> ## Documentation Index
> Fetch the complete documentation index at: https://arizeai-433a7140.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 05.05.2026: REST API Updates

# Filter-Based Annotation Delete Endpoints

May 5, 2026

**Available in arize-phoenix 15.4.0+**

Three new `DELETE` endpoints let you bulk-remove annotations from a project by filter — without knowing every individual span/trace/session ID. This closes the annotation lifecycle loop for automated pipelines that tag annotations with a custom `identifier` on creation and need to roll them back later.

```http theme={null}
DELETE /v1/projects/{project_identifier}/span_annotations
DELETE /v1/projects/{project_identifier}/trace_annotations
DELETE /v1/projects/{project_identifier}/session_annotations
```

**Query parameters** (all optional, but at least one time-bound or `delete_all=true` is required):

| Parameter        | Description                                    |
| ---------------- | ---------------------------------------------- |
| `name`           | Exact match on annotation name                 |
| `identifier`     | Exact match on annotation identifier           |
| `annotator_kind` | `LLM`, `CODE`, or `HUMAN`                      |
| `start_time`     | Inclusive lower bound on `created_at`          |
| `end_time`       | Exclusive upper bound on `created_at`          |
| `delete_all`     | Set `true` to waive the time-bound requirement |

```bash theme={null}
# Delete all annotations tagged with identifier "eval-run-42" on spans
curl -X DELETE \
  "https://your-phoenix/v1/projects/my-project/span_annotations?identifier=eval-run-42" \
  -H "Authorization: Bearer $PHOENIX_API_KEY"

# Delete LLM annotations older than a cutoff
curl -X DELETE \
  "https://your-phoenix/v1/projects/my-project/trace_annotations?annotator_kind=LLM&end_time=2026-05-01T00:00:00Z" \
  -H "Authorization: Bearer $PHOENIX_API_KEY"
```

# Token Counts in Trace and Session REST Payloads

May 5, 2026

**Available in arize-phoenix 15.4.0+**

The `GET /v1/projects/{project_identifier}/traces` and `GET /v1/projects/{project_identifier}/sessions` endpoints now include cumulative token usage fields — `cumulative_token_count_prompt`, `cumulative_token_count_completion`, and `cumulative_token_count_total` — so you can read aggregate token consumption directly from the REST API without recomputing from raw span attributes.

Values are summed from root spans and default to `0` for traces or sessions with no LLM calls. The `/v1/spans` endpoint is unchanged — span-level token counts remain in the existing `attributes` dictionary.

# Experiment CSV Export Includes Dataset Metadata

May 5, 2026

**Available in arize-phoenix 15.3.0+**

Downloading an experiment as CSV now includes per-example dataset metadata columns. Each metadata key appears as a `metadata_<key>` column — matching the format used by the dataset CSV export — so you can cross-reference experiment results with the original dataset context without a separate download.

# Evals: Runtime Model Capability Detection

May 5, 2026

**Available in arize-phoenix-evals 3.1.0+**

The OpenAI evaluator adapter now detects structured-output and tool-call support at runtime rather than checking against a hardcoded model list. This unblocks OpenAI reasoning models (`o1`, `o3`, `o3-mini`, `o4-mini`) for use with `ClassificationEvaluator` and ensures new models work automatically without requiring a library update.

The adapter tries structured output first, falls back to tool calling if unsupported, and caches the result per adapter instance — matching the approach already used by the Google GenAI adapter.

```python theme={null}
from phoenix.evals import ClassificationEvaluator, OpenAIModel

# Reasoning models now work without any special configuration
evaluator = ClassificationEvaluator(
    model=OpenAIModel(model="o3-mini"),
    template="my-eval-template",
)
```