# 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:**
```json
{
"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:**
```json
{
"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 |
