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. 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.

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
`--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
`-p, --profile`	string	default	Configuration profile

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
`-p, --profile`	string	default	Configuration profile

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
`-p, --profile`	string	default	Configuration profile

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
`-p, --profile`	string	no	Configuration profile

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	ID of the dataset example this run corresponds to
`output`	string	yes	The model/system output for this example

Additional columns are passed through as additionalProperties on the run.

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
`-p, --profile`	string	default	Configuration profile

Experiment Run Schema

Each run corresponds to one dataset example:

{
  "example_id": "required -- links to dataset example",
  "output": "required -- 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

Find or create a dataset:

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

Export the dataset examples:

ax datasets export DATASET_NAME --space SPACE

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.

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 and output. Optional fields: evaluations, metadata.

Create the experiment:

ax experiments create --name "gpt-4o-baseline" --dataset DATASET_NAME --space SPACE --file runs.json

Verify: ax experiments get "gpt-4o-baseline" --dataset DATASET_NAME --space SPACE

Compare two experiments

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

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

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

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

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

ax experiments list --dataset DATASET_NAME --space SPACE -- find experiments
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE -- download to file
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-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`	Ensure `example_id` values match IDs from the dataset (export dataset to verify)
`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.

arize-experiment

Arize Experiment Skill

Concepts

Prerequisites

List Experiments: ax experiments list

Flags

Get Experiment: ax experiments get

Flags

Response fields

Export Experiment: ax experiments export

Flags

REST vs Flight (--all)

Create Experiment: ax experiments create

Flags

Passing data via stdin

Required columns in the runs file

Delete Experiment: ax experiments delete

Flags

Experiment Run Schema

Evaluation fields

Workflows

Run an experiment against a dataset

Compare two experiments

Download experiment results for analysis

Pipe export to other tools

Related Skills

Troubleshooting

Save Credentials for Future Use

List Experiments: `ax experiments list`

Get Experiment: `ax experiments get`

Export Experiment: `ax experiments export`

REST vs Flight (`--all`)

Create Experiment: `ax experiments create`

Delete Experiment: `ax experiments delete`