AGILAB

AGILAB is an anti-lock-in reproducibility workbench for AI/ML engineering. It turns notebooks and scripts into executable, portable, evidence-backed apps while preserving a notebook export path. That export is an agi-core runtime handoff: you can continue to run the saved project and stage contract with only the stable core runtime, without depending on the AGILAB UI or distributed worker layer. That means you do not lose your work if the AGILAB UI or distributed runtime is no longer the right interface. Those apps can run locally or on distributed workers, and the workflow stays portable: export it back to an agi-core notebook, inspect or adapt the Python stages, and hand off tracking evidence to MLflow when that integration is enabled.

You do not need a cluster to get AGILAB's core value. The primary adoption path is local: turn a notebook or script into a replayable app with evidence, artifacts, analysis views, and a notebook or MLflow handoff. Cluster execution is a scale-out option after that local proof works.

Use it to keep experimental AI work:

• one-command setup
• controlled environments
• local or distributed execution
• reviewable run evidence
• runnable outside the AGILAB UI as agi-core notebooks
• optional MLflow integration

AGILAB complements MLflow and production MLOps platforms. It owns the reproducible execution and analysis layer around them. In short: MLflow tracks experiments; AGILAB transforms notebooks and scripts into executable, portable, evidence-backed AI applications.

Core Flow

Notebook/script → AGILAB app → controlled execution → artifacts + evidence → notebook / MLflow / UI handoff

The flow is reversible where it matters for long-term reuse: WORKFLOW can export the saved pipeline as a runnable agi-core supervisor notebook, so the code, stage order, runtime hints, and review context remain usable through the stable, core runtime if the AGILAB UI or distributed runtime is no longer the right interface for that work.

Start with the public browser preview or the demo chooser:

• AGILAB Space
• Demo chooser
• Local quick start
• Demo capture guide

Featured Opt-In App

• PyTorch Playground is the opt-in classifier playground app for synthetic datasets, hidden-layer activation maps, network diagnostics, and the Loss landscape view. It is a reproducible app project, not a generic app-agnostic analysis page, and loss landscape is part of that project.

Quick Start

Local PyPI UI Proof

uv --preview-features extra-build-dependencies tool install --upgrade "agilab[ui]"
agilab

For a zero-install browser preview, open the public AGILAB Space. It opens the lightweight flight_telemetry_project path by default and exposes the weather_forecast_project notebook-migration demo with forecast analysis views. Advanced scenarios such as mission_decision_project, execution_pandas_project, execution_polars_project, and uav_relay_queue_project are collected in the Advanced Proof Pack. The default hosted flight journey covers PROJECT, ORCHESTRATE, WORKFLOW, and ANALYSIS, including bundled flight analysis views.

If startup fails, run a progressive fallback:

agilab dry-run
agilab first-proof --json --with-ui
agilab adoption-report

agilab dry-run is the fast alias for agilab first-proof --dry-run; it verifies CLI/core readiness only. agilab first-proof --json --with-ui does the local onboarding contract including manifest generation for the UI path. agilab adoption-report reads the manifest and tells you whether the first proof is a safe baseline before trying notebooks, private apps, or cluster work.

Maturity snapshot

Capability	Status
Local run	Stable
Distributed (Dask)	Stable
UI Streamlit	Beta
MLflow	Beta
Production	Experimental
RL examples	Example available

AGILAB is most mature in the bridge between notebook experimentation and reproducible AI applications: local execution, environment control, and analysis. Distributed execution is mature in the core runtime; remote cluster mounts, credentials, and hardware stacks remain environment-dependent. Production-grade MLOps features are delivered through integrations and are not yet a packaged platform claim.

Production Boundary

AGILAB should be adopted as an experimentation and validation workbench first. Use this boundary before deploying it in sensitive environments:

Boundary	Status	Required controls
Safe for production-like use	Local research sandboxes, internal demos, notebook-to-app migration, reproducible validation with non-sensitive data.	Normal repository hygiene and local proof evidence.
Conditional use only	Shared team workspaces, SSH/Dask clusters, external apps, LLM connectors, or sensitive datasets.	Per-user isolation, explicit secrets management, TLS/auth for exposed services, SBOM plus vulnerability scan evidence, and a deployment threat model.
Not safe as-is	Sole production MLOps control plane, public Streamlit exposure, regulated production model serving, enterprise governance, online monitoring, drift detection, or audit-trail ownership.	Pair AGILAB with a hardened production stack such as MLflow/Kubeflow/SageMaker/Dagster/Airflow or an internal platform.

For shared adoption, run agilab security-check --profile shared --json and use --strict or AGILAB_SECURITY_CHECK_STRICT=1 when missing controls should block the gate. The stricter profiles check app-repository allowlists, public UI bind controls, cluster-share isolation, generated-code execution boundaries, plaintext local secrets, and profile-specific SBOM / pip-audit evidence.

