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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

  1. Log in to OpenObserve and navigate to Traces
  2. Filter by service_name = cometapi to isolate CometAPI spans
  3. Filter by operation_name = ChatCompletion to see all inference calls
  4. Use llm_model_name to filter by specific model versions
  5. 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.

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