Find your Alpha. Build your SUCCESSOR Ω.

MONTREAL.AI FLAGSHIP · Computer Use · Level 3 strategy development · Level 4 problem reframing

Find a valuable opportunity in software, research or mathematics. Use Computer Use to investigate real public material and produce a concrete work product. Develop competing strategies, combine what works, and challenge problem formulations that unnecessarily limit the outcome. Keep the attempts, evidence and lessons in one project.

Start the flagship → · Démarrer le parcours phare →

Planning and the catalog example are free in your browser. Computer execution uses a separately configured native model and isolated runner. What works now and how to begin.

Featured research

TRANSFORMATIONAL INTELLIGENCE ∞

Learning to Change the Game

Identify restrictive problem formulations, construct better ones and test that they still serve the intended purpose\. A framework for independently testable reformulation, not just better search\. Vincent Boucher · MONTREAL\.AI · 2026-10 · English PDF.

Companion papers · reading order

• Out\-Learn 📚 · Studies how to construct measurement protocols and learn reusable calibration, with error guarantees under declared drift and measurement\-error assumptions in exact synthetic studies\.

• Out\-Think 🧠 · Studies executable reasoning representations that preserve required answers and operations, and when to replace rather than refine inherited internal codes, in exact finite models\.

• Out\-Design 🎨 · Studies equivalent implementations that preserve required services and release usable capacity for reconfiguration and new capabilities, with checked constructions in finite executable models\.

• Out\-Strategise ♟️ · Couple observation with construction to create strategies that achieve the required outcome despite unresolved uncertainty\.

• Out\-Execute ⚡ · Preserve progress as tasks change through reusable execution capabilities and independently checked boundary contracts, demonstrated in finite synthetic studies\.

• Out\-Evolve ♾️ · A proposed architecture for building experimentation and production capabilities that make further discoveries and successor capabilities possible\.

Research proposals and bounded studies, not claims of general autonomous capability or physical deployment. Explore the papers in English or French

Current source candidate · 1.37.1. Research-feature release notes. This combined software candidate adds bounded OUT-EXECUTE execution, simpler paper publication and seven featured research manuscripts. The paper collection is not a claim that every proposed capability is implemented. Existing release records below keep their original scope. Exact-source acceptance and publication remain required.

Choose your next step

Open the guided website → · Commencer en français →

| Your goal | Start here | | --- | --- | | Investigate a valuable digital-work opportunity, software bottleneck, research question or mathematical problem | Find your Alpha. Build your SUCCESSOR Ω. · Guide | | Inspect a maintained local engineering example | Catalog reconciliation example · Scope and reproduction | | Find your Alpha and retain one project through Jobs / Fleet | Discovery → Guided project history | | Explore a free example with no account or installation | Guided first plan | | Create useful files and retain a personal SUCCESSOR Ω | Five work families | | Prepare, protect and continue a project | Nova-Seed Studio | | Commission USDC work or prepare a worker computer | AGIJobManager · Fleet setup | | Explore the AGI Jobs demos and $AGIALPHA contract ecosystem | AGI Jobs v0 · Deployment directory | | Install the native agent | Published downloads and evidence · Operator guide |

Browser preparation is free. Execution, participant review and settlement require their own configuration and approval. AGIJobManager settles in USDC; the Alpha / Jobs v0 contracts use $AGIALPHA. Source versions and published downloads can differ: use the evidence and instructions supplied with your chosen release.

New Ethereum Mainnet deployment, October 18, 2026: five contract addresses and verified graph. Publication records the deployment; operating approval remains separate. These five current records are separate from the older 29-address ecosystem snapshots below. Existing wallet profiles retain their pinned historical addresses; publication does not migrate them.

Current contribution policy: Local pre-commit checks are recommended feedback, not a prerequisite for opening a draft or pull request. Disclose failed or unrun checks and their reason; do not claim a pass. This current policy supersedes historical local-hook submission prerequisites in the preserved guide below. Installed hooks, access restrictions and required hosted merge/release checks are unchanged and not waived. See Contributing and agent guidance.

