https://github.com/thalesgroup/agilab

Open-source platform for reproducible AI/ML workflows, from local experimentation to distributed workers and long-lived services.
https://github.com/thalesgroup/agilab
agentic-ai ai ai-agents ai-engineering cython dask data-science distributed-computing experiment-tracking free-threaded-python jupyter-notebook machine-learning mlflow mlops python reproducibility reproducible-research rust streamlit workflow-orchestration
Last synced: 4 days ago
JSON representation
Open-source platform for reproducible AI/ML workflows, from local experimentation to distributed workers and long-lived services.
Host: GitHub
URL: https://github.com/thalesgroup/agilab
Owner: ThalesGroup
License: other
Created: 2025-03-14T14:29:22.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2026-05-29T17:41:23.000Z (5 days ago)
Last Synced: 2026-05-29T18:15:33.639Z (5 days ago)
Topics: agentic-ai, ai, ai-agents, ai-engineering, cython, dask, data-science, distributed-computing, experiment-tracking, free-threaded-python, jupyter-notebook, machine-learning, mlflow, mlops, python, reproducibility, reproducible-research, rust, streamlit, workflow-orchestration
Language: Python
Homepage: https://thalesgroup.github.io/agilab
Size: 26.3 MB
Stars: 15
Watchers: 2
Forks: 1
Open Issues: 1
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
- Roadmap: docs/ROADMAP.md
- Notice: NOTICE
- Agents: AGENTS.md
Awesome Lists containing this project

README

          


  

  

  

  

  

  

  

  

  





  

  

  

  

  

  

  

  

  

  

  

  

  

  





  

  

  

  

  

  

  



# 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, production-grade core

technology, without depending on the AGILAB UI or distributed worker layer.

The stable core runtime remains the smallest supported handoff surface.

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.

Apps can also declare multiple UI surfaces, so an app can keep the same runtime

and evidence contract while exposing Streamlit, hosted Hugging Face, or future

NiceGUI/Gradio/FastAPI adapters.

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.

## Demo Routes

Start with the route that matches the proof you want to show:

| Goal | Demo route | What it proves |

|---|---|---|

| Try AGILAB in the browser | [AGILAB Space](https://huggingface.co/spaces/jpmorard/agilab) | Hosted `PROJECT` -> `ORCHESTRATE` -> `WORKFLOW` -> `ANALYSIS` path. |

| Choose by objective | [Demo chooser](https://thalesgroup.github.io/agilab/demos.html) | Public router for notebook, UI, proof-pack, and performance demos. |

| Migrate a notebook into an app | [Weather notebook migration](https://thalesgroup.github.io/agilab/notebook-migration-skforecast-meteo.html) | Notebook stages, `lab_stages.toml`, artifacts, and reusable analysis views. |

| Keep Excel as the front end | [Excel workbook proof](https://thalesgroup.github.io/agilab/excel-users.html) | Workbook inputs plus CSV and JSON evidence without an Office add-in. |

| Keep a notebook dashboard | [Voila notebook proof](https://thalesgroup.github.io/agilab/voila-users.html) | Hide-code notebook dashboard path plus widget-to-args and app-view evidence. |

| Publish run evidence | [Quarto bridge](https://thalesgroup.github.io/agilab/quarto-users.html) | `agilab export quarto` turns a run manifest into a publishable `.qmd` report. |

| Prove database access locally | [SQLite connector proof](https://thalesgroup.github.io/agilab/data-connectors.html#sqlite-database-proof) | Local schema, parameterized SQL query, result CSV, and JSON evidence hashes. |

| Gate candidate data | [Data Quality Gate](src/agilab/apps/builtin/data_quality_gate_project) | Contract, drift, leakage, and promotion decision evidence before training. |

| Show performance engineering | [Cython worker speedup demo](https://thalesgroup.github.io/agilab/execution-playground.html) | Worker execution model plus checksum-matched typed-kernel speedup evidence. |

| Show a native extension boundary | [Rust/PyO3 native worker preview](https://thalesgroup.github.io/agilab/execution-playground.html#optional-rust-pyo3-worker-preview) | Generated PyO3/maturin worker skeleton with explicit evidence handoff. |

| Explore an opt-in app | [PyTorch Playground](https://thalesgroup.github.io/agilab/pytorch-playground.html) | Reproducible classifier playground with live play/pause training, replayable teaching route, evidence ZIPs, generated PyTorch/Lightning code, multi-UI surface declarations, and loss-landscape analysis. |

| Go deeper after first proof | [Advanced Proof Pack](https://thalesgroup.github.io/agilab/advanced-proof-pack.html) | Mission decision, execution playground, UAV queue, service, MLflow, and release-proof routes. |