Security Reporting

Do not use public GitHub issues, discussions, pull requests, or comments for suspected vulnerabilities. Use the private reporting path in SECURITY.md; if GitHub Private Vulnerability Reporting is not available to you, request a private AGILAB security intake through a maintainer contact or another private channel listed by the project. The public issue tracker is only for non-sensitive bugs, support questions, and post-fix follow-up.

For adoption boundaries and the shared-use hardening checklist, see Security and adoption.

Dependency And Supply-Chain Boundaries

The public package is intentionally profile-based so operators can install only what they need:

Profile	Dependency scope	Use when
Base package	agilab plus agi-core, which wires agi-env, agi-node, and agi-cluster. This includes the core local/distributed runtime dependencies but not the built-in app or page-bundle payload.	CLI/core tooling, source-checkout validation, and worker-runtime development.
ui extra	Streamlit UI, page helpers, pandas/network graph utilities, agi-apps, and the agi-pages provider. Promoted app and page payload packages are on PyPI; unpromoted app payloads remain release artifacts until publication is enabled.	Running the local product UI with the packaged runtime and optional public demo assets.
examples extra	agi-apps app catalog/examples plus notebook/demo helper dependencies such as JupyterLab and optional plotting packages.	Running packaged notebooks, demos, learning examples, and package first-proof routes.
pages extra	agi-pages page-provider helpers without the full UI profile.	Installing or validating sidecar page-bundle discovery separately from built-in app projects.
agents extra	API client dependency boundary for packaged agent workflow helpers.	Reproducible coding-agent and assistant-backed workflows.
mlflow extra	MLflow tracking integration.	Recording runs, metrics, artifacts, or model registry handoff evidence.
ai and viz extras	API LLM clients and optional plotting packages.	Assistant-backed workflows or richer visual analysis.
local-llm / offline extras	Local/offline model stacks such as Torch, Transformers, GPT-OSS, and MLX where supported.	Isolated local-model experiments; expect a larger supply-chain and hardware footprint.
dev extra	Contributor test/build/audit tooling only.	Validating a source checkout or release candidate; avoid it for runtime installs.

Agent workflows can now produce AGILAB evidence directly. Use agilab agent-run --agent codex --label "Review current diff" --tag review --metadata branch=main -- codex review to execute a local coding-agent command and write a redacted agilab.agent_run.v1 manifest plus local stdout/stderr artifacts under ~/log/agents/. Each run also writes an append-only agilab.agent_trace.v1 stream in agent_events.ndjson, with typed events for session, command/tool, permission, compaction, rewind, and completion evidence. Command arguments are redacted by default and represented by an argv hash; pass --include-command-args only when the prompt/arguments are safe to store. Add --protocol-adapter mcp or --capability app-as-tool as metadata-only labels when experimenting with agent protocol bridges; the base package records those labels and lifecycle events without depending on the protocol stacks. Use agilab agent-run list --agent codex --json or the Python helpers agilab.agent_run.trace_agent_run() and agilab.agent_run.list_agent_runs() to create or consume run evidence from automation. Provider/model capability context can be stamped with --provider, --model, project-local .agilab/agents.json, or global ~/.agilab/agents/agents.json.

Cluster/Dask support is intentionally part of the base runtime through agi-core. AGILAB keeps local, pool, and distributed back planes behind the same reproducible execution contract; moving agi-cluster behind an extra would be only an install-footprint optimization if measured adoption data ever justifies the added conditional paths.

Release and adoption supply-chain evidence is explicit: Dependabot watches Python and GitHub Actions manifests, release workflows publish per-profile pip-audit JSON and CycloneDX SBOM artifacts, and tools/profile_supply_chain_scan.py can regenerate the same profile evidence locally. PyPI publication uses Trusted Publishing/OIDC and the release workflow runs tools/pypi_provenance_check.py after upload so missing PyPI attestations fail before GitHub release assets are published. The workflow then prunes older PyPI releases for each selected project so the current release version is the only retained public PyPI release before GitHub release assets are published. After release assets are published, the same workflow syncs the public Hugging Face Space, runs the hosted smoke test, and records the resulting Space commit in release proof.

Evidence Taxonomy

AGILAB separates public claims by evidence type:

Evidence type	What it proves	What it does not prove
Automated proof	Commands such as agilab first-proof --json, workflow parity checks, coverage, release proof, and UI robot evidence ran successfully.	Independent certification or coverage of every deployment topology.
Integration tests	A specific source path, package route, app, or workflow contract is exercised by tests.	Production SLA, security certification, or external operator acceptance.
Benchmarks	Timings for declared hardware, datasets, modes, and benchmark scripts.	General performance across arbitrary hardware, networks, or datasets.
Self-assessment	KPI scores such as production readiness and strategic potential are maintained from repository evidence.	External validation or third-party certification.
External validation	Only claimed when a named external artifact, reviewer, CI provider, or hosted demo proof is linked.	Implied endorsement beyond the linked evidence.

