Demos
Use this page to choose a public AGILAB demo route. It is a router, not a quick-start guide.
Choose a demo
What each route is for
AGILAB demo: use AGILAB Demo for the self-serve public Hugging Face Spaces route for the AGILAB web UI. It publishes the lightweight
flight_projectandmeteo_forecast_projectpaths, so use it as the public first proof forPROJECT->ORCHESTRATE->PIPELINE->ANALYSIS, includingview_maps,view_forecast_analysis, andview_release_decision.agi-core demo: notebook-first runtime path. Use this if you want the smaller
AgiEnv/AGI.run(...)surface before the web UI.Notebook migration demo: use Notebook Migration Example when you want the notebook-to-AGILAB story: source notebooks, migrated
lab_steps.toml,pipeline_view.dot, exported forecast artifacts, and the hostedmeteo_forecast_projectanalysis route.Advanced Proof Pack: use Advanced Proof Pack after the first demo when you want the deeper packaged proof routes:
data_io_2026_project,execution_pandas_project/execution_polars_project, UAV queue analysis withuav_relay_queue_project,service_modepreviews,inter_project_dagpreviews,mlflow_auto_trackingpreviews,resilience_failure_injectionpreviews,train_then_servepreviews, Data Connectors, and Release Proof.Industrial optimization examples: use Industrial Optimization Examples when your apps repository includes
sb3_trainer_projectand you want the advanced SB3 routes: Active Mesh Optimization, MLflow auto-tracking, multi-app DAGs, resilience/failure injection, and train-then-serve contracts.Quick start: the safest truthful first proof of the full product path. Use Quick-Start if you want the recommended local run instead of a public demo.
Four short demos
Use these as narrow product demos. They are intentionally generic and should not depend on private apps or app-specific claims.
The static scenario contract is available as JSON:
uv --preview-features extra-build-dependencies run python tools/public_proof_scenarios.py --compact
uv --preview-features extra-build-dependencies run python tools/public_proof_scenarios.py --first-proof-json first-proof.json --hf-smoke-json hf-space-smoke.json --output public-proof-scenarios.json
- Local app proof
Install the released package or use the source checkout, then run the public first proof:
python -m pip install agilab agilab first-proof --json --max-seconds 60
Stop when the command exits successfully and writes
run_manifest.json. The same route is available in the UI by followingPROJECT->ORCHESTRATE->ANALYSISwithflight_project.- Distributed worker route
Use the same public app, then switch ORCHESTRATE from the local path to the configured worker or SSH-host path. Keep the demo bounded: prove that worker packaging is staged, service health gates report status, and outputs land under the normal log directory.
uv --preview-features extra-build-dependencies run python tools/service_health_check.py --format json
Stop when the health gate is explicit. This is a worker/operator demo, not a certification of every possible remote topology.
- MLflow tracking route
Start with the packaged preview when you want a short, deterministic proof:
uv --preview-features extra-build-dependencies run python src/agilab/examples/mlflow_auto_tracking/preview_mlflow_auto_tracking.py --output-dir /tmp/mlflow_auto_tracking_preview
Add
--with mlflowto theuvcommand when you want the same evidence logged into a local MLflow store. The demo objective is to show that AGILAB keeps setup, execution, artifacts, and visible results together while MLflow remains the tracking system of record when it is used.Stop when the pipeline artifacts and the MLflow run link point to the same experiment evidence.
- Resilience failure-injection route
Use the packaged preview when the demo objective is strategy comparison under a controlled degradation event:
uv --preview-features extra-build-dependencies run python src/agilab/examples/resilience_failure_injection/preview_resilience_failure_injection.py --output /tmp/resilience_preview.json
Stop when the route ranking before failure, route ranking after failure, and recommended fixed/replanned/search/policy response are visible in the same JSON payload. The preview is deterministic and does not train a real policy.
- Train-then-serve route
Use the packaged preview when the demo objective is the handoff from experiment evidence to a service-ready contract:
uv --preview-features extra-build-dependencies run python src/agilab/examples/train_then_serve/preview_train_then_serve.py --output-dir /tmp/train_then_serve_preview
Stop when
service_contract.json,prediction_sample.json, andservice_health.jsonare visible. The preview is deterministic and does not start persistent workers.- Notebook migration route
Use the packaged migration example when the demo objective is notebook consolidation rather than execution speed:
uv --preview-features extra-build-dependencies run python src/agilab/examples/notebook_to_dask/preview_notebook_to_dask.py --output /tmp/notebook_to_dask_preview.json
Then open Notebook Migration Example or switch the hosted UI to
meteo_forecast_projectand openview_forecast_analysis. Stop when the notebook source, migrated pipeline shape, exported artifacts, and reusable analysis view are visible together.
Demo naming
Keep the two public AGILAB demo lanes separate:
flight_projectis the default hosted/newcomer demo. It is a lightweight data-generation path used to prove the core UI and local execution flow quickly.meteo_forecast_projectis the second hosted demo. It is a lightweight notebook-migration path with source notebooks, forecast artifacts, and release-decision views.uav_relay_queue_projectis the UAV Relay Queue RL demo. It is the advanced domain scenario and should not be described as the default hosted app.data_io_2026_projectand the execution playground apps are advanced proof routes. They are public built-in demos, but they should not replaceflight_projectas the default hosted/newcomer app.