Use the [local quick start](#quick-start) when you want to run the product

locally, the [demo capture guide](https://thalesgroup.github.io/agilab/demo_capture_script.html)

when preparing screenshots or video, and the [50-star promotion kit](PROMOTE.md)

when sharing the project publicly.

If AGILAB helps you make AI/ML experiments reproducible, please star the

repository so other engineers can find it.

For spreadsheet-first users, the packaged `excel_workbook_proof` preview keeps

Excel as the familiar interface and writes a proof workbook, Power Query-friendly

CSV files, and JSON hash evidence without requiring an Office add-in.

For notebook-dashboard users, the packaged `voila_notebook_proof` preview keeps

the notebook dashboard flow and writes a hide-code manifest, widget-to-args

hints, an app-view plan, and JSON evidence without requiring Voila in the base

install.

## Featured Opt-In App

- [Data Quality Gate](src/agilab/apps/builtin/data_quality_gate_project)

  is the deterministic data-readiness gate: baseline/candidate datasets,

  contract checks, drift metrics, leakage detection, and promotion evidence

  without external services.

- [PyTorch Playground](src/agilab/apps/builtin/pytorch_playground_project)

  is the opt-in classifier playground app for

  synthetic datasets, real play/pause training, hidden-layer activation maps,

  network diagnostics, a replayable teaching route, and the **Loss landscape**

  view. It keeps the instant visual learning loop of classic neural playgrounds,

  then adds PyTorch-native evidence, replay tokens, and reusable PyTorch or

  Lightning code. It demonstrates the reusable app-surface contract: inspect backends with

  `agilab app surface pytorch_playground_project --list`, open the local UI with

  `agilab app surface pytorch_playground_project --ui streamlit`, or open the

  hosted backend with `agilab app surface pytorch_playground_project --ui hf`.

  The shortcut `agilab pytorch-playground` remains for convenience. It is a

  reproducible app project, not a generic app-agnostic analysis page, and loss

  landscape is part of that project.

## Featured Performance Demo

- [Execution Pandas Project](src/agilab/apps/builtin/execution_pandas_project)

  is the Cython worker speedup demo. It keeps Pandas I/O and reducer evidence

  in Python, then isolates the hot scoring loop as a typed contiguous

  `float64` kernel so AGILAB can compare Python and Cython execution honestly.

  The versioned local kernel proof reports `0.620s` Python vs `0.002s` Cython

  on 100,000 rows x 32 passes, a checksum-matched `306x` speedup. That is a

  focused hot-loop result, not an end-to-end runtime promise.

- [Flight Telemetry Project](src/agilab/apps/builtin/flight_telemetry_project)

  is the real-world worker-only Cython example: the Polars ingestion and

  artifact contract stay in Python, while the per-row haversine distance kernel

  reports `speed_kernel_runtime`, `speed_dtype_contract`, and checksum evidence

  in the reducer summary.

## [Quick Start](https://thalesgroup.github.io/agilab/quick-start.html)



  

  



### Local PyPI Proof

```bash

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

agilab first-proof --json

agilab adoption-report

```

Use the UI profile only when you also want the local Streamlit pages:

```bash

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

agilab

```

For a zero-install browser preview, open the public

[AGILAB Space](https://huggingface.co/spaces/jpmorard/agilab). 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`,

the `execution_pandas_project` Cython worker speedup demo,

`execution_polars_project`, and `uav_relay_queue_project` are collected in the

[Advanced Proof Pack](https://thalesgroup.github.io/agilab/advanced-proof-pack.html).

For the full project/package/status matrix, see the

[Public App Catalog](https://thalesgroup.github.io/agilab/public-app-catalog.html).

The default hosted flight journey covers `PROJECT`, `ORCHESTRATE`, `WORKFLOW`,

and `ANALYSIS`, including bundled flight analysis views.

