> ## Documentation Index > Fetch the complete documentation index at: https://arize-ax.mintlify.site/docs/llms.txt > Use this file to discover all available pages before exploring further. # OrcaRouter > Trace OrcaRouter API calls with OpenInference and send spans to Arize AX for LLM observability. [OrcaRouter](https://www.orcarouter.ai/) is an OpenAI-compatible AI gateway that routes requests across 200+ models. Its `orcarouter/auto` virtual model selects an upstream provider per request; you can also target specific models using a `/` identifier. Because OrcaRouter mirrors the OpenAI API schema, you instrument it with the [`openinference-instrumentation-openai`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-openai) package by pointing the client's `base_url` at OrcaRouter's endpoint. ## Prerequisites * Python 3.9+ * An Arize AX account ([sign up](https://arize.com/sign-up/)) * An `ORCAROUTER_API_KEY` from the [OrcaRouter dashboard](https://app.orcarouter.ai) ## Launch Arize 1. Sign in to your [Arize AX account](https://app.arize.com/). 2. From **Space Settings**, copy your **Space ID** and **API Key**. You will set them as `ARIZE_SPACE_ID` and `ARIZE_API_KEY` below. ## Install ```bash theme={null} pip install arize-otel openinference-instrumentation-openai openai ``` ## Configure credentials ```bash theme={null} export ARIZE_SPACE_ID="" export ARIZE_API_KEY="" export ARIZE_PROJECT_NAME="orcarouter-tracing-example" export ORCAROUTER_API_KEY="" ``` ## Setup tracing ```python theme={null} # instrumentation.py import os from arize.otel import register from openinference.instrumentation.openai import OpenAIInstrumentor tracer_provider = register( space_id=os.environ["ARIZE_SPACE_ID"], api_key=os.environ["ARIZE_API_KEY"], project_name=os.environ["ARIZE_PROJECT_NAME"], ) OpenAIInstrumentor().instrument(tracer_provider=tracer_provider) print("Arize AX tracing initialized for OrcaRouter.") ``` ## Run OrcaRouter ```python theme={null} # example.py # Importing instrumentation first ensures tracing is set up # before `openai` is imported. from instrumentation import tracer_provider import os from openai import OpenAI # Point the OpenAI client at OrcaRouter's OpenAI-compatible endpoint. client = OpenAI( base_url="https://api.orcarouter.ai/v1", api_key=os.environ["ORCAROUTER_API_KEY"], ) response = client.chat.completions.create( model="orcarouter/auto", messages=[ { "role": "user", "content": "Why is the ocean salty? Answer in two sentences.", }, ], ) print(response.choices[0].message.content) ``` `orcarouter/auto` selects an upstream provider per request. The span recorded in Arize AX will show the **resolved upstream model name** (e.g. `deepseek-v4-flash-202505`) rather than `orcarouter/auto`. To test without a funded account, replace `orcarouter/auto` with a free model such as `deepseek/deepseek-v4-flash-free`. ### Expected output ```text wrap theme={null} Arize AX tracing initialized for OrcaRouter. The ocean is salty because rivers continuously dissolve mineral salts from rocks and soil and carry them to the sea, where they accumulate over millions of years. Water leaves the ocean through evaporation but the salts remain, steadily concentrating until reaching today's roughly 3.5% salinity. ``` ## Verify in Arize 1. Open your Arize AX space and select project **`orcarouter-tracing-example`**. 2. You should see a new trace within \~30 seconds containing a `ChatCompletion` LLM span with the prompt, response, and token usage attached. The model name on the span will be the upstream model OrcaRouter selected (e.g. `deepseek-v4-flash`), not the virtual `orcarouter/auto` identifier. 3. If no traces appear, see [Troubleshooting](#troubleshooting). ### Check from the skill, CLI, or SDK Confirm spans are actually reaching your Arize AX project. Use whichever fits your workflow — the skill and CLI work for any framework; the SDK check is shown for each language. Install the [Arize Skills](https://github.com/Arize-ai/arize-skills) plugin and let your coding agent check for you: ```bash theme={null} npx skills add Arize-ai/arize-skills ``` Then prompt your agent: > Use the `arize-trace` skill to export and analyze recent traces from my project. Confirm spans are arriving, and summarize any errors or latency issues. Export recent spans for your project — any rows mean traces are landing: ```bash theme={null} ax spans export "$ARIZE_PROJECT_NAME" --space "$ARIZE_SPACE_ID" \ --limit 5 --stdout | jq 'length' ``` A non-zero count confirms spans reached Arize AX. Run `ax auth login` first if you have not authenticated. See the [`ax spans` reference](/api-clients/cli/spans). Query the project's spans and check that at least one came back. ```python Python theme={null} import os from arize import ArizeClient client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"]) resp = client.spans.list( project=os.environ["ARIZE_PROJECT_NAME"], space=os.environ["ARIZE_SPACE_ID"], limit=5, ) count = len(resp.spans) print( f"{count} span(s) found" if count else "No spans yet — recheck setup" ) ``` ```typescript TypeScript theme={null} // Reads ARIZE_API_KEY from the environment. import { listSpans } from "@arizeai/ax-client"; const { data: spans } = await listSpans({ project: process.env.ARIZE_PROJECT_NAME!, space: process.env.ARIZE_SPACE_ID!, limit: 5, }); const count = spans.length; console.log( count ? `${count} span(s) found` : "No spans yet — recheck setup", ); ``` ```go Go theme={null} client, err := arize.NewClient( arize.Config{APIKey: os.Getenv("ARIZE_API_KEY")}, ) if err != nil { log.Fatal(err) } resp, err := client.Spans.List(ctx, spans.ListRequest{ Project: os.Getenv("ARIZE_PROJECT_NAME"), Space: os.Getenv("ARIZE_SPACE_ID"), Limit: 5, }) if err != nil { log.Fatal(err) } fmt.Printf("%d span(s) found\n", len(resp.Spans)) ``` SDK span references: [Python](/api-clients/python/version-8/client-resources/spans) · [TypeScript](/api-clients/typescript/version-1/client-resources/spans) · [Go](/api-clients/go/version-2/client-resources/spans). ## Troubleshooting * **No traces in Arize.** Confirm `ARIZE_SPACE_ID` and `ARIZE_API_KEY` are set in the same shell that runs `example.py`. Enable OpenTelemetry debug logs with `export OTEL_LOG_LEVEL=debug` and re-run. * **OrcaRouter spans missing but other spans present.** `OpenAIInstrumentor().instrument(...)` must run before any `import openai`. Make sure `instrumentation.py` is the first import in your entry point. * **`401` from OrcaRouter.** Use your **OrcaRouter** API key (from the [OrcaRouter dashboard](https://app.orcarouter.ai)), not an OpenAI or other provider key. * **`403 insufficient_user_quota`.** `orcarouter/auto` routes to paid upstream models and requires a funded OrcaRouter wallet. Add credit in the [OrcaRouter dashboard](https://app.orcarouter.ai), or use a free model such as `deepseek/deepseek-v4-flash-free` to test without balance. * **Model not found.** OrcaRouter's primary virtual model is `orcarouter/auto`. You can also pass specific provider models in `/` format (e.g. `deepseek/deepseek-v4-flash-free`, `openai/gpt-4.1-mini`). See the [OrcaRouter documentation](https://docs.orcarouter.ai/introduction) for available identifiers. ## Resources