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fundamentals-required-attributes.md

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Required and Recommended Attributes

This document covers the required attribute and highly recommended attributes for all OpenInference spans.

Required Attribute

Every span MUST have exactly one required attribute:

{
  "openinference.span.kind": "LLM"
}

Highly Recommended Attributes

While not strictly required, these attributes are highly recommended on all spans as they:

  • Enable evaluation and quality assessment
  • Help understand information flow through your application
  • Make traces more useful for debugging

Input/Output Values

Attribute Type Description
input.value String Input to the operation (prompt, query, document)
output.value String Output from the operation (response, result, answer)

Example:

{
  "openinference.span.kind": "LLM",
  "input.value": "What is the capital of France?",
  "output.value": "The capital of France is Paris."
}

Why these matter:

  • Evaluations: Many evaluators (faithfulness, relevance, hallucination detection) require both input and output to assess quality
  • Information flow: Seeing inputs/outputs makes it easy to trace how data transforms through your application
  • Debugging: When something goes wrong, having the actual input/output makes root cause analysis much faster
  • Analytics: Enables pattern analysis across similar inputs or outputs

Phoenix Behavior:

  • Input/output displayed prominently in span details
  • Evaluators can automatically access these values
  • Search/filter traces by input or output content
  • Export inputs/outputs for fine-tuning datasets

Valid Span Kinds

There are exactly 9 valid span kinds in OpenInference:

Span Kind Purpose Common Use Case
LLM Language model inference OpenAI, Anthropic, local LLM calls
EMBEDDING Vector generation Text-to-vector conversion
CHAIN Application flow orchestration LangChain chains, custom workflows
RETRIEVER Document/context retrieval Vector DB queries, semantic search
RERANKER Result reordering Rerank retrieved documents
TOOL External tool invocation API calls, function execution
AGENT Autonomous reasoning ReAct agents, planning loops
GUARDRAIL Safety/policy checks Content moderation, PII detection
EVALUATOR Quality assessment Answer relevance, faithfulness scoring