arize-experiment

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
• 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 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. Never ask the user to paste secrets into chat. For missing credentials, see references/ax-profiles.md.
• 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.

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.

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.

# 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:

[
  {
    "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.

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:

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

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.

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:

[
  {
    "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.

{
  "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:

   ax datasets list --space SPACE
   ax datasets export DATASET_NAME --space SPACE --stdout | jq 'length'
   

2. Export the dataset examples:

   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:

   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:

   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:

   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:

   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:

   [
     {
       "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:

   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:

   # 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:

   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):

   # 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):

   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

# 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
401 Unauthorized	API key is wrong, expired, or doesn't have access to this space. Fix the profile using references/ax-profiles.md.
No profile found	No profile is configured. See 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 § Save Credentials for Future Use.