arize-experiment

Install to Claude Code

npx -y skills add https://github.com/arize-ai/arize-skills --skill arize-experiment

Description

arize experiment

SKILL.md

---
name: arize-experiment
description: Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.
metadata:
  author: arize
  version: "1.0"
compatibility: Requires the ax CLI and a configured Arize profile.
---

# Arize Experiment Skill

> **`SPACE`** — All `--space` flags and the `ARIZE_SPACE` env var accept a space **name** (e.g., `my-workspace`) or a base64 space **ID** (e.g., `U3BhY2U6...`). Find yours with `ax spaces list`.

## Concepts

- **Experiment** = a named evaluation run against a specific dataset version, containing one run per example
- **Experiment Run** = the result of processing one dataset example -- includes the model output, optional evaluations, and optional metadata
- **Dataset** = a versioned collection of examples; every experiment is tied to a dataset and a specific dataset version
- **Evaluation** = a named metric attached to a run (e.g., `correctness`, `relevance`), with optional label, score, and explanation

The typical flow: export a dataset → process each example → collect outputs and evaluations → create an experiment with the runs.

## Prerequisites

Proceed directly with the task — run the `ax` command you need. Do NOT check versions, env vars, or profiles upfront.

If an `ax` command fails, troubleshoot based on the error:
- `command not found` or version error → see [references/ax-setup.md](references/ax-setup.md)
- `401 Unauthorized` / missing API key → run `ax profiles show` to inspect the current profile. If the profile is missing or the API key is wrong, follow [references/ax-profiles.md](references/ax-profiles.md) to create/update it. If the user doesn't have their key, direct them to https://app.arize.com/admin > API Keys
- Space unknown → run `ax spaces list` to pick by name, or ask the user
- Project unclear → ask the user, or run `ax projects list -o json --limit 100` and present as selectable options
- **Security:** Never read `.env` files or search the filesystem for credentials. Use `ax profiles` for Arize credentials and `ax ai-integrations` for LLM provider keys. If credentials are not available through these channels, ask the user.
- **CRITICAL — Never fabricate outputs:** When running an experiment, you MUST call the real model API specified by the user for every dataset example. Never fabricate, simulate, or hardcode model outputs, latencies, or evaluation scores. If you cannot call the API (missing SDK, missing credentials, network error), stop and tell the user what is needed before proceeding.

## List Experiments: `ax experiments list`

Browse experiments, optionally filtered by dataset. Output goes to stdout.

```bash
ax experiments list
ax experiments list --dataset DATASET_NAME --space SPACE --limit 20   # DATASET_NAME: name or ID (name preferred)
ax experiments list --cursor CURSOR_TOKEN
ax experiments list -o json
```

### Flags

| Flag | Type | Default | Description |
|------|------|---------|-------------|
| `--dataset` | string | none | Filter by dataset |
| `--name, -n` | string | none | Substring filter on experiment name |
| `--limit, -l` | int | 15 | Max results (1-100) |
| `--cursor` | string | none | Pagination cursor from previous response |
| `-o, --output` | string | table | Output format: table, json, csv, parquet, or file path |

## Get Experiment: `ax experiments get`

Quick metadata lookup -- returns experiment name, linked dataset/version, and timestamps.

```bash
ax experiments get NAME_OR_ID
ax experiments get NAME_OR_ID -o json
ax experiments get NAME_OR_ID --dataset DATASET_NAME --space SPACE   # required when using experiment name instead of ID
```

### Flags

| Flag | Type | Default | Description |
|------|------|---------|-------------|
| `NAME_OR_ID` | string | required | Experiment name or ID (positional) |
| `--dataset` | string | none | Dataset name or ID (required if using experiment name instead of ID) |
| `--space` | string | none | Space name or ID (required if using dataset name instead of ID) |
| `-o, --output` | string | table | Output format |

### Response fields

| Field | Type | Description |
|-------|------|-------------|
| `id` | string | Experiment ID |
| `name` | string | Experiment name |
| `dataset_id` | string | Linked dataset ID |
| `dataset_version_id` | string | Specific dataset version used |
| `experiment_traces_project_id` | string | Project where experiment traces are stored |
| `created_at` | datetime | When the experiment was created |
| `updated_at` | datetime | Last modification time |

## Export Experiment: `ax experiments export`

Download all runs to a file. By default uses the REST API; pass `--all` to use Arrow Flight for bulk transfer.

```bash
# EXPERIMENT_NAME, DATASET_NAME: name or ID (name preferred)
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE
# -> experiment_abc123_20260305_141500/runs.json

ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --all
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --output-dir ./results
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq '.[0]'
```

### Flags

| Flag | Type | Default | Description |
|------|------|---------|-------------|
| `NAME_OR_ID` | string | required | Experiment name or ID (positional) |
| `--dataset` | string | none | Dataset name or ID (required if using experiment name instead of ID) |
| `--space` | string | none | Space name or ID (required if using dataset name instead of ID) |
| `--all` | bool | false | Use Arrow Flight for bulk export (see below) |
| `--output-dir` | string | `.` | Output directory |
| `--stdout` | bool | false | Print JSON to stdout instead of file |

### REST vs Flight (`--all`)

- **REST** (default): Lower friction -- no Arrow/Flight dependency, standard HTTPS ports, works through any corporate proxy or firewall. Limited to 500 runs per page.
- **Flight** (`--all`): Required for experiments with more than 500 runs. Uses gRPC+TLS on a separate host/port (`flight.arize.com:443`) which some corporate networks may block.

**Agent auto-escalation rule:** If a REST export returns exactly 500 runs, the result is likely truncated. Re-run with `--all` to get the full dataset.

Output is a JSON array of run objects:

```json
[
  {
    "id": "run_001",
    "example_id": "ex_001",
    "output": "The answer is 4.",
    "evaluations": {
      "correctness": { "label": "correct", "score": 1.0 },
      "relevance": { "score": 0.95, "explanation": "Directly answers the question" }
    },
    "metadata": { "model": "gpt-4o", "latency_ms": 1234 }
  }
]
```

## Create Experiment: `ax experiments create`

Create a new experiment with runs from a data file.

```bash
ax experiments create --name "gpt-4o-baseline" --dataset DATASET_NAME --space SPACE --file runs.json
ax experiments create --name "claude-test" --dataset DATASET_NAME --space SPACE --file runs.csv
```

### Flags

| Flag | Type | Required | Description |
|------|------|----------|-------------|
| `--name, -n` | string | yes | Experiment name |
| `--dataset` | string | yes | Dataset to run the experiment against |
| `--space, -s` | string | no | Space name or ID (required if using dataset name instead of ID) |
| `--file, -f` | path | yes | Data file with runs: CSV, JSON, JSONL, or Parquet |
| `-o, --output` | string | no | Output format |

### Passing data via stdin

Use `--file -` to pipe data directly — no temp file needed:

```bash
echo '[{"example_id": "ex_001", "output": "Paris"}]' | ax experiments create --name "my-experiment" --dataset DATASET_NAME --space SPACE --file -

# Or with a heredoc
ax experiments create --name "my-experiment" --dataset DATASET_NAME --space SPACE --file - << 'EOF'
[{"example_id": "ex_001", "output": "Paris"}]
EOF
```

### Required columns in the runs file

| Column | Type | Required | Description |
|--------|------|----------|-------------|
| `example_id` | string | yes | The dataset example's **top-level `id`** from `ax datasets export` |
| `output` | string | yes | The model/system output for this example |

Additional columns are passed through as `additionalProperties` on the run.

> **`example_id` must be the Arize row id** — the top-level `id` field on each exported dataset example (`ex["id"]`). Do **not** use a value nested inside the example's input fields or `additional_properties`; a wrong value fails silently or attaches the run to the wrong example. Export the dataset and inspect the top-level `id` field before creating runs.

> **⚠️ Inline evaluations in the create file do NOT attach as scores.** `create` only reads `example_id` and `output`; every other column — including an `evaluations` object — is stored as a passthrough additional field, **not** as an experiment evaluation, and will **not** appear as a score in the UI. This fails silently (no error). To attach scores/labels, create the experiment first, then run `ax experiments annotate-runs`. The `evaluations` object in the schemas below is the **export (read)** shape returned once annotations exist — it is not an input to `create`.

## Delete Experiment: `ax experiments delete`

```bash
ax experiments delete NAME_OR_ID
ax experiments delete NAME_OR_ID --dataset DATASET_NAME --space SPACE   # required when using experiment name instead of ID
ax experiments delete NAME_OR_ID --force   # skip confirmation prompt
```

### Flags

| Flag | Type | Default | Description |
|------|------|---------|-------------|
| `NAME_OR_ID` | string | required | Experiment name or ID (positional) |
| `--dataset` | string | none | Dataset name or ID (required if using experiment name instead of ID) |
| `--space` | string | none | Space name or ID (required if using dataset name instead of ID) |
| `--force, -f` | bool | false | Skip confirmation prompt |

## Annotate Runs: `ax experiments annotate-runs`

**This is the required step to attach evaluation scores/labels to an experiment and make them show up in the UI.** Evaluations cannot be attached through `create`; see the warning under Create Experiment. You write them here, after the experiment exists. Upsert semantics — resubmitting the same annotation `name` for the same run overwrites the previous value. Up to 1000 runs per request; unmatched record IDs are silently ignored.

```bash
ax experiments annotate-runs NAME_OR_ID --file annotations.json --dataset DATASET_NAME --space SPACE
ax experiments annotate-runs NAME_OR_ID --file annotations.csv --dataset DATASET_NAME --space SPACE
```

### Annotation file schema

A JSON array; each item annotates one run:

```json
[
  {
    "record_id": "run_001",
    "values": [
      { "name": "correctness", "label": "correct", "score": 1.0 },
      { "name": "relevance", "score": 0.95, "text": "Directly answers the question" }
    ]
  }
]
```

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `record_id` | string | yes | The **experiment run ID** (the run's `id` from `ax experiments export`) — **not** the `example_id` |
| `values` | array | yes | One or more annotation dicts, each with a `name` plus at least one of `score`, `label`, or `text` |
| `values[].name` | string | yes | Annotation/evaluation name (e.g., `correctness`) — becomes the score column in the UI |
| `values[].score` | number | no | Numeric score (e.g., `0.0`–`1.0`) |
| `values[].label` | string | no | Categorical label (e.g., `correct`, `incorrect`) |
| `values[].text` | string | no | Freeform explanation |

> `record_id` keys on the **run** id, which only exists after `create`. So the order is always: `create` → `export` (to read each run's `id`) → build annotations → `annotate-runs`.

### Flags

| Flag | Type | Required | Description |
|------|------|----------|-------------|
| `NAME_OR_ID` | string | yes | Experiment name or ID (positional) |
| `--file, -f` | path | yes | Annotation file: JSON, JSONL, CSV, or Parquet (use `-` for stdin) |
| `--dataset` | string | yes | Dataset name or ID (required when using experiment name instead of ID) |
| `--space` | string | no | Space name or ID |

## Experiment Run Schema

Each run corresponds to one dataset example. **On `create`, only `example_id` and `output` are consumed** — `evaluations` shown here is the shape `export` returns *after* you attach scores via `annotate-runs`; it is not an input to `create`.

```json
{
  "example_id": "required on create -- the dataset example's top-level id",
  "output": "required on create -- the model/system output for this example",
  "evaluations": {
    "metric_name": {
      "label": "optional string label (e.g., 'correct', 'incorrect')",
      "score": "optional numeric score (e.g., 0.95)",
      "explanation": "optional freeform text"
    }
  },
  "metadata": {
    "model": "gpt-4o",
    "temperature": 0.7,
    "latency_ms": 1234
  }
}
```

### Evaluation fields

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `label` | string | no | Categorical classification (e.g., `correct`, `incorrect`, `partial`) |
| `score` | number | no | Numeric quality score (e.g., 0.0 - 1.0) |
| `explanation` | string | no | Freeform reasoning for the evaluation |

At least one of `label`, `score`, or `explanation` should be present per evaluation.

## Workflows

### Run an experiment against a dataset

1. Find or create a dataset:
   ```bash
   ax datasets list --space SPACE
   ax datasets export DATASET_NAME --space SPACE --stdout | jq 'length'
   ```
2. Export the dataset examples:
   ```bash
   ax datasets export DATASET_NAME --space SPACE
   ```
3. Call the real model API for each example and collect outputs. Use `ax datasets export --stdout` to pipe examples directly into an inference script:

   ```bash
   ax datasets export DATASET_NAME --space SPACE --stdout | python3 infer.py > runs.json
   ```

   Write `infer.py` to read examples from stdin, call the target model, and write runs JSON to stdout. The script below is a template — first inspect the exported dataset JSON to find the correct input field name, then uncomment the provider block the user wants:

