Skip to content
OpenObserve
Get Demo Star
OpenObserve
Get Demo Star
Open Menu Close Menu

Open WebUI → OpenObserve

Capture chat completion spans from your Open WebUI instance. Open WebUI is a self-hosted interface for local and cloud LLMs that exposes an OpenAI-compatible API. Wrap API calls in manual OTel spans to record model, question, and response metadata in OpenObserve.

Prerequisites

  • Python 3.8+
  • Open WebUI running (Docker recommended)
  • An OpenObserve account (cloud or self-hosted)
  • Your OpenObserve organisation ID and Base64-encoded auth token
  • An Open WebUI API key (from your profile settings)

Installation

pip install openobserve-telemetry-sdk python-dotenv requests

Configuration

Start Open WebUI with Docker:

docker run -d --name openwebui \
  -p 3001:8080 \
  -e OPENAI_API_KEY=your-openai-key \
  -v openwebui-data:/app/backend/data \
  ghcr.io/open-webui/open-webui:main

Create a .env file in your project root:

OPENOBSERVE_URL=https://api.openobserve.ai/
OPENOBSERVE_ORG=your_org_id
OPENOBSERVE_AUTH_TOKEN=Basic <your_base64_token>
OPENWEBUI_BASE_URL=http://localhost:3001
OPENWEBUI_API_KEY=your-openwebui-api-key
OPENWEBUI_MODEL=gpt-4o-mini

To get your Open WebUI API key, go to your profile > API Keys and generate a new key.

Instrumentation

Call openobserve_init() to set up the tracer provider, then wrap each API call in a manual span.

from dotenv import load_dotenv
load_dotenv()

from openobserve import openobserve_init
openobserve_init()

from opentelemetry import trace
import os
import requests

tracer = trace.get_tracer(__name__)

base_url = os.environ.get("OPENWEBUI_BASE_URL", "http://localhost:3001")
api_key = os.environ["OPENWEBUI_API_KEY"]
model = os.environ.get("OPENWEBUI_MODEL", "gpt-4o-mini")

prompt = "Explain distributed tracing in one sentence."

with tracer.start_as_current_span("openwebui.chat_completion") as span:
    span.set_attribute("openwebui.model", model)
    span.set_attribute("openwebui.question", prompt)
    resp = requests.post(
        f"{base_url}/api/chat/completions",
        headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
        json={"model": model, "messages": [{"role": "user", "content": prompt}], "max_tokens": 100},
    )
    resp.raise_for_status()
    content = resp.json()["choices"][0]["message"]["content"]
    span.set_attribute("openwebui.response_length", len(content))

print(content)

What Gets Captured

Attribute Description
openwebui_model The model used for the completion (e.g. gpt-4o-mini)
openwebui_question The prompt sent to the model
openwebui_response_length Character length of the model response
operation_name openwebui.chat_completion
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 operation_name = openwebui.chat_completion
  3. Click any span to inspect the model, prompt, and response length
  4. Filter by openwebui_model to compare latency across models

Open WebUI trace in OpenObserve

Next Steps

With Open WebUI traces in OpenObserve, you can monitor chat response latency, track which models are used most, and set alerts on failed requests.

Read More