worldjen 0.2.0

WorldJen Python SDK for running evaluation pipelines on video and world models

WorldJen Python SDK

Python SDK and CLI for running WorldJen evaluation on video and world models. Use it to create runs, manage models and API keys, and operate GPU runners from the command line or from your own code.

The SDK provides the same capabilities as the runs, models, and api-keys CLI commands, plus the high-level worldjen.run() for end-to-end pipeline execution. Runner commands (systemd, register, etc.) remain CLI-only.

Requirements: Python 3.10+

Installation

Install the core package (for programmatic runs and CLI):

pip install worldjen

With uv:

uv pip install worldjen

If you use a virtual environment, activate it so the worldjen CLI is on your PATH (e.g. source .venv/bin/activate).

To run the runner (Celery worker for GPU execution), install the optional runner extra:

pip install worldjen[runner]
# or
uv pip install worldjen[runner]

The runner requires a Linux system with systemd for service management.

Configuration

Set your API key via worldjen.config() or environment variables:

• WORLDJEN_API_KEY — API key (required for creating runs and for CLI commands that modify data)
• WORLDJEN_VIDEO_DIR — Directory for cached prompts and run outputs (default: ~/.worldjen/data/videos)

import worldjen

worldjen.config(api_key="your-api-key")
worldjen.config(video_dir="/path/to/videos")  # optional

Usage

Dimensions

Use the Dimensions enum when specifying evaluation dimensions:

from worldjen import Dimensions

dims = [Dimensions.SUBJECT_CONSISTENCY, Dimensions.SCENE_CONSISTENCY]
# or strings: dims = ["subject_consistency", "scene_consistency"]

Running a pipeline

Your pipeline must be one of:

• A callable: pipeline(prompt, **kwargs) -> list[frames]
• An object with a generate method: pipeline.generate(prompt, **kwargs) -> list[frames]
• An object with an infer method: pipeline.infer(prompt, **kwargs) -> list[frames]

Frames can be PIL Images or numpy arrays (HWC; the SDK handles writing to video).

import worldjen

def my_pipeline(prompt, **kwargs):
    # Generate frames from prompt (text-to-video)
    ...
    return frames  # list of PIL or numpy arrays

result = worldjen.run(
    my_pipeline,
    dimensions=[worldjen.Dimensions.SUBJECT_CONSISTENCY],
    run_name="my-eval-run",
    model_id="my-org/my-model-checkpoint-xyz",
    wait_for_evals=True,
    # optional pipeline kwargs: num_frames=16, etc.
)

print(result.run_id, result.status, result.video_paths, result.eval_results)
if result.error_message:
    print("Error:", result.error_message)

On failure (run creation, pipeline error, or upload), the run is marked failed and RunResult includes status="failed" and error_message.

Examples

See the examples/ directory for runnable scripts (e.g. LTX-Video with real prompts from worldjen/prompts/ and a single dimension, with notes on Dimensions.ALL and multiple dimensions).

API summary

High-level (primary)

• worldjen.config(api_key=..., video_dir=..., api_url=..., timeout=...) — Set or override config; returns the config object.
• worldjen.run(pipeline, dimensions, run_name=None, model_id=None, wait_for_evals=True, **pipeline_kwargs) — End-to-end: create a run, run your pipeline locally, generate and upload videos, then optionally poll until evals are ready. Returns RunResult(run_id, video_paths, output_dir, eval_results, status, error_message).

Run management (worldjen.runs)

Lower-level API for inspecting and managing runs (equivalent to worldjen runs CLI commands):

• worldjen.runs.create(name, dimensions, run_instructions=None, model_id=None) — Create a run record; returns run ID. Use worldjen.run() for end-to-end execution.
• worldjen.runs.list_runs(status=None, page=1, limit=50) — List runs.
• worldjen.runs.get(run_id) — Fetch run metadata and result data.
• worldjen.runs.cancel(run_id) — Cancel a run.
• worldjen.runs.delete(run_id) — Delete a run.
• worldjen.runs.get_logs(run_id) — Fetch run logs.
• worldjen.runs.get_csv(run_id) — Fetch run CSV as bytes.
• worldjen.runs.get_videos(run_id) — List video metadata (id, prompt, url).
• worldjen.runs.download_videos(run_id, output_dir=".") — Download all run videos to a directory.