Proof Capsule Direction

The north-star product primitive is an AGILAB proof capsule: one portable, reviewable bundle for a run or app promotion decision. It should collect the run manifest, app/stage metadata, exported notebook handoff, MLflow handoff metadata when enabled, UI robot screenshots/traces/HAR/video when captured, artifact hashes, dependency locks, SBOM, pip-audit, wheel hashes, provenance, and a short human/machine summary.

AGILAB already ships many of those pieces separately through first-proof manifests, notebook export, release proof, supply-chain scans, robot artifacts, and adoption reports. The first public proof-pack layer now adds agilab prove, agilab verify, agilab replay, agilab export-lineage, agilab policy-check, agilab cards, and agilab metadata-store for run_manifest.json evidence. A signed .agipack archive, native lineage or observability transport, durable ML metadata, rich app-authored cards, and enterprise governance integrations remain roadmap work. See the proof capsule contract for the intended boundary.

Repository Map And Stability Boundaries

AGILAB is a monorepo, but it is not a single stability surface:

Use three planes to read the repository:

Plane	Owns	Main roots
Control plane	Product entry points, runtime APIs, environment resolution, worker packaging, and local/distributed execution.	src/agilab/core/*, src/agilab/lib/agi-gui, src/agilab/pages
Payload plane	Apps, page bundles, templates, notebooks, examples, and PyPI payload umbrellas.	src/agilab/apps/builtin, src/agilab/apps-pages, src/agilab/lib/agi-apps, src/agilab/lib/agi-pages, src/agilab/examples
Evidence plane	Proof, audits, release contracts, supply-chain evidence, UI robot outputs, docs mirror, and agent/runbook automation.	tools, .github, docs/source, .codex, .claude, badges

This is why AGILAB can look broader than a normal Python package: the runtime, its reusable app/page payloads, and the evidence proving those paths are kept in the same releaseable tree.

Area	Role	Stability contract
src/agilab/core/agi-env, agi-node, agi-cluster, agi-core	Runtime packages for environment setup, worker packaging, distributed execution, and the compact API.	Stable where documented; changes require focused regression evidence.
src/agilab/lib/agi-gui, src/agilab/pages	Streamlit UI and page helpers.	Beta product surface; useful for operators, still evolving.
src/agilab/lib/agi-apps	PyPI umbrella that carries app catalog/example assets and exact-pins the app payload packages already promoted to PyPI.	Packaged asset surface for the ui and examples extras.
src/agilab/lib/agi-pages	PyPI provider package for public analysis page discovery. Published agi-page-* payload packages are distributed independently; agi-pages supplies the discovery/provider surface.	Packaged page-provider surface for the ui and pages extras.
src/agilab/apps/builtin	Public built-in apps used for first proof, demos, workflow examples, and regression coverage.	Packaged examples, not enterprise deployment templates.
src/agilab/examples	Learning scripts, notebooks, and preview examples.	Educational material; optional helper dependencies live behind extras.
tools, .github, pycharm, .codex, .claude, dev	Contributor, release, agent, and IDE automation.	Maintainer tooling, not runtime API.
docs/source	Public documentation mirror.	Published docs source; canonical docs are synchronized before release.

This split is intentional. Treat AGILAB as an AI engineering reproducibility workbench first: stable runtime contracts, beta UI, packaged examples, and maintainer automation live together so release proof can validate the same source tree users install from.

Package Surface Contract

Local source checkouts can grow after runs because built-in apps can create .venv directories, build outputs, caches, datasets, and local logs. Those local artifacts are not the package contract. Public wheels are bounded by pyproject.toml package data rules and exclude virtual environments, tests, docs/html, build directories, generated C files, __pycache__, .pyc, and .egg-info artifacts.

Current packaging policy is conservative:

• Base agilab keeps CLI/core proof dependencies separate from UI, page bundles, examples, agents, MLflow, visualization, local-LLM, offline, and dev profiles.
• Promoted app payloads live in per-app packages such as agi-app-mission-decision, agi-app-pandas-execution, agi-app-polars-execution, agi-app-flight-telemetry, agi-app-global-dag, agi-app-weather-forecast, agi-app-pytorch-playground, agi-app-tescia-diagnostic, and agi-app-uav-relay-queue; agi-apps is the umbrella catalog/example package pulled in by the ui and examples extras.
• Public analysis page bundles use decoupled agi-page-* package names such as agi-page-feature-attribution; agi-pages is the provider package pulled in by the ui and pages extras.
• The optional PyTorch playground lives in src/agilab/apps/builtin/pytorch_playground_project. It is a reproducible app project rather than a generic app-agnostic analysis page; its loss-landscape projection is part of that project, not a separate view_loss_landscape bundle.
• Larger optional stacks must stay behind extras, and release evidence must include SBOM / pip-audit data for the actual enabled profile.
• agi-cluster remains in the base runtime by design. A separate minimal runtime profile should only be considered if measured install-footprint or adoption data justifies the new conditional package contract.

