envloop-cli: EnvLoop CLI and Python SDK

Install the public client to run agent Jobs, inspect Trials, manage training and checkpoints, and deploy or call inference endpoints. The el and envloop commands are equivalent. The Python import package is envloop.

Install

Use a Python virtual environment:

python -m pip install --upgrade envloop-cli
el --help

The standard installation includes training support:

python -c 'from envloop import Client, training; print("EnvLoop SDK ready")'

The package installs the training protocol client by default. You do not need a source checkout, GitHub account, local execution backend, Docker, or a GPU to install the client.

Connect

Use an authorized EnvLoop account and project. Browser authorization:

el auth login
el auth status --check

For an existing project API key, use the masked configuration wizard:

el auth config
el auth status --check

CI can read credentials from ENVLOOP_API_KEY and connection settings from ENVLOOP_BASE_URL, ENVLOOP_WORKSPACE_ID and ENVLOOP_PROJECT_ID. Keep keys in your secret manager. Public API access is scoped to the authorized project.

Explore your project

el job list --limit 5
el job list --type train --limit 5
el trail list --limit 5
el trail list --type train --job-id JOB_ID
el checkpoint list --limit 5
el job list --type inference

Query a returned Job ID with el job get JOB_ID and el job result JOB_ID. Trial commands use the spelling trail, for example el trail artifact list TRIAL_ID. Use each command's --help for options.

For the Python SDK:

from envloop import Client

with Client.remote() as client:
    print(client.jobs.list(limit=5))
    print(client.trials.list(type="train", limit=5))

Submitting work, training a model or starting inference can allocate billable resources. Follow the task-specific guide and retain the outputs you need before cleaning up temporary resources.

Sandbox jobs

With a matching platform and a published envhub/base ANS Runner, submit a Sandbox Job without a Dataset:

el job submit --type sandbox --runner base --command 'python --version'
el job submit --type sandbox --runner base --image python:3.11 --wait
el job list --type sandbox
el job log JOB_ID --follow

The platform resolves base to the registered envhub/base@main source ref and freezes its immutable Revision. The Sandbox image defaults to python:3.11; --image accepts an image name, tag or digest. The execution provider is configured by the server. Job logs retain the actual captured output and collection metadata. Failures include a code, readable explanation, HTTP status when available, and recovery guidance without echoing arbitrary response bodies or credentials.

Existing installations

This distribution provides both the CLI and the envloop import package. Use a fresh environment if you previously installed the separate source distribution named envloop, so two distributions do not own the same Python module files. Existing client configuration is reused; installing a package does not change your account permissions or move remote resources. Check el --help when upgrading because command groups may differ from your earlier installation.

Documentation

• English documentation
• 中文文档
• Installation
• CLI reference
• Training and deployment
• Agent entry: skill.md