agi-app-multi-dag 2026.6.4

AGILAB cross-app DAG preview showing artifact handoffs between demo projects

agi-app-multi-dag

agi-app-multi-dag publishes the multi_app_dag_project AGILAB app as a self-contained PyPI payload. It is a read-only workflow-contract example rather than a domain worker benchmark.

Purpose

Use this package to understand how AGILAB can connect several app projects with explicit artifact handoffs. The bundled DAG shows a flight stage producing a summary artifact that a weather stage can consume.

Installed Project

The distribution name is agi-app-multi-dag; the AGILAB project name is multi_app_dag_project. The package exposes both multi_app_dag and multi_app_dag_project through the agilab.apps entry point group, so AgiEnv(app="multi_app_dag_project") resolves the project without a monorepo checkout.

Install

pip install agi-app-multi-dag

Most users get this package through agi-apps, agilab[ui], or agilab[examples]; direct installation is useful when validating one app package in isolation.

Run In AGILAB

Select multi_app_dag_project, open WORKFLOW, choose the multi-app DAG template, and inspect the runner-state preview. This package is primarily for workflow review; use flight_telemetry_project or weather_forecast_project when you want to execute concrete worker code.

Expected Inputs

The package ships a DAG template that names the producer and consumer projects. No private data, cluster, service mode, or live external service is required for the default preview.

Expected Outputs

The preview writes a runner-state JSON file under the AGILAB execution logs and renders the runnable versus blocked stages. It does not create a domain reducer artifact because it demonstrates cross-app orchestration, not a worker merge.

Change One Thing

Edit a handoff name in a copied DAG template and reload the preview. The blocked stage should make the missing artifact contract obvious before any downstream execution is attempted.

Scope

This package teaches multi-app DAG contracts. It is not a scheduler replacement, an Airflow/Kubeflow clone, or a production workflow governance layer.