Find a valuable opportunity. Invent a better approach. Build what matters. →

Start with a real software improvement, an evidence-based research question or a rigorous mathematical

investigation. Produce a source-grounded artifact, design tests for competing approaches, and consider

whether a different formulation creates more value. Code proposals need execution; mathematical claims

need proof review. The configured browser runner retains observed work for your review.

English / Français guide and reproduction.

Current source candidate · 1.37.0. Start here ·

Release notes · Release evidence and supported scope.

Version 1.37.0 adds the capability workspace and bounded Out-Evolve constructor, independent checking,

frozen reuse and retained successor histories. These are synthetic software mechanisms, not physical deployment or general invention.

Published v1.36.0 and its source-bound artifacts remain unchanged; they do not contain these later additions.

Exact-source 1.37.0 release qualification and Jobs/Fleet compatibility remain separate gates.

Use matching published assets from Releases;

a source label or feature merge alone is not publication.

Find your Alpha. Build your SUCCESSOR Ω. · Computer Use expansion.

Define a useful outcome, prepare screenshot-and-keyboard/mouse work, and retain its results in the same project.

Level 3: discover, test, combine and improve strategies. Level 4: challenge an unnecessary restriction

and test a different problem formulation while preserving the required outcome and acceptance criteria.

Browser preparation makes no model call. Execution uses the separately configured native model and isolated

Chromium runner; imports retain historical observations and your review, with no inherited authority.

The exact release manifest and acceptance evidence identify delivery. This addition is absent from v1.34.0;

live-model task qualification and independent review remain separate from software publication.

English / Français journey and current limits.

Current Cross-Industry supplied-input journey

The original project narrative and historical quick-start below are preserved for context. For the supported local comparison, use this current guide; historical deployment commands are unqualified advanced material and are not required for the supplied-input journey.

In an installed Alpha environment, inspect the explicit synthetic example:

python -m alpha_factory_v1.demos.cross_industry_alpha_factory.opportunity_comparison --example

Create a useful SUCCESSOR Ω →

Five bounded work families · English / Français · Real artifacts you can retain · Optional native Computer Use.

Scope, setup and reproduction.

Useful work. Recovered. Reused. — Read the computer-use study.

Two repaired websites, 19/19 recovery checks and 21.4% fewer scripted setup controls.

Simulated owner; real software checks. Not a production benchmark or measured human time saving.

English / Français visual reader · Public report pack · Recorded results.

Build your own SUCCESSOR Ω with Alpha Agent + AGI Jobs →

Name your mission, run four candidate programs, compile 21 bounded job drafts, and keep the working lab offline.

English / Français · Large text · No account, wallet or installation. Source and reproduction guide · Version 1.28.0.

The example runs both projects’ code locally. Independent proof remains absent; admission stays on HOLD.

Take home your own SUCCESSOR Ω. Create your personal lab.

Give it a name, run real experiments, then save the whole working lab as one file. Reopen it offline,

try new challenges and keep your discoveries. Free, English/French, larger text, no account or installation.

Personal lab guide ·

What is new in 1.27.2.

Ideas behind your SUCCESSOR Ω

Read four selected essays on governed improvement, sustainable capabilities, invention and retained work history. English/French summaries connect each essay to a current guide. Reading notes and version context.

Alpha Agent + AGI Jobs: from a useful idea to accepted work

Alpha helps produce the work; AGI Jobs supplies rules for commissioning, reviewing and settling it. Together, they offer the building blocks for a traceable work cycle: define a worthwhile task, produce a result, check the evidence, settle according to agreed terms, and retain what proved useful. Alpha's deployed Ascension suite also provides plan records, campaign funding and spending constrained to committed jobs in its own market. A connected AGI Jobs workflow requires explicit configuration and commissioning.

Shared token · Ethereum mainnet · $AGIALPHA · 18 decimals

0xA61a3B3a130a9c20768EEBF97E21515A6046a1fA

[Explore the visual connection guide and its 29-instance historical inventory][ecosystem-page] · [Français][ecosