/output transcripts, llm.model_name, llm.invocation_parameters
├─ USER  "user"                 ← input.audio.url (WAV data URI), input.audio.transcript, or input.value for text input
├─ LLM   "assistant"            ← output.audio.url, output.audio.transcript, token counts, time_to_first_token_ms
│  └─ TOOL "<tool_name>"        ← one per function call within the turn
└─ ...                          ← additional USER / LLM siblings for split input or tool round-trips

A runnable mic/speaker example with two function tools lives at examples/realtime_with_tools.py.

Audio redaction

The realtime instrumentor recognizes three environment variables for redacting captured audio:

• OPENINFERENCE_HIDE_INPUT_AUDIO — when truthy (1 / true / yes / on), drops input.audio.url, input.audio.mime_type, and input.audio.transcript from USER spans. Default: false.
• OPENINFERENCE_HIDE_OUTPUT_AUDIO — same shape, drops the output.audio.* attributes from LLM spans. Default: false.
• OPENINFERENCE_BASE64_AUDIO_MAX_LENGTH — caps the base64 payload length of audio data: URIs. The data:audio/wav;base64, prefix is always preserved. Default: 32000.

TraceConfig(hide_inputs=True) and TraceConfig(hide_outputs=True) also cascade to the corresponding audio attributes.

More Info

• More info on OpenInference and Phoenix
• More info on OpenInference and Arize AX
• How to customize spans to track sessions, metadata, etc.
• How to account for private information and span payload customization