CometAPI → OpenObserve

Automatically capture token usage, latency, and model metadata for every CometAPI inference call in your Python application. CometAPI is a unified API gateway providing access to 500+ models from OpenAI, Anthropic, and other providers through a single OpenAI-compatible endpoint. Instrumentation uses the standard OpenAI instrumentor pointed at the CometAPI base URL.

Prerequisites

Python 3.8+
An OpenObserve account (cloud or self-hosted)
Your OpenObserve organisation ID and Base64-encoded auth token
A CometAPI API key

Installation

pip install openobserve-telemetry-sdk openinference-instrumentation-openai openai python-dotenv

Configuration

Create a .env file in your project root:

OPENOBSERVE_URL=http://localhost:5080/
OPENOBSERVE_ORG=default
OPENOBSERVE_AUTH_TOKEN=Basic <your_base64_token>
COMETAPI_API_KEY=your-cometapi-api-key

Instrumentation

Call OpenAIInstrumentor().instrument() before creating the OpenAI client. Point the client at the CometAPI base URL and pass your CometAPI key.

from dotenv import load_dotenv
load_dotenv()

from openinference.instrumentation.openai import OpenAIInstrumentor
OpenAIInstrumentor().instrument()

from openobserve import openobserve_init
openobserve_init(service_name="cometapi")

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["COMETAPI_API_KEY"],
    base_url="https://api.cometapi.com/v1",
)

response = client.chat.completions.create(
    model="claude-haiku-4-5-20251001",
    messages=[{"role": "user", "content": "Explain distributed tracing in one sentence."}],
)
print(response.choices[0].message.content)

What Gets Captured

Attribute	Description
`operation_name`	`ChatCompletion`
`llm_system`	`openai` (OpenAI-compatible client)
`llm_model_name`	Resolved model returned by the API (e.g. `claude-haiku-4-5-20251001`)
`llm_request_parameters_model`	Model name sent in the request (e.g. `claude-haiku-4-5-20251001`)
`llm_observation_type`	`GENERATION`
`llm_token_count_prompt`	Prompt tokens consumed
`llm_token_count_completion`	Completion tokens returned
`llm_token_count_total`	Total tokens consumed
`llm_usage_tokens_input`	Input tokens
`llm_usage_tokens_output`	Output tokens
`llm_usage_cost_input`	Estimated cost for input tokens
`llm_usage_cost_output`	Estimated cost for output tokens
`openinference_span_kind`	`LLM`
`duration`	End-to-end request latency
`span_status`	`OK` on success, `ERROR` on failure

Viewing Traces

Log in to OpenObserve and navigate to Traces
Filter by service_name = cometapi to isolate CometAPI spans
Filter by operation_name = ChatCompletion to see all inference calls
Use llm_model_name to filter by specific model versions
Filter by span_status = ERROR to find failed requests

CometAPI trace in OpenObserve

Next Steps

With CometAPI instrumented, every inference call is recorded in OpenObserve regardless of which underlying model was used. From here you can compare latency and token usage across models, monitor cost per request, and set alerts on error spans.