OpenAI

Instrument OpenAI API calls with automatic token counting and cost tracking using the AgentVista OpenAI adapter.

The OpenAIAdapter extracts telemetry from OpenAI responses — model name, token counts, and cost in USD — and passes them directly into agentvista.record() or a traced run. The adapter works with both the Chat Completions API (client.chat.completions.create) and the Responses API (client.responses.create), auto-detecting the response shape. Cost is calculated automatically from the model name using AgentVista’s built-in pricing table.

Setup

1. Install the SDK with the OpenAI extra

   pip install agentvista[openai]
   

   This installs agentvista along with the openai package.

2. Initialize AgentVista

   Call agentvista.init() once at application startup with your API key.

   import agentvista
   
   agentvista.init(api_key="av_xxxxx")
   
3. Import the adapter

   from agentvista.adapters.openai import OpenAIAdapter
   
   adapter = OpenAIAdapter()
   

Usage examples

Basic

Use adapter.extract() on any OpenAI response and unpack the result directly into agentvista.record().

from openai import OpenAI
import agentvista
from agentvista.adapters.openai import OpenAIAdapter

agentvista.init(api_key="av_xxxxx")
adapter = OpenAIAdapter()

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4.1",
    messages=[{"role": "user", "content": "Qualify this lead..."}],
)

# Extract model, input_tokens, output_tokens, total_tokens, cost_usd
telemetry = adapter.extract(response)
agentvista.record(agent="lead-qualifier", success=True, **telemetry)

adapter.extract() returns a dict with any of these keys present:

Key	Type	Description
model	str	Model ID returned by the API (e.g. "gpt-4.1")
input_tokens	int	Prompt tokens (or input_tokens from the Responses API)
output_tokens	int	Completion tokens (or output_tokens from the Responses API)
total_tokens	int	Sum of input and output tokens
cost_usd	float	Total cost in USD, rounded to 6 decimal places

The adapter automatically detects whether the response uses prompt_tokens / completion_tokens (Chat Completions) or input_tokens / output_tokens (Responses API) and normalizes both into the same output shape.

With tracing

Wrap your OpenAI call in agentvista.run() to record a full traced run with outcome signals.

from openai import OpenAI
import agentvista
from agentvista.adapters.openai import OpenAIAdapter

agentvista.init(api_key="av_xxxxx")
adapter = OpenAIAdapter()
client = OpenAI()

with agentvista.run("lead-qualifier") as r:
    response = client.chat.completions.create(
        model="gpt-4.1",
        messages=[{"role": "user", "content": "Qualify this lead..."}],
    )

    telemetry = adapter.extract(response)
    agentvista.record(agent="lead-qualifier", success=True, **telemetry)

    qualified = "yes" in response.choices[0].message.content.lower()
    r.set_outcome(success=qualified, outcome="qualified" if qualified else "not-qualified")

r.set_outcome(success, outcome) attaches a business-level result to the trace. The context manager automatically records duration and flushes the trace on exit.

Responses API

The adapter works identically with the OpenAI Responses API.

from openai import OpenAI
import agentvista
from agentvista.adapters.openai import OpenAIAdapter

agentvista.init(api_key="av_xxxxx")
adapter = OpenAIAdapter()
client = OpenAI()

response = client.responses.create(
    model="gpt-4.1",
    input="Summarize this support ticket...",
)

telemetry = adapter.extract(response)
agentvista.record(agent="ticket-summarizer", success=True, **telemetry)

The adapter reads input_tokens, output_tokens, and input_tokens_details.cached_tokens from Responses API responses, applying the correct cached token rate when calculating cost.

Supported models

Cost calculation is automatic for the following OpenAI models. If your model is not listed, cost_usd will be absent from the extracted telemetry; all other fields (tokens, model name) are still captured.

Model	Input (per M tokens)	Output (per M tokens)
gpt-5.4	$2.50	$15.00
gpt-5.4-mini	$0.75	$4.50
gpt-5.4-nano	$0.20	$1.25
gpt-5	$0.625	$5.00
gpt-4.1	$2.00	$8.00
gpt-4.1-mini	$0.40	$1.60
gpt-4.1-nano	$0.10	$0.40
gpt-4o	$2.50	$10.00
gpt-4o-mini	$0.15	$0.60
o3	$2.00	$8.00
o4-mini	$1.10	$4.40

Models that support prompt caching (gpt-5.4, gpt-5.4-mini, gpt-5.4-nano, gpt-5, gpt-4.1, gpt-4.1-mini, gpt-4.1-nano, gpt-4o, gpt-4o-mini, o3, o4-mini) automatically detect cached token counts from the response and apply the correct cached read rate when calculating cost.