Choose Your Path

1. Preview the product quickly: AGILAB Space
2. Understand notebook-to-app migration: Notebook Migration Demo
3. Prove the full source-checkout flow: Source Checkout
4. Verify a CLI-only package install: Published Package
5. Contribute safely: Contributor onboarding
6. Audit external apps and evidence: App Repository Updates and Release Proof

For a single-page adoption checklist, use ADOPTION.md.

Source Version vs Package Version

AGILAB publishes from this repository, but each public surface has a distinct role:

Surface	Meaning	Source of truth
main branch and root pyproject.toml	Active source checkout and next release candidate. It can move after a package has already been published.	GitHub source tree
Release tag	Immutable source snapshot used for a public release. Use this for reproducible source installs.	GitHub tag and GitHub Release
PyPI package	Latest installable public wheel/sdist for agilab and the agi-* packages.	PyPI project and PyPI version badge
Release proof	Public evidence tying the release tag, PyPI package version, docs, CI, coverage, and demo proof together.	Release Proof

For development, use main. For reproducible release validation, use the release tag or the PyPI package version recorded in the release proof.

AGILAB uses date-based public versions. The dense .postN history in April-May 2026 records public-beta packaging hardening, provenance refreshes, and dependency-pin alignment across the split package set. It is kept visible for auditability, but it is not the target steady-state release rhythm; normal feature or behavior changes should advance to a deliberate new date-based release. The pypi-publish workflow now rejects committed public .postN versions unless a maintainer explicitly marks the dispatch as a critical hotfix and records the reason; release candidates or TestPyPI should be used before a final public release.

Source Checkout

Run the installable product path with the built-in flight_telemetry_project:

CHECKOUT="${AGILAB_CHECKOUT:-$HOME/agilab-src}"
git clone https://github.com/ThalesGroup/agilab.git "$CHECKOUT"
cd "$CHECKOUT"
./install.sh --install-apps
uv --preview-features extra-build-dependencies run --extra ui streamlit run src/agilab/main_page.py

On native Windows, prefer the published package route below. The source checkout installer uses POSIX shell scripts, so run that path from WSL2 until native installer parity is published.

Follow the in-app pages from PROJECT to ORCHESTRATE, WORKFLOW, and ANALYSIS. To collect the same check as JSON:

uv --preview-features extra-build-dependencies run agilab first-proof --json
uv --preview-features extra-build-dependencies run agilab adoption-report

The JSON proof writes run_manifest.json under ~/log/execute/flight_telemetry/. For installer flags, IDE run configs, and troubleshooting, use the Quick Start docs.

Published Package

For a CLI-only package smoke without Streamlit:

uv --preview-features extra-build-dependencies tool install --upgrade "agilab[examples]"
agilab first-proof --json --max-seconds 60

App Repository Updates

When APPS_REPOSITORY points at an external apps repository, rerun the installer after app changes:

./install.sh --non-interactive --apps-repository /path/to/apps-repository --install-apps all

During an update, the apps repository is treated as the source of truth. If the target app/page already exists as a real directory instead of a symlink, AGILAB backs it up as <name>.previous.<timestamp>, then links the repository copy in its place. After the update, AGILAB runs the repository version; the .previous directory is kept only for manual recovery. See Service mode and paths for the full path contract.

Evidence And Scope

The README is only the entry page. Detailed capability evidence, compatibility status, and roadmap scope live in:

• Features
• Release proof
• Compatibility matrix
• MLOps positioning
• Package publishing policy
• Future work

Evaluation Snapshot

Current public evaluation summary, refreshed from the public KPI bundle:

• 4.0 / 5 for ease of adoption, research experimentation, and engineering prototyping.
• 3.2 / 5 for production readiness.
• 4.2 / 5 for strategic potential.
• Overall public evaluation, rounded category average: 3.8 / 5.

These are public experimentation-workbench scores, not production MLOps claims. They cover project setup, environment management, execution, and result analysis. The evidence and limits are maintained in the compatibility matrix and MLOps positioning. The strategic evidence criteria are tracked in the strategic scorecard.

Read Next

• Newcomer troubleshooting
• MLOps positioning
• Strategic scorecard
• Documentation
• Contributor onboarding
• Flight-telemetry project guide
• Adoption guide
• Releases
• Changelog
• Developer runbook