“Standardized naming schemes and attribute definitions for telemetry data across services, languages, and platforms.”A semantic convention is an agreement about what to call things. Without conventions, every team invents their own attribute names — one calls it
prompt, another input.text, another messages[0].content — and tools that try to read all of those have to maintain mappings for every variant.
OpenInference defines the canonical attribute names for AI/LLM telemetry. Arize AX uses these conventions to render spans in the UI, and any OTel-compatible backend that understands OpenInference can do the same.
Why Semantic Conventions Matter
Three concrete benefits, all of which compound as your stack grows:| What it gives you | |
|---|---|
| Consistency | One name for each concept across services, languages, and platforms. Same key in Python, JS, and Go. |
| Interoperability | Tools and backends understand your data without custom mapping. Switching backends or layering on new tools doesn’t require re-instrumenting. |
| Best practices | The conventions encode what to trace, not just how. They are an opinionated answer to “which attributes should I set on an LLM span?” |
llm.input_messages on a span, Arize AX knows it’s the chat history. So does any other OpenInference-aware backend. So does anyone reading your trace export six months from now.
OpenInference vs GenAI
The GenAI observability space currently has two semantic convention standards. They overlap in what they describe but differ in maturity and governance.| OpenInference | GenAI | |
|---|---|---|
| Maintained by | Arize | OpenTelemetry community |
| Status | Stable | Still in development; not on a stable release |
| Stability guarantees | Conventions are stable across releases. Breaking changes are versioned. | Conventions are subject to change at any time, with explicit guidance about version transitions and opt-in for newer experimental revisions. |
| Attribute prefix | Domain-specific prefixes: llm.*, tool.*, agent.*, retriever.*, etc. | Generally prefixed gen_ai.* |
| Instrumentation libraries | Extensive — auto-instrumentors for most popular AI frameworks. | Smaller surface area today; instrumentors are still landing. |
| Arize AX support | First-class. Arize AX reads these directly. | Spans tagged with gen_ai.* attributes still arrive at Arize AX, but won’t get the same UI treatment as OpenInference-tagged spans. |
The Authoritative Source
OpenInference is open-source. The canonical attribute lists, span kind enums, MIME type values, and LLM provider/system enums live in the OpenInference repository — language-specific implementations track these definitions exactly.Python Semantic Conventions
TS Semantic Conventions
What’s Covered in OpenInference
The conventions cover four broad categories:| Category | Examples | Where to read more |
|---|---|---|
| Span kinds | LLM, TOOL, AGENT, CHAIN, RETRIEVER, EMBEDDING, RERANKER, GUARDRAIL, EVALUATOR, PROMPT, UNKNOWN | Span Kinds |
| Per-kind attributes | llm.input_messages, tool.parameters, agent.name, retrieval.documents | Span Kinds |
| Common attributes | input.value, output.value, input.mime_type, output.mime_type, metadata, session.id, user.id, tag.tags | Span Kinds |
| Enums | LLM providers (openai, anthropic, cohere, …), MIME types (text/plain, application/json), span kind values (ALL CAPS) | Span Kinds and the canonical source |