arize-experiment

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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.