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Valiqor automatically traces all OpenAI chat.completions.create calls — both sync and async. Every call is recorded as a span with model name, token usage, cost, and full message content.

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Install

pip install valiqor[openai]
This installs valiqor plus openai>=1.0.0.
Add a single import at the top of your app — all OpenAI calls are automatically traced: 
import valiqor.auto  # ← Add this line

import openai

client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)
print(response.choices[0].message.content)
That’s it. Every chat.completions.create call is now traced with full metadata.

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Selective Instrumentation

If you only want OpenAI tracing (not other providers), use the provider-specific function: 
from valiqor.trace import openai_autolog

openai_autolog()

# Or using the namespace-style API:
from valiqor.trace import OpenAI
OpenAI.autolog()
Both are equivalent — they enable tracing only for OpenAI.

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Async Support

Async OpenAI calls are automatically traced too: 
import valiqor.auto
import openai

client = openai.AsyncOpenAI()

async def main():
    response = await client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Explain quantum computing"}]
    )
    print(response.choices[0].message.content)

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Tool / Function Calls

Tool calls are captured automatically. Each tool call in the response is recorded with the function name, call ID, and parsed arguments: 
import valiqor.auto
import openai

client = openai.OpenAI()

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the weather for a city",
            "parameters": {
                "type": "object",
                "properties": {"city": {"type": "string"}},
                "required": ["city"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools
)
# Tool calls are automatically captured in the trace span

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What Gets Captured

Each traced OpenAI call records:
FieldDescription
modelModel name (e.g. gpt-4o, gpt-4o-mini)
prompt_tokensInput token count
completion_tokensOutput token count
total_tokensCombined token count
costEstimated cost in USD
system_fingerprintOpenAI system fingerprint
messagesUser and assistant messages
tool_callsFunction name, ID, and arguments (if any)
duration_msCall latency
statusSuccess or error

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With Workflows

Combine with trace_workflow to group multiple OpenAI calls into a single trace: 
import valiqor.auto
from valiqor.trace import trace_workflow
import openai

client = openai.OpenAI()

with trace_workflow("research-assistant"):
    # Step 1: Generate outline
    outline = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Create an outline about AI safety"}]
    )

    # Step 2: Write each section
    content = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "user", "content": f"Write a detailed section based on: {outline.choices[0].message.content}"}
        ]
    )
Both calls appear as child spans under the research-assistant trace.

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Disabling

To disable OpenAI tracing: 
from valiqor.trace import disable_autolog

disable_autolog("openai")    # Disable OpenAI only
disable_autolog()            # Disable all providers

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Limitations

  • Streaming is not currently instrumented — streamed responses are not captured in traces
  • Embeddings (client.embeddings.create) are not traced — only chat.completions.create is instrumented

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Next Steps

Tracing Guide

Learn about traces, spans, workflows, and exporters.

Failure Analysis

Run failure analysis on your traced OpenAI calls.