OpenInference Span Kinds

The eleven OpenInference span kinds — LLM, Tool, Agent, Chain, Retriever, and the rest — with the canonical attributes Arize AX expects on each.

A span kind is the category of operation a span represents. OpenTelemetry has its own span kinds for network calls (SERVER, CLIENT, PRODUCER, …). OpenInference adds an AI-specific set: LLM, Tool, Agent, Chain, Retriever, and more.

Span kind is what drives the span-kind icons in the Arize AX UI. It’s also how Arize AX knows which attributes to expect — an LLM span gets a different visual treatment from a tool span.

Setting the Span Kind

Span kind is set via the openinference.span.kind attribute. The value is ALL CAPS — "TOOL", not "Tool". Casing matters: the canonical OpenInference Python enum is OpenInferenceSpanKindValues.TOOL = "TOOL".

from openinference.semconv.trace import (
    SpanAttributes,
    OpenInferenceSpanKindValues,
)

span.set_attribute(
    SpanAttributes.OPENINFERENCE_SPAN_KIND,
    OpenInferenceSpanKindValues.TOOL.value,
)

Arize AX UI behavior: span-kind icons in the Arize AX trace tree come directly from the openinference.span.kind attribute. A span without that attribute renders with a blank icon. Auto-instrumentors set this for you — set it explicitly on every manual span.

The Eleven Span Kinds

OpenInference defines eleven span-kind values. The first four (LLM, Chain, Agent, Tool) are the ones you’ll encounter most often:

Icon	Span kind	Description
	LLM	A call to a large language model.
	CHAIN	A starting point or a link between different LLM application steps.
	AGENT	A span that encompasses calls to LLMs and tools — typically the top-level wrapper for an agent loop.
	TOOL	A span that represents a call to an external tool, API, or function on behalf of an LLM.
	RETRIEVER	A data retrieval query for context from a datastore (e.g., a vector DB query).
	EMBEDDING	An encoding of unstructured data into a vector.
	RERANKER	A relevance-based re-ordering of documents.
	GUARDRAIL	A validation of LLM input or output for safety, policy, or compliance.
	EVALUATOR	An evaluation process — the type, configuration, and results.
	PROMPT	A span representing prompt construction or templating.
	UNKNOWN	Default when no kind is set explicitly.

Common Attributes Across All Kinds

These attributes apply to every span kind. Auto-instrumentors set them automatically; set them yourself on every manual span:

Attribute	Purpose
openinference.span.kind	The span kind (ALL CAPS — e.g., "LLM").
input.value	The span’s input. Free-form string or serialized JSON.
input.mime_type	"text/plain" (default) or "application/json". The TS semconv also defines "audio/wav".
output.value	The span’s output.
output.mime_type	"text/plain" (default) or "application/json". The TS semconv also defines "audio/wav".
metadata	Arbitrary JSON-stringified metadata.
session.id	Groups traces into a session. See Sessions.
user.id	The user the request belongs to.
tag.tags	A list of tags for filtering and search.

Arize AX UI behavior: Arize AX reads the trace-level input and output from the root span’s input.value and output.value. If your root span doesn’t set them, the trace list view shows those columns as blank even if child spans have data.

Per-Kind Attributes

The four most-used kinds have a richer attribute set. The full list lives in the OpenInference semantic conventions — the tables below are a summary.

LLM

A call to a large language model.

