Demos

Use this page to choose a public AGILAB demo route. It is a router, not a quick-start guide.

Choose a demo

agi-core demo AGILAB demo notebook migration demo advanced proof pack

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_telemetry_project and weather_forecast_project paths, so use it as the public first proof for PROJECT -> ORCHESTRATE -> WORKFLOW -> ANALYSIS, including view_maps, view_forecast_analysis, and view_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_stages.toml, pipeline_view.dot, exported forecast artifacts, and the hosted weather_forecast_project analysis route.

  • PyTorch Playground: use PyTorch Playground when you want a browser-visible neural-network lesson that goes beyond a classic playground: live play/pause training, replayable lesson configs, PyTorch code handoff, and an evidence ZIP from the same run.

  • Advanced Proof Pack: use Advanced Proof Pack after the first demo when you want the deeper packaged proof routes: mission_decision_project, the execution_pandas_project Cython worker speedup demo, execution_polars_project, the sqlite_connector_proof database evidence preview, UAV queue analysis with uav_relay_queue_project, service_mode previews, inter_project_dag previews, mlflow_auto_tracking previews, resilience_failure_injection previews, train_then_serve previews, Data Connectors, and Release Proof.

  • Industrial optimization examples: use Industrial Optimization Examples when your apps repository includes sb3_trainer_project and 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.

Short demo routes

Use these as narrow product demos. They are intentionally generic and should not depend on private apps or app-specific claims.

Robot/proof coverage

Every route on this page must be tied to one of the local validation contracts:

  • UI robot: browser-visible AGILAB routes are covered by tools/ui_robot_coverage_contract.py --json. The contract checks the local all-built-in ui-robot-matrix profile, the hosted first-proof visual robot, the hosted install robot, and the configured apps-pages used by flight_telemetry_project, weather_forecast_project, mission_decision_project, execution_pandas_project, execution_polars_project, uav_queue_project, and uav_relay_queue_project.

  • Static/CLI proof: non-browser preview routes are covered by tools/public_proof_scenarios.py --compact. This includes the local package proof, hosted weather proof, MLflow proof, distributed-worker health proof, notebook migration proof, resilience failure-injection proof, train-then-serve proof, and service-mode preview proof.

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 examples profile or use the source checkout, then run the public first proof:

python -m pip install "agilab[examples]"
python -m agilab.lab_run first-proof --json --max-seconds 60

Stop when the command exits successfully and writes run_manifest.json. This package route uses agi-apps as the public app umbrella and resolves the built-in project from the matching per-app package. Install agilab[ui] and rerun with --with-ui when you also want to boot the packaged local pages and agi-pages analysis views. The same route is available in the UI by following PROJECT -> ORCHESTRATE -> ANALYSIS with flight_telemetry_project.

PyTorch Playground teaching route

Use PyTorch Playground when the demo objective is visual ML learning plus reproducible engineering handoff:

agilab app surface pytorch_playground_project --ui streamlit

Stop when the boundary-first panel is visible, press Run instant demo for Instant wow: clean circles, read the start/now replay, try the XOR lesson card, and download the evidence ZIP. The useful proof is not the visual boundary alone; it is the replay token, manifest, generated PyTorch/Lightning snippets, and boundary snapshots attached to that lesson.

SQLite database proof

Use the packaged sqlite_connector_proof preview when the demo objective is reproducible database access without a remote database, Docker, or secrets:

uv --preview-features extra-build-dependencies run python src/agilab/examples/sqlite_connector_proof/preview_sqlite_connector_proof.py --output-dir /tmp/agilab-sqlite-proof

Stop when sqlite_connector_proof.db, promotion_candidates.csv, and database_evidence.json are visible. The evidence records the connector ID, SQLite driver, read-only query mode, schema hash, query hash, row count, result hash, and artifact hashes. This is the public first step before a Postgres, warehouse, or cloud SQL connector.

Cython worker speedup demo

Use Execution Playground when the demo objective is performance engineering rather than a domain story. Select execution_pandas_project, keep the default typed_numeric kernel and Cython setting, run INSTALL then EXECUTE, and inspect the reducer evidence for kernel_mode, kernel_runtime, and dtype_contract. The versioned local kernel proof records 0.620s Python vs 0.002s Cython on 100,000 rows x 32 passes, with matching checksums and a 306x hot-loop speedup.

For a short command-line proof of the compiled hot loop, run:

uv --preview-features extra-build-dependencies run python tools/benchmark_execution_pandas_cython_kernel.py --rows 100000 --compute-passes 32 --repeats 3 --warmups 1

Stop when the Python and Cython checksums match and the report shows the Cython runtime separately. This is a kernel-scoped speedup demo; full AGILAB runs still include CSV reads, dataframe grouping, result writes, worker startup, and optional Dask/process orchestration.

For the real-world version of the same pattern, run flight_telemetry_project. Its Polars ingestion and map/network analysis stay in Python, while the haversine speed kernel records speed_kernel_runtime, speed_dtype_contract, and checksum evidence in the reducer summary.

Rust/PyO3 native-worker preview

Use the packaged native_rust_worker preview when the demo objective is to show AGILAB’s native-worker extension boundary without adding Rust to the base install:

uv --preview-features extra-build-dependencies run python src/agilab/examples/native_rust_worker/preview_native_rust_worker.py --output-dir /tmp/agilab-rust-worker

Stop when native_rust_worker_evidence.json and the generated rust_worker PyO3/maturin skeleton are visible. This is an advanced worker extension preview: AGILAB orchestration and evidence stay in Python, only a measured typed hot kernel moves to Rust, and compiling the generated extension remains an explicit follow-up step.

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 mlflow to the uv command 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, and service_health.json are 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 weather_forecast_project and open view_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_telemetry_project is the default hosted/newcomer demo. It is a lightweight data-generation path used to prove the core UI and local execution flow quickly.

  • weather_forecast_project is the second hosted demo. It is a lightweight notebook-migration path with source notebooks, forecast artifacts, and release-decision views.

  • uav_relay_queue_project is the UAV Relay Queue RL demo. It is the advanced domain scenario and should not be described as the default hosted app.

  • mission_decision_project and the execution playground apps are advanced proof routes. They are public built-in demos, but they should not replace flight_telemetry_project as the default hosted/newcomer app.

See also