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OpenRouter → OpenObserve

Automatically capture token usage, latency, and model metadata for every OpenRouter request in your Python application. OpenRouter is a unified API gateway that routes to 200+ models from providers including OpenAI, Anthropic, Meta, Google, and Mistral. Instrumentation uses the standard OpenAI instrumentor pointed at the OpenRouter endpoint.

Prerequisites

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

Installation

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

Configuration

Create a .env file in your project root:

# OpenObserve instance URL
# Default for self-hosted: http://localhost:5080
OPENOBSERVE_URL=https://api.openobserve.ai/

# Your OpenObserve organisation slug or ID
OPENOBSERVE_ORG=your_org_id

# Basic auth token — Base64-encoded "email:password"
OPENOBSERVE_AUTH_TOKEN=Basic <your_base64_token>

# OpenRouter API key
OPENROUTER_API_KEY=your-openrouter-api-key

Instrumentation

Call OpenAIInstrumentor().instrument() before creating the OpenAI client. The two optional OpenRouter headers passed via default_headers are not required for requests to succeed, but they populate your OpenRouter usage dashboard.

from dotenv import load_dotenv
load_dotenv()

from openinference.instrumentation.openai import OpenAIInstrumentor
from openobserve import openobserve_init

OpenAIInstrumentor().instrument()
openobserve_init()

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["OPENROUTER_API_KEY"],
    base_url="https://openrouter.ai/api/v1",
    default_headers={
        "HTTP-Referer": "https://your-app.com",
        "X-Title": "Your App Name",
    },
)

response = client.chat.completions.create(
    model="openai/gpt-oss-20b:free",
    messages=[{"role": "user", "content": "Explain distributed tracing in one sentence."}],
)
print(response.choices[0].message.content)

Switching models

Because every request goes through the same OpenRouter endpoint, changing the model string is all it takes to route to a different provider. Each switch produces a separate span with its own llm_model_name, making cross-provider comparisons straightforward in OpenObserve.

models = [
    "openai/gpt-oss-20b:free",
    "google/gemma-4-31b-it:free",
    "qwen/qwen3-coder:free",
]

for model in models:
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "What is OpenTelemetry?"}],
    )
    print(f"{model}: {response.choices[0].message.content}")

Free-tier models use the :free suffix and are rate-limited. OpenRouter's full model list is available at openrouter.ai/models.

What Gets Captured

Attribute Description
llm_model_name Full model identifier including provider (e.g. openai/gpt-oss-20b:free)
llm_request_parameters_model The model requested
gen_ai_response_model The model that served the response
llm_token_count_prompt Tokens in the prompt
llm_token_count_completion Tokens in the response
llm_token_count_total Total tokens consumed
llm_system openai (the client library used)
openinference_span_kind LLM
duration End-to-end request latency
span_status OK or error status

Viewing Traces

  1. Log in to OpenObserve and navigate to Traces
  2. Filter by llm_model_name to compare latency and token usage across different providers and models in a single dashboard
  3. Click any span to inspect the full request and response payload

OpenRouter trace in OpenObserve

Next Steps

With OpenRouter instrumented, every request is recorded in OpenObserve regardless of which underlying provider served it. From here you can build dashboards comparing cost and latency across providers, identify which model delivers the best performance for your workload, and set alerts on error spikes.

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