> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# OpenInference Semantic Conventions
> Standardized attribute names that make AI trace data portable across tools, languages, and platforms — and how OpenInference compares to the still-evolving GenAI conventions.
> *"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?" |
Two examples of OpenInference attribute names:
```
llm.token_count.total
llm.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.
# 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. |
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.
# 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.
When in doubt about an exact attribute name, those files are the source of truth.
# 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](/ax/concepts/otel-openinference/span-kinds) |
| **Per-kind attributes** | `llm.input_messages`, `tool.parameters`, `agent.name`, `retrieval.documents` | [Span Kinds](/ax/concepts/otel-openinference/span-kinds) |
| **Common attributes** | `input.value`, `output.value`, `input.mime_type`, `output.mime_type`, `metadata`, `session.id`, `user.id`, `tag.tags` | [Span Kinds](/ax/concepts/otel-openinference/span-kinds#common-attributes-across-all-kinds) |
| **Enums** | LLM providers (`openai`, `anthropic`, `cohere`, ...), MIME types (`text/plain`, `application/json`), span kind values (ALL CAPS) | [Span Kinds](/ax/concepts/otel-openinference/span-kinds) and the [canonical source](https://github.com/Arize-ai/openinference/blob/main/python/openinference-semantic-conventions/src/openinference/semconv/trace/__init__.py) |
The next page walks through the span-kind catalog in detail.
***
## Next step
Span kinds are the most important convention OpenInference adds — they determine how spans render in the Arize AX UI and which attributes are expected: