Installation
pip install pandaprobe[openai]
Setup
from pandaprobe.wrappers import wrap_openai
from openai import OpenAI
client = wrap_openai(OpenAI())
from pandaprobe.wrappers import wrap_openai
from openai import AsyncOpenAI
async_client = wrap_openai(AsyncOpenAI())
wrap_openai entry point.
Chat Completions API
Span name: "openai-chat", SpanKind: LLM
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain quantum computing."},
],
temperature=0.7,
)
- Input: messages array
- Output: assistant message
- Model name
- Token usage:
prompt_tokens, completion_tokens, total_tokens, plus detail fields (for example reasoning_tokens from completion_tokens_details)
- Model parameters:
temperature, top_p, max_tokens, and other safe parameters only
Streaming
stream = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello!"}],
stream=True,
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")
completion_start_time on the first chunk for time-to-first-token tracking. Chunks are reduced to a single response for the span output.
Responses API
Span name: "openai-response", SpanKind: LLM
response = client.responses.create(
model="gpt-4o",
instructions="You are a helpful assistant.",
input="What is the capital of France?",
)
- Input:
instructions plus input, normalized to messages format
- Output: response output items
- Token usage:
input_tokens mapped to prompt tokens, output_tokens mapped to completion tokens, plus detail fields
- Reasoning summaries extracted from reasoning output items
- Model parameters:
max_output_tokens, temperature, top_p, reasoning, and related fields
Built-in tools such as web_search, file_search, and code_interpreter are automatically traced as child spans with SpanKind TOOL:
response = client.responses.create(
model="gpt-4o",
input="Search the web for PandaProbe",
tools=[{"type": "web_search"}],
)
TOOL span with the tool type as the span name (for example "web_search_call", "function_call").
Function calls (function_call items) are also captured as TOOL child spans with arguments as input and results as output.
Token usage mapping
| OpenAI Field | PandaProbe Field |
|---|
prompt_tokens | prompt_tokens |
completion_tokens | completion_tokens |
total_tokens | total_tokens |
completion_tokens_details.reasoning_tokens | reasoning_tokens |
(Responses) input_tokens | prompt_tokens |
(Responses) output_tokens | completion_tokens |
(Responses) input_tokens_details.cached_tokens | cache_read_tokens |
(Responses) output_tokens_details.reasoning_tokens | reasoning_tokens |
Chat Completions and Responses return different usage object shapes from the SDK. The wrapper normalizes both into the PandaProbe fields in this table; do not assume raw OpenAI field names are identical across APIs when reading span payloads in custom exporters.