Model management (worldjen.models)

• worldjen.models.list_user(sort_by=None) — List your models.
• worldjen.models.list_base(sort_by=None) — List base (suggested) models.
• worldjen.models.list_ref(sort_by=None) — List reference models.
• worldjen.models.create(name, provider, hf_repo_id, model_type, encrypted_tokens=None) — Create a model. For private repos, use --hf-token via CLI or configure in the dashboard.
• worldjen.models.delete(model_id) — Delete a model.

API keys (worldjen.api_keys)

• worldjen.api_keys.list_keys() — List API keys.
• worldjen.api_keys.create(name=None, expires_in=None) — Create an API key.
• worldjen.api_keys.revoke(key_id) — Revoke an API key.

Create and revoke are also available in the dashboard.

CLI

All CLI commands that create or read data accept an API key via the WORLDJEN_API_KEY environment variable or --api-key. Optional: WORLDJEN_API_URL or --api-url.

Runs

• worldjen runs create --name NAME --dimensions DIM1,DIM2 [--model-id ID] — Create a run.
• worldjen runs list [--status STATUS] [--page N] [--limit N] — List runs.
• worldjen runs get RUN_ID — Show run metadata.
• worldjen runs cancel RUN_ID — Cancel a run.
• worldjen runs delete RUN_ID — Soft-delete a run.
• worldjen runs logs RUN_ID — Print run logs.
• worldjen runs csv RUN_ID [--output FILE] — Download run CSV.
• worldjen runs videos RUN_ID — List video metadata (id, prompt, url).
• worldjen runs download-videos RUN_ID [--output-dir DIR] — Download all run videos.

Models

• worldjen models list — List your models.
• worldjen models list-base — List base (suggested) models.
• worldjen models list-ref — List reference models.
• worldjen models create --name NAME --provider P --hf-repo-id REPO --type text_to_video [--hf-token TOKEN] — Create a model. For private repos, pass --hf-token (or set WORLDJEN_HF_TOKEN); the token is encrypted per registered runner so runners can pull the model.
• worldjen models delete MODEL_ID — Delete a model.

API keys

• worldjen api-keys list — List API keys. Create and revoke keys in the dashboard.

Runner

Runner commands require the worldjen[runner] extra. Local commands act on this machine (config file, systemd service). Backend commands act on your account’s runner resources via the API.

Backend (API / your account) — require WORLDJEN_API_KEY or --api-key:

• worldjen runner create [--name NAME] [--data-dir DIR] — Create a runner resource in the backend, register this machine, install the systemd service, and start it.
• worldjen runner delete [--runner-id ID] — Delete the runner from the backend; also stops and uninstalls the local service. Fails if the runner has queued runs.
• worldjen runner list — List runners for your account.

Local (this machine) — act on local config and systemd; Linux with systemd required for install/start/stop/logs:

• worldjen runner register --token TOKEN [--api-url URL] [--config PATH] — Register this machine using a one-time token from the web UI; writes runner.conf, then optionally installs and starts the service.
• worldjen runner install [--data-dir DIR] — Install and start the systemd service (requires an existing runner.conf from a prior register).
• worldjen runner uninstall — Stop and uninstall the systemd service.
• worldjen runner start — Start the installed service.
• worldjen runner stop — Stop the service.
• worldjen runner status — Show systemctl status for the worldjen-runner service.
• worldjen runner logs [-f] [-n N] — Tail logs of the systemd service.

The runner runs as a Celery worker. Jobs are submitted from the web UI or when you create a run and select a runner in the dashboard.

Links

• WorldJen — Web app and dashboard
• Documentation — Full docs and guides