Attribute	Description
llm.model_name	The model identifier (e.g., gpt-4o, claude-3-5-sonnet-20241022).
llm.provider	The provider (e.g. openai, anthropic, mistralai).
llm.system	The underlying LLM system, when distinct from provider.
llm.input_messages	The chat history sent to the model.
llm.output_messages	The model’s response messages.
llm.invocation_parameters	Temperature, top-p, max tokens, etc. (JSON-stringified).
llm.tools	Tool definitions exposed to the model.
llm.function_call	Function call returned by the model (legacy).
llm.prompts	The raw prompts sent (for completion-style APIs).
llm.choices	The choices returned by the model.
llm.prompt_template.template	The prompt template before variable substitution.
llm.prompt_template.variables	The variables substituted into the template.
llm.prompt_template.version	A version identifier for the template.
llm.token_count.total	Total tokens.
llm.token_count.prompt	Prompt tokens.
llm.token_count.completion	Completion tokens.
llm.token_count.prompt_details.*	Breakdown of prompt tokens (cached, audio, etc.).
llm.token_count.completion_details.*	Breakdown of completion tokens.
llm.cost.total	Total cost.
llm.cost.prompt, llm.cost.completion	Per-direction cost.
llm.cost.*_details.*	Breakdown of cost by token type.
llm.finish_reason	Why the model stopped generating (stop, length, tool_calls, …).

TOOL

A call to an external tool, API, or function on behalf of an LLM.

Attribute	Description
tool.id	A unique identifier for the tool invocation (often correlated with the LLM’s tool-call ID).
tool.name	The tool’s name.
tool.description	The tool’s description (often matches what was shown to the model).
tool.parameters	The JSON-serialized parameters passed to the tool.
tool.json_schema	The tool’s full JSON schema as exposed to the model.

Tool calls are one of the clearest cases where you must instrument manually when making an LLM calls directly, instead of via a framework. When you call OpenAI directly, the auto-instrumentor traces the model’s request for a tool call — but the actual execution of the tool function happens in your Python code, where the instrumentor can’t see it. Wrap the function in a tool span yourself.

AGENT

A span that encompasses calls to LLMs and tools — typically the top-level wrapper for an agent loop.

Attribute	Description
agent.name	The agent’s name.
graph.node.id	Node ID for graph-based agent frameworks (e.g., LangGraph).
graph.node.name	Node display name.
graph.node.parent_id	Parent node ID, for graph traversal visualization.

CHAIN

A starting point or a link between different application steps. Use a chain span to group pre/post-processing logic that doesn’t fit any of the more specific kinds.

Chain spans use only the common attributes — input.value, output.value, metadata, session.id, etc.

Other Kinds

The remaining seven span kinds — RETRIEVER, EMBEDDING, RERANKER, GUARDRAIL, EVALUATOR, PROMPT, UNKNOWN — each have their own attribute schemas. Consult the canonical source for the full per-kind attribute list.

Multimodal Message Content

Inputs and outputs in modern AI applications aren’t always plain text. OpenInference defines a message_content attribute family for multimodal content:

Attribute	Description
message_content.type	One of text, image, audio, reasoning, tool_use.
message_content.text	The text payload (when type is text or reasoning).
message_content.image	The image payload — URL or inline base64 (when type=image).
message_content.id	Provider-assigned identifier for the message content item (e.g., OpenAI Responses reasoning IDs).
message_content.signature	Opaque vendor-issued signature (e.g., Gemini thoughtSignature).
message_content.data	Opaque vendor-issued data (e.g., Anthropic redacted_thinking.data).
message_content.encrypted_content	Encrypted payload for sensitive content (e.g., OpenAI encrypted_content).

For audio specifically, OpenInference exposes a separate set of attributes — audio.url, audio.mime_type, audio.transcript — see the canonical source.

How a Trace Tree Uses Span Kinds

A typical agent trace nests span kinds inside each other:

AGENT span                         (agent.name = "research-assistant")
├── LLM span                       "decide next step"
├── TOOL span                      "search knowledge base"
│   └── RETRIEVER span             "vector search"
│       └── EMBEDDING span         "embed query"
└── LLM span                       "answer with retrieved docs"

Each level has its own kind, its own attributes, and its own rendering in the Arize AX UI. The combination is what makes agent behavior legible.

---

Next step

You know what attributes go on a span. Next, the three ways to actually emit spans — auto, manual, and hybrid:

Next: Instrumentation Approaches