Architecture in 5 minutes

AGILAB is a reproducible AI/ML experimentation workbench for engineering teams, bridging local interactive development, distributed execution, and result analysis.

Use this page for the mental model before opening the detailed architecture reference.

One control path

User
  |
  v
Streamlit UI, CLI wrappers, or notebook entry points
  |
  v
AgiEnv: settings, project selection, app paths, logs, local workspace
  |
  v
AGI facade: install, get_distrib, run, service actions
  |
  v
agi-node / agi-cluster: package workers, dispatch work, start local or SSH workers
  |
  v
Dask back-plane and optional MLflow tracking
  |
  v
Artifacts, run manifests, app pages, and ANALYSIS views

What each layer owns

Layer

Responsibility

Proof to look for

UI / CLI / notebook

Capture user intent and keep PROJECT, ORCHESTRATE, PIPELINE, and ANALYSIS on one visible path.

agilab first-proof --json and the hosted Hugging Face demo.

AgiEnv

Resolve app paths, settings, logs, environment variables, and per-user workspace files.

run_manifest.json plus app settings under ~/.agilab/apps.

AGI facade

Provide the public install, distribution, execution, and service actions used by both UI and examples.

Example scripts in src/agilab/examples.

worker packaging

Build and stage worker runtimes so manager code and worker code stay isolated.

Worker copies under ~/wenv/<app>_worker.

Dask / MLflow

Dask is the worker dispatch back-plane; MLflow is an optional tracking system AGILAB can integrate with, not replace.

Dask logs, MLflow runs, artifacts, and ANALYSIS pages.

Boundary

AGILAB does not replace MLflow, Kubeflow, cloud deployment platforms, security gateways, or production rollback systems. It keeps the engineering experiment path reproducible and visible, then exports evidence and artifacts that those deployment-focused systems can consume.

Next pages