> ## 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. # Open Agent Spec > Trace Oracle Open Agent Spec agents with OpenInference and send spans to Arize AX for LLM observability. [Open Agent Spec](https://oracle.github.io/agent-spec/development/agentspec/index.html) is Oracle's portable, platform-agnostic configuration language for describing agents and agentic systems. Its tracing extension standardizes how agent and flow executions emit traces across runtime adapters. Arize AX captures every Agent Spec run — agent invocations, LLM turns, and tool calls — via the [`openinference-instrumentation-agentspec`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-agentspec) package. ## Prerequisites * Python 3.10+ * An Arize AX account ([sign up](https://arize.com/sign-up/)) * An `OPENAI_API_KEY` from the [OpenAI Platform](https://platform.openai.com/api-keys) (the example loads the Agent Spec agent through the LangGraph adapter, which calls OpenAI) ## Launch Arize AX 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 The `langgraph` extra pulls the LangGraph adapter (and `langchain-openai`) that turns an Agent Spec definition into a runnable agent: ```bash theme={null} pip install arize-otel \ openinference-instrumentation-agentspec \ "pyagentspec[langgraph]" ``` ## Configure credentials ```bash theme={null} export ARIZE_SPACE_ID="" export ARIZE_API_KEY="" export ARIZE_PROJECT_NAME="agentspec-tracing-example" export OPENAI_API_KEY="" ``` ## Setup tracing ```python theme={null} # instrumentation.py import os from arize.otel import register from openinference.instrumentation.agentspec import AgentSpecInstrumentor tracer_provider = register( space_id=os.environ["ARIZE_SPACE_ID"], api_key=os.environ["ARIZE_API_KEY"], project_name=os.environ["ARIZE_PROJECT_NAME"], ) AgentSpecInstrumentor().instrument(tracer_provider=tracer_provider) print("Arize AX tracing initialized for Open Agent Spec.") ``` ## Run Open Agent Spec ```python theme={null} # example.py # Importing instrumentation first ensures tracing is set up before the # Agent Spec agent runs. from instrumentation import tracer_provider from pyagentspec.adapters.langgraph import AgentSpecLoader from pyagentspec.agent import Agent from pyagentspec.llms import OpenAiConfig # Describe the agent in Agent Spec, then load it into a runnable # LangGraph agent. The LLM reads OPENAI_API_KEY from the environment. agent = Agent( name="assistant", description="A general-purpose assistant without tools", llm_config=OpenAiConfig( name="openai-gpt-5.4-mini", model_id="gpt-5.4-mini", ), system_prompt="You are a helpful assistant. Answer politely.", ) langgraph_agent = AgentSpecLoader().load_component(agent) response = langgraph_agent.invoke( input={ "messages": [ { "role": "user", "content": ( "Why is the ocean salty? Answer in two sentences." ), } ] }, config={"configurable": {"thread_id": "1"}}, ) print(response["messages"][-1].content.strip()) ``` ### Expected output ```text wrap theme={null} Arize AX tracing initialized for Open Agent Spec. 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 dissolved salts remain and steadily concentrate over time. ``` ## Verify in Arize AX 1. Open your Arize AX space and select project **`agentspec-tracing-example`**. 2. You should see a new trace within \~30 seconds with an `AgentExecution[assistant]` AGENT span carrying the prompt as input and the agent's answer as output, wrapping an `LlmGenerationSpan` LLM child span with the model call and token usage. Any tools the agent invokes appear as child TOOL spans with their inputs and outputs. 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 AX.** 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. * **Agent spans missing.** `AgentSpecInstrumentor().instrument(...)` must run before the agent is loaded and invoked. Make sure `instrumentation.py` is the first import in your entry point. * **`ModuleNotFoundError: No module named 'langgraph'`.** Install the LangGraph adapter with the `langgraph` extra: `pip install "pyagentspec[langgraph]"`. Other runtime adapters (`agent-framework`, `autogen`, `crewai`) have their own extras. * **`401` from OpenAI.** Verify `OPENAI_API_KEY` is set and has access to the model in the example. Swap `gpt-5.4-mini` for a model your key can call. ## Resources