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 Phoenix UI. It’s also how Phoenix 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,
)

Phoenix UI behavior: span-kind icons in Phoenix 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.

Phoenix UI behavior: Phoenix 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 Phoenix UI. The combination is what makes agent behavior legible. Trace in Phoenix from the OpenAI Agents SDK example, showing agent, chain, tool, and LLM spans nested together

Trace in Phoenix from the OpenAI Agents SDK example, showing agent, chain, tool, and LLM spans nested together

Next step

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

Get Started

MCP

Tracing

Evaluation

Datasets & Experiments

Prompts

Settings

Concepts

Resources

OpenInference Span Kinds

Setting the Span Kind

The Eleven Span Kinds

Common Attributes Across All Kinds

Per-Kind Attributes

LLM

TOOL

AGENT

CHAIN

Other Kinds

Multimodal Message Content

How a Trace Tree Uses Span Kinds

Next step

Next: Instrumentation Approaches

​Setting the Span Kind

​The Eleven Span Kinds

​Common Attributes Across All Kinds

​Per-Kind Attributes

​LLM

​TOOL

​AGENT

​CHAIN

​Other Kinds

​Multimodal Message Content

​How a Trace Tree Uses Span Kinds

​Next step

Next: Instrumentation Approaches

Setting the Span Kind

The Eleven Span Kinds

Common Attributes Across All Kinds

Per-Kind Attributes

LLM

TOOL

AGENT

CHAIN

Other Kinds

Multimodal Message Content

How a Trace Tree Uses Span Kinds

Next step