If startup fails, run a progressive fallback:

```bash

agilab dry-run

agilab first-proof --json

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` does the local onboarding contract and writes the

manifest without requiring Streamlit. Add `--with-ui` only when you intentionally

want the proof to boot the packaged Streamlit pages too.

`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 |

|---|---|---|

| Go for controlled local use | Local research sandboxes, internal demos, notebook-to-app migration, reproducible validation with non-sensitive data. | Normal repository hygiene and local proof evidence. |

| Go for hardened shared use | Shared team workspaces, SSH/Dask clusters, reviewed external apps, LLM connectors, local/offline LLMs, or sensitive internal datasets when the hardening gate passes. | Per-user isolation, strict `agilab security-check` gate, explicit secrets management, TLS/auth for exposed services, pinned/allowlisted external apps, SBOM plus vulnerability scan evidence for deployed install profiles, bounded resources, 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.

Treat a clean strict report plus profile-specific SBOM / `pip-audit` evidence

as the documented go gate for hardened shared/team use. To persist the combined

decision, write the gate artifact:

```bash

uv --preview-features extra-build-dependencies run python tools/shared_go_gate.py \

  --security-check-json test-results/security-check.json \

  --supply-chain-dir test-results/supply-chain \

  --output test-results/shared_go_gate.json

```

## Security Reporting

Do not use public GitHub issues, discussions, pull requests, or comments for

suspected vulnerabilities. Use the private reporting path in

[SECURITY.md](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](https://thalesgroup.github.io/agilab/security-adoption.html).

## 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 | Lightweight `agilab` command shell plus Python 3.13 stdlib shims. It does not install the core runtime, UI, apps, pages, notebooks, or model stacks by default. | Version/help checks, package/app management commands, and metadata/reporting helpers that do not execute AGILAB runtime code. |

| `core` extra | `agi-core`, which wires `agi-env`, `agi-node`, and `agi-cluster` for compact local/distributed runtime smoke checks. | CLI proof, source-checkout validation, notebook/API runtime, and worker-runtime development without the UI or packaged examples. |

| `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. |

| `notebook` extra | Notebook execution helpers such as `nbclient`, `nbformat`, and `ipykernel`. | Running `agilab run notebook` to execute a local notebook and write AGILAB evidence. |

| `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. |

| `proof` extra | Optional `cryptography` dependency for detached Ed25519 proof-capsule signatures. | Signing `.agipack` archives and verifying them against local trust policies. |

| `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 --permission-level standard --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.

Output artifact files redact obvious secret assignments, supported secret refs,

and common standalone API-token patterns by default; pass `--include-raw-output`

only for safe local diagnostics. Destructive executable names and obvious

destructive shell, Python, Git, Docker, Kubernetes, or package-manager command

content are operator-gated, but this permission layer is an evidence guard, not

a process sandbox. 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. By default, the workflow omits

selected PyPI projects whose current wheel/sdist artifacts already exist, so

unchanged split packages do not enter upload, provenance, retention, or release

asset jobs. For packages that still need publication, the workflow then attempts

to prune older PyPI releases; missing current versions or remaining stale

releases are hard failures. If PyPI asks for unrecognized login confirmation,

the workflow waits on the temporary

`PYPI_CONFIRM_LOGIN_URL` Actions variable instead of silently continuing. Release

assets and Hugging Face sync only proceed after PyPI retention verifies that each

selected project exposes only the current release. 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 export-traces`, `agilab policy-check`, `agilab cards`, and

`agilab metadata-store` for `run_manifest.json` evidence, plus a

hash-verifiable `.agipack` archive for portable handoff with

`agilab prove --export proof.agipack`. The `proof` extra adds detached Ed25519

signing with `agilab sign proof.agipack --key signer.pem --signature

proof.agipack.sig.json` and trust-policy verification with `agilab verify

proof.agipack --signature proof.agipack.sig.json --trust-policy policy.toml`.

For fail-closed evidence, maintainers can run `./dev robust`; it executes a P0

robustness matrix of synthetic bad states covering cluster shares, public UI

binds, service health gates, evidence manifests, notebook import, app settings,

and Streamlit route contracts. A scenario passes only when the bad state is

rejected with a clear remediation and replay command.

For adoption-surface evidence, maintainers can run `./dev app-contracts`; it

checks that built-in apps, worker manifests, reducer expectations, promoted PyPI

app packages, the app catalog, and public app docs stay aligned. The same guard

runs in release validation, CI, and targeted pre-push checks when those inputs

change.

For style tooling, maintainers should run `./dev lint`; it executes Ruff through

the repo `dev` extra so a fresh checkout provisions the tool instead of relying

on an already-synced local environment.

External Sigstore/SLSA attestation binding, native lineage or observability

transport, durable ML metadata, rich app-authored cards, and enterprise

governance integrations remain roadmap work. See the

[proof capsule](https://thalesgroup.github.io/agilab/proof-capsule.html)

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` | Main web UI, Streamlit page helpers, and app-surface launch adapters. | Beta product surface; useful for operators, still evolving. App runtime contracts should not depend on one UI backend. |

| `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 the command shell separate from core runtime, UI, page

  bundles, examples, agents, MLflow, visualization, local-LLM, offline, and dev

  profiles; use `agilab[core]` when CLI proof/runtime smoke checks are needed.

- 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-multi-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`](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](https://huggingface.co/spaces/jpmorard/agilab)

2. Understand notebook-to-app migration: [Notebook Migration Demo](https://thalesgroup.github.io/agilab/notebook-migration-skforecast-meteo.html)

3. Prove the full source-checkout flow: [Source Checkout](#source-checkout)

4. Verify a CLI-only package install: [Published Package](#published-package)

5. Contribute safely: [Contributor onboarding](CONTRIBUTING.md)

6. Audit external apps and evidence: [App Repository Updates](#app-repository-updates) and [Release Proof](https://thalesgroup.github.io/agilab/release-proof.html)

For a single-page adoption checklist, use [ADOPTION.md](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](https://thalesgroup.github.io/agilab/release-proof.html) |

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`:

```bash

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:

```bash

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:

```bash

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:

```bash

./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 `.previous.`, 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](https://thalesgroup.github.io/agilab/service_mode_and_paths.html)

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](https://thalesgroup.github.io/agilab/features.html)

- [Capability map](https://thalesgroup.github.io/agilab/capability-map.html)

- [Release proof](https://thalesgroup.github.io/agilab/release-proof.html)

- [Compatibility matrix](https://thalesgroup.github.io/agilab/compatibility-matrix.html)

- [MLOps positioning](https://thalesgroup.github.io/agilab/agilab-mlops-positioning.html)

- [Maintenance playbook](https://thalesgroup.github.io/agilab/maintenance-playbook.html)

- [Extension contracts](https://thalesgroup.github.io/agilab/extension-contracts.html)

- [Package publishing policy](https://thalesgroup.github.io/agilab/package-publishing-policy.html)

- [Future work](https://thalesgroup.github.io/agilab/roadmap/agilab-future-work.html)

- [Audience bridges](https://thalesgroup.github.io/agilab/roadmap/audience-bridges.html)

## 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](https://thalesgroup.github.io/agilab/compatibility-matrix.html)

and [MLOps positioning](https://thalesgroup.github.io/agilab/agilab-mlops-positioning.html).

The strategic evidence criteria and strategic score movement rule are tracked in the

[strategic scorecard](https://thalesgroup.github.io/agilab/strategic-potential.html).

## Read Next

- [Newcomer troubleshooting](https://thalesgroup.github.io/agilab/newcomer-troubleshooting.html)

- [MLOps positioning](https://thalesgroup.github.io/agilab/agilab-mlops-positioning.html)

- [Strategic scorecard](https://thalesgroup.github.io/agilab/strategic-potential.html)

- [Documentation](https://thalesgroup.github.io/agilab)

- [Contributor onboarding](CONTRIBUTING.md)

- [Flight-telemetry project guide](https://thalesgroup.github.io/agilab/flight-telemetry-project.html)

- [Adoption guide](ADOPTION.md)

- [Releases](https://github.com/ThalesGroup/agilab/releases)

- [Changelog](CHANGELOG.md)

- [Developer runbook](AGENTS.md)
ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Awesome

https://github.com/thalesgroup/agilab

Awesome Lists containing this project

README
