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, evaluat

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. 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. 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. 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: Create Experiment: ax experiments create Create a new experiment with runs from a data file. 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: 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 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: 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: 2. Export the dataset examples: 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: 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: 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: Each run must have example id and output . Optional fields: evaluations , metadata . 5. Create the experiment: 6. Verify: ax experiments get "gpt 4o baseline" dataset DATASET NAME space SPACE Compare two experiments 1. Export both experiments: 2. Compare evaluation scores by example id : 3. Find examples where results differ: 4. Score distribution per evaluator (pass/fail/partial counts): 5. Find regressions (examples that passed in A but fail in B): 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 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