Installation
pip install pandaprobe[openai-agents]
Setup
from pandaprobe.integrations.openai_agents import OpenAIAgentsAdapter
adapter = OpenAIAgentsAdapter(
session_id="conversation-123",
user_id="user-abc",
tags=["production"],
)
adapter.instrument()
Unlike other integrations that use monkey-patching, the OpenAI Agents SDK integration registers a TracingProcessor via the SDK’s first-class callback interface. This is the cleanest integration pattern.
Usage
from agents import Agent, Runner
agent = Agent(
name="my-assistant",
instructions="You are a helpful assistant.",
model="gpt-4o",
)
result = Runner.run_sync(agent, "What is PandaProbe?")
print(result.final_output)
What gets traced
| SDK Span Type | PandaProbe Kind | Description |
|---|
agent | AGENT | Agent execution with tools, handoffs, output_type metadata |
handoff | AGENT | Agent handoff with from_agent / to_agent metadata |
response | LLM | Responses API call with input, output, model, usage, reasoning |
generation | LLM | Chat Completions call with messages, model, usage |
function | TOOL | Function tool call with JSON-parsed input/output |
guardrail | OTHER | Guardrail check with triggered metadata |
custom | OTHER | Custom span with custom_data metadata |
I/O propagation
The adapter automatically propagates input/output from LLM spans to their parent agent and the root trace:
- First LLM input becomes the agent span’s input
- Last LLM output becomes the agent span’s output
- Last user message becomes the trace input; last assistant message becomes the trace output
from agents import Agent, Runner, function_tool
@function_tool
def get_weather(city: str) -> str:
"""Get the weather for a city."""
return f"Sunny, 72F in {city}"
agent = Agent(
name="weather-assistant",
instructions="Help users check the weather.",
model="gpt-4o",
tools=[get_weather],
)
result = Runner.run_sync(agent, "What's the weather in Tokyo?")
CHAIN (root) → AGENT (weather-assistant) → LLM (response) → TOOL (get_weather) → LLM (final response).
Token usage mapping
| OpenAI Agents Field | PandaProbe Field |
|---|
input_tokens / prompt_tokens | prompt_tokens |
output_tokens / completion_tokens | completion_tokens |
total_tokens | total_tokens |
input_tokens_details.cached_tokens | cache_read_tokens |
output_tokens_details.reasoning_tokens | reasoning_tokens |
