> ## 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. # Gravitee > Trace Gravitee AI Gateway LLM Proxy calls with OpenInference and send spans to Arize AX for LLM observability. The [Gravitee AI Gateway](https://documentation.gravitee.io/apim/ai-agent-management/llm-proxy) LLM Proxy is a governed front door to your LLM providers — one OpenAI-compatible endpoint that fronts OpenAI, Anthropic, Gemini, Bedrock, Vertex AI, and any OpenAI-compatible provider, with subscriptions, token-based rate limiting, and guard rails applied at the gateway. The proxy exposes `/chat/completions`, `/responses`, `/embeddings`, and `/models` at `https:///`, so any OpenAI client works with `base_url` set to the proxy. Arize AX captures every call via the [`openinference-instrumentation-openai`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-openai) package — the same instrumentor that covers OpenAI's hosted API. This guide instruments **your application's** calls to the gateway, producing OpenInference LLM spans with prompts, responses, and token usage. Gravitee's own OpenTelemetry support emits gateway HTTP spans instead, which do not carry LLM attributes. ## Prerequisites * Python 3.9+ * An Arize AX account ([sign up](https://arize.com/sign-up/)) * A deployed Gravitee LLM Proxy API — requires a self-hosted or hybrid APIM installation (4.10 or later) and an [Enterprise license](https://documentation.gravitee.io/apim/introduction/enterprise-edition). See [Proxy your LLMs](https://documentation.gravitee.io/apim/ai-agent-management/llm-proxy/proxy-your-llms). * A subscription API key for a plan on that API ## 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="gravitee-tracing-example" export GRAVITEE_BASE_URL="https:///" export GRAVITEE_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 Gravitee.") ``` ## Run Gravitee ```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 the Gravitee LLM Proxy context path. client = OpenAI( base_url=os.environ["GRAVITEE_BASE_URL"], # An API Key plan reads the subscription key from a custom header, so the # `api_key` argument is unused. The OpenAI client still requires a value. api_key="unused", default_headers={"X-Gravitee-Api-Key": os.environ["GRAVITEE_API_KEY"]}, ) response = client.chat.completions.create( # Model IDs are namespaced by context path. List yours with # `curl $GRAVITEE_BASE_URL/models`. model="llmtest:gpt-5.4-mini", messages=[ { "role": "user", "content": "Why is the ocean salty? Answer in two sentences.", }, ], ) print(response.choices[0].message.content) ``` If your LLM Proxy is secured with a JWT or OAuth2 plan instead, Gravitee reads a bearer token from the `Authorization` header. Pass the token as `api_key=` and drop `default_headers`. ### Expected output ```text wrap theme={null} Arize AX tracing initialized for Gravitee. The ocean is salty because rivers continuously dissolve mineral salts from rocks and soil and carry them to the sea, where they accumulate over millions of years. Water leaves the ocean through evaporation but the salts remain, steadily concentrating until reaching today's roughly 3.5% salinity. ``` ## Verify in Arize 1. Open your Arize AX space and select project **`gravitee-tracing-example`**. 2. You should see a new trace within \~30 seconds containing a `ChatCompletion` LLM span with the prompt, response, and token usage attached. 3. If no traces appear, see [Troubleshooting](#troubleshooting). ### Check from the skill, CLI, or SDK Confirm spans are actually reaching your Arize AX project. Use whichever fits your workflow — the skill and CLI work for any framework; the SDK check is shown for each language. Install the [Arize Skills](https://github.com/Arize-ai/arize-skills) plugin and let your coding agent check for you: ```bash theme={null} npx skills add Arize-ai/arize-skills ``` Then prompt your agent: > Use the `arize-trace` skill to export and analyze recent traces from my project. Confirm spans are arriving, and summarize any errors or latency issues. Export recent spans for your project — any rows mean traces are landing: ```bash theme={null} ax spans export "$ARIZE_PROJECT_NAME" --space "$ARIZE_SPACE_ID" \ --limit 5 --stdout | jq 'length' ``` A non-zero count confirms spans reached Arize AX. Run `ax auth login` first if you have not authenticated. See the [`ax spans` reference](/docs/api-clients/cli/spans). Query the project's spans and check that at least one came back. ```python Python theme={null} import os from arize import ArizeClient client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"]) resp = client.spans.list( project=os.environ["ARIZE_PROJECT_NAME"], space=os.environ["ARIZE_SPACE_ID"], limit=5, ) count = len(resp.spans) print( f"{count} span(s) found" if count else "No spans yet — recheck setup" ) ``` ```typescript TypeScript theme={null} // Reads ARIZE_API_KEY from the environment. import { listSpans } from "@arizeai/ax-client"; const { data: spans } = await listSpans({ project: process.env.ARIZE_PROJECT_NAME!, space: process.env.ARIZE_SPACE_ID!, limit: 5, }); const count = spans.length; console.log( count ? `${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](/docs/api-clients/python/version-8/client-resources/spans) · [TypeScript](/docs/api-clients/typescript/version-1/client-resources/spans) · [Go](/docs/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. * **Gravitee 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 Gravitee.** An API Key plan expects the subscription key in a custom header — `X-Gravitee-Api-Key` by default — not in `Authorization: Bearer`. Passing the key as `api_key=` alone will fail. If your plan overrides the header name, use that name in `default_headers`. * **`404` on the request path.** The base URL is your Gateway URL plus the LLM Proxy context path, with no `/v1` segment (e.g. `https://gateway.example.com/llmtest`). The OpenAI client appends `/chat/completions` itself. * **Model not found.** Model IDs are namespaced by context path in a `context-path:model-name` format (e.g. `llmtest:gpt-5.4-mini`). Copy the exact ID from `curl $GRAVITEE_BASE_URL/models`. ## Resources