# Observe: Tracing Setup
Configure tracing to capture data for evaluation.
## Quick Setup
```python
# Python
from phoenix.otel import register
register(project_name="my-app", auto_instrument=True)
```
```typescript
// TypeScript
import { registerPhoenix } from "@arizeai/phoenix-otel";
registerPhoenix({ projectName: "my-app", autoInstrument: true });
```
## Essential Attributes
| Attribute | Why It Matters |
| --------- | -------------- |
| `input.value` | User's request |
| `output.value` | Response to evaluate |
| `retrieval.documents` | Context for faithfulness |
| `tool.name`, `tool.parameters` | Agent evaluation |
| `llm.model_name` | Track by model |
## Custom Attributes for Evals
```python
span.set_attribute("metadata.client_type", "enterprise")
span.set_attribute("metadata.query_category", "billing")
```
## Exporting for Evaluation
```python
from phoenix.client import Client
# Client() works for local Phoenix (falls back to env vars or localhost:6006)
# For remote/cloud: Client(base_url="https://app.phoenix.arize.com", api_key="...")
client = Client()
spans_df = client.spans.get_spans_dataframe(
project_identifier="my-app", # NOT project_name= (deprecated)
root_spans_only=True,
)
dataset = client.datasets.create_dataset(
name="error-analysis-set",
dataframe=spans_df[["input.value", "output.value"]],
input_keys=["input.value"],
output_keys=["output.value"],
)
```
## Uploading Evaluations as Annotations
After running evaluations, upload results back to Phoenix as span annotations:
```python
from phoenix.evals import async_evaluate_dataframe
from phoenix.evals.utils import to_annotation_dataframe
# Run evaluations
results_df = await async_evaluate_dataframe(dataframe=spans_df, evaluators=[my_eval])
# Format results for Phoenix annotations
annotations_df = to_annotation_dataframe(results_df)
# Upload to Phoenix
client.spans.log_span_annotations_dataframe(dataframe=annotations_df)
```
This creates annotations visible in the Phoenix UI alongside your traces.
## Verify
Required attributes: `input.value`, `output.value`, `status_code`
For RAG: `retrieval.documents`
For agents: `tool.name`, `tool.parameters`