   ```python
   import json, sys, time

   examples = json.load(sys.stdin)
   runs = []

   for ex in examples:
       # Inspect the exported JSON to find the right field (e.g. "input", "question", "prompt")
       user_input = ex.get("input") or ex.get("question") or ex.get("prompt") or str(ex)

       start = time.time()

       # === CALL THE REAL MODEL API HERE — never fabricate or simulate ===
       # Uncomment and adapt the provider block the user requested:
       #
       # OpenAI (pip install openai  — uses OPENAI_API_KEY env var):
       #   from openai import OpenAI
       #   resp = OpenAI().chat.completions.create(
       #       model="gpt-4o",
       #       messages=[{"role": "user", "content": user_input}]
       #   )
       #   output_text = resp.choices[0].message.content
       #
       # Anthropic (pip install anthropic  — uses ANTHROPIC_API_KEY env var):
       #   import anthropic
       #   resp = anthropic.Anthropic().messages.create(
       #       model="claude-sonnet-4-6", max_tokens=1024,
       #       messages=[{"role": "user", "content": user_input}]
       #   )
       #   output_text = resp.content[0].text
       #
       # Google Gemini (pip install google-genai  — uses GOOGLE_API_KEY env var):
       #   from google import genai
       #   resp = genai.Client().models.generate_content(
       #       model="gemini-2.5-pro", contents=user_input
       #   )
       #   output_text = resp.text
       #
       # Custom / OpenAI-compatible proxy (pip install openai — uses CUSTOM_BASE_URL + CUSTOM_API_KEY env vars):
       # Use this for Azure OpenAI, NVIDIA NIM, local Ollama, or any OpenAI-compatible endpoint,
       # including a test integration proxy. Matches the `custom` provider in `ax ai-integrations create`.
       #   import os
       #   from openai import OpenAI
       #   resp = OpenAI(
       #       base_url=os.environ["CUSTOM_BASE_URL"],          # e.g. https://my-proxy.example.com/v1
       #       api_key=os.environ.get("CUSTOM_API_KEY", "none"),
       #   ).chat.completions.create(
       #       model=os.environ.get("CUSTOM_MODEL", "default"),
       #       messages=[{"role": "user", "content": user_input}]
       #   )
       #   output_text = resp.choices[0].message.content

       latency_ms = round((time.time() - start) * 1000)
       runs.append({
           "example_id": ex["id"],
           "output": output_text,
           "metadata": {"model": "MODEL_NAME", "latency_ms": latency_ms}
       })
       print(f"  {ex['id']}: {latency_ms}ms", file=sys.stderr)

   json.dump(runs, sys.stdout, indent=2)
   ```

   **Before running:** install the provider SDK (`pip install openai` / `anthropic` / `google-genai`) and ensure the API key is set as an environment variable in your shell. If you cannot access the API, stop and tell the user what is needed.

4. Verify the runs file:
   ```bash
   python3 -c "import json; runs=json.load(open('runs.json')); print(f'{len(runs)} runs'); print(json.dumps(runs[0], indent=2))"
   ```
   Each run must have `example_id` (the dataset row's top-level `id`) and `output`. `metadata` is optional. **Do not put `evaluations` here** — `create` ignores them; scores are attached in steps 7–9 below.
5. Create the experiment:
   ```bash
   ax experiments create --name "gpt-4o-baseline" --dataset DATASET_NAME --space SPACE --file runs.json
   ```
6. Verify: `ax experiments get "gpt-4o-baseline" --dataset DATASET_NAME --space SPACE`

   **Attach evaluation scores (required for scores to show in the UI).** Evaluations do **not** come from the create file — you attach them with `annotate-runs`, which keys on each run's `id` (assigned at create time), so you must export first to learn those IDs.

7. Export the experiment to structured data so you can read each run's `id` alongside its `example_id`. Confirm that the exported run records include both fields.
8. Build the annotation file with structured JSON handling, keyed by `record_id` (the run `id`). Score/label each run via an LLM-as-judge, a code check, or human review; never fabricate scores. Emit this shape:
   ```json
   [
     {
       "record_id": "RUN_ID_FROM_EXPERIMENT_EXPORT",
       "values": [
         { "name": "correctness", "score": 1.0, "label": "correct" }
       ]
     }
   ]
   ```
9. Attach the scores with `ax experiments annotate-runs ... --file annotations.json`, then export or inspect the experiment to confirm the evaluations are attached.
   The scores now render in the experiment view in the Arize UI.

### Compare two experiments

1. Export both experiments:
   ```bash
   ax experiments export "experiment-a" --dataset DATASET_NAME --space SPACE --stdout > a.json
   ax experiments export "experiment-b" --dataset DATASET_NAME --space SPACE --stdout > b.json
   ```
2. Compare evaluation scores by `example_id`:
   ```bash
   # Average correctness score for experiment A
   jq '[.[] | .evaluations.correctness.score] | add / length' a.json

   # Same for experiment B
   jq '[.[] | .evaluations.correctness.score] | add / length' b.json
   ```
3. Find examples where results differ:
   ```bash
   jq -s '.[0] as $a | .[1][] | . as $run |
     {
       example_id: $run.example_id,
       b_score: $run.evaluations.correctness.score,
       a_score: ($a[] | select(.example_id == $run.example_id) | .evaluations.correctness.score)
     }' a.json b.json
   ```
4. Score distribution per evaluator (pass/fail/partial counts):
   ```bash
   # Count by label for experiment A
   jq '[.[] | .evaluations.correctness.label] | group_by(.) | map({label: .[0], count: length})' a.json
   ```
5. Find regressions (examples that passed in A but fail in B):
   ```bash
   jq -s '
     [.[0][] | select(.evaluations.correctness.label == "correct")] as $passed_a |
     [.[1][] | select(.evaluations.correctness.label != "correct") |
       select(.example_id as $id | $passed_a | any(.example_id == $id))
     ]
   ' a.json b.json
   ```

**Statistical significance note:** Score comparisons are most reliable with ≥ 30 examples per evaluator. With fewer examples, treat the delta as directional only — a 5% difference on n=10 may be noise. Report sample size alongside scores: `jq 'length' a.json`.

### Download experiment results for analysis

1. `ax experiments list --dataset DATASET_NAME --space SPACE` -- find experiments
2. `ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE` -- download to file
3. Parse: `jq '.[] | {example_id, score: .evaluations.correctness.score}' experiment_*/runs.json`

### Pipe export to other tools

```bash
# Count runs
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq 'length'

# Extract all outputs
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq '.[].output'

# Get runs with low scores
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq '[.[] | select(.evaluations.correctness.score < 0.5)]'

# Convert to CSV
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq -r '.[] | [.example_id, .output, .evaluations.correctness.score] | @csv'
```

## Related Skills

- **arize-dataset**: Create or export the dataset this experiment runs against → use `arize-dataset` first
- **arize-prompts**: Store and version the prompt template in Prompt Hub (`ax prompts`) before or after experiments
- **arize-prompt-optimization**: Use experiment results to improve prompts → next step is `arize-prompt-optimization`
- **arize-trace**: Inspect individual span traces for failing experiment runs → use `arize-trace`
- **arize-link**: Generate clickable UI links to traces from experiment runs → use `arize-link`

## Troubleshooting

| Problem | Solution |
|---------|----------|
| `ax: command not found` | See [references/ax-setup.md](references/ax-setup.md) |
| `401 Unauthorized` | API key is wrong, expired, or doesn't have access to this space. Fix the profile using [references/ax-profiles.md](references/ax-profiles.md). |
| `No profile found` | No profile is configured. See [references/ax-profiles.md](references/ax-profiles.md) to create one. |
| `Experiment not found` | Verify experiment name with `ax experiments list --space SPACE` |
| `Invalid runs file` | Each run must have `example_id` and `output` fields |
| `example_id mismatch` | `example_id` must be the dataset row's **top-level `id`** from `ax datasets export` — not a value nested in the example's fields or `additional_properties`. Export the dataset and inspect the top-level `id` field. |
| Runs created but no scores / evals in the UI | Evaluations in the create file are silently ignored. Attach them with `ax experiments annotate-runs` (keyed by run `id`) after creating the experiment — see the workflow steps 7–9. |
| `annotate-runs` reports success but nothing changes | `record_id` must be the **run `id`** (from `ax experiments export`), not the `example_id`. Unmatched record IDs are silently ignored. |
| `No runs found` | Export returned empty -- verify experiment has runs via `ax experiments get` |
| `Dataset not found` | The linked dataset may have been deleted; check with `ax datasets list` |

## Save Credentials for Future Use

See [references/ax-profiles.md](references/ax-profiles.md) § Save Credentials for Future Use.