Standardized attribute names that make AI trace data portable across tools, languages, and platforms — and how OpenInference compares to the still-evolving GenAI conventions.
Use this file to discover all available pages before exploring further.
“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.
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?”
Two examples of OpenInference attribute names:
llm.token_count.totalllm.input_messages
If you set 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.
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.
For new instrumentation today, use OpenInference. Over time the two conventions are expected to converge as the GenAI spec stabilizes — when that happens, the OpenInference auto-instrumentors will pick up the change so your application code doesn’t have to.
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
When in doubt about an exact attribute name, those files are the source of truth.
Span kinds are the most important convention OpenInference adds — they determine how spans render in the Arize AX UI and which attributes are expected:
