Arize Experiment

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

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

Response fields

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

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

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

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

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

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 and output. Optional fields: evaluations, metadata. 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

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-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

Save Credentials for Future Use

See references/ax-profiles.md § Save Credentials for Future Use.

ax Profile Setup

Consult this when authentication fails (401, missing profile, missing API key). Do NOT run these checks proactively.

Use this when there is no profile, or a profile has incorrect settings (wrong API key, wrong region, etc.).

1. Inspect the current state

ax profiles show

Look at the output to understand what's configured:

• API Key: (not set) or missing → key needs to be created/updated
• No profile output or "No profiles found" → no profile exists yet
• Connected but getting 401 Unauthorized → key is wrong or expired
• Connected but wrong endpoint/region → region needs to be updated

2. Fix a misconfigured profile

If a profile exists but one or more settings are wrong, patch only what's broken.

Never pass a raw API key value as a flag. Always reference it via the ARIZE_API_KEY environment variable. If the variable is not already set in the shell, instruct the user to set it first, then run the command:

# If ARIZE_API_KEY is already exported in the shell:
ax profiles update --api-key $ARIZE_API_KEY

# Fix the region (no secret involved — safe to run directly)
ax profiles update --region us-east-1b

# Fix both at once
ax profiles update --api-key $ARIZE_API_KEY --region us-east-1b

update only changes the fields you specify — all other settings are preserved. If no profile name is given, the active profile is updated.

3. Create a new profile

If no profile exists, or if the existing profile needs to point to a completely different setup (different org, different region):

Always reference the key via `$ARIZE_API_KEY`, never inline a raw value.

# Requires ARIZE_API_KEY to be exported in the shell first
ax profiles create --api-key $ARIZE_API_KEY

# Create with a region
ax profiles create --api-key $ARIZE_API_KEY --region us-east-1b

# Create a named profile
ax profiles create work --api-key $ARIZE_API_KEY --region us-east-1b

To use a named profile with any ax command, add -p NAME:

ax spans export PROJECT -p work

4. Getting the API key

Never ask the user to paste their API key into the chat. Never log, echo, or display an API key value.

If ARIZE_API_KEY is not already set, instruct the user to export it in their shell:

export ARIZE_API_KEY="..."   # user pastes their key here in their own terminal

They can find their key at https://app.arize.com/admin > API Keys. Recommend they create a scoped service key (not a personal user key) — service keys are not tied to an individual account and are safer for programmatic use. Keys are space-scoped — make sure they copy the key for the correct space.

Once the user confirms the variable is set, proceed with ax profiles create --api-key $ARIZE_API_KEY or ax profiles update --api-key $ARIZE_API_KEY as described above.

5. Verify

After any create or update:

ax profiles show

Confirm the API key and region are correct, then retry the original command.

Space

There is no profile flag for space. Save it as an environment variable — accepts a space name (e.g., my-workspace) or a base64 space ID (e.g., U3BhY2U6...). Find yours with ax spaces list -o json.

macOS/Linux — add to ~/.zshrc or ~/.bashrc:

export ARIZE_SPACE="my-workspace"    # name or base64 ID

Then source ~/.zshrc (or restart terminal).

Windows (PowerShell):

[System.Environment]::SetEnvironmentVariable('ARIZE_SPACE', 'my-workspace', 'User')

Restart terminal for it to take effect.

Save Credentials for Future Use

At the end of the session, if the user manually provided any credentials during this conversation and those values were NOT already loaded from a saved profile or environment variable, offer to save them.

Skip this entirely if:

• The API key was already loaded from an existing profile or ARIZE_API_KEY env var
• The space was already set via ARIZE_SPACE env var
• The user only used base64 project IDs (no space was needed)

How to offer: Use AskQuestion: "Would you like to save your Arize credentials so you don't have to enter them next time?" with options "Yes, save them" / "No thanks".

If the user says yes:

1. API key — Run ax profiles show to check the current state. Then run ax profiles create --api-key $ARIZE_API_KEY or ax profiles update --api-key $ARIZE_API_KEY (the key must already be exported as an env var — never pass a raw key value).

2. Space — See the Space section above to persist it as an environment variable.

ax CLI — Troubleshooting

Consult this only when an ax command fails. Do NOT run these checks proactively.

Check version first

If ax is installed (not command not found), always run ax --version before investigating further. The version must be 0.14.0 or higher — many errors are caused by an outdated install. If the version is too old, see Version too old below.

ax: command not found

macOS/Linux: 1. Check common locations: ~/.local/bin/ax, ~/Library/Python/*/bin/ax 2. Install: uv tool install arize-ax-cli (preferred), pipx install arize-ax-cli, or pip install arize-ax-cli 3. Add to PATH if needed: export PATH="$HOME/.local/bin:$PATH"

Windows (PowerShell): 1. Check: Get-Command ax or where.exe ax 2. Common locations: %APPDATA%\Python\Scripts\ax.exe, %LOCALAPPDATA%\Programs\Python\Python*\Scripts\ax.exe 3. Install: pip install arize-ax-cli 4. Add to PATH: $env:PATH = "$env:APPDATA\Python\Scripts;$env:PATH"

Version too old (below 0.14.0)

Upgrade: uv tool install --force --reinstall arize-ax-cli, pipx upgrade arize-ax-cli, or pip install --upgrade arize-ax-cli

SSL/certificate error

• macOS: export SSL_CERT_FILE=/etc/ssl/cert.pem
• Linux: export SSL_CERT_FILE=/etc/ssl/certs/ca-certificates.crt
• Fallback: export SSL_CERT_FILE=$(python -c "import certifi; print(certifi.where())")

Subcommand not recognized

Upgrade ax (see above) or use the closest available alternative.

Still failing

Stop and ask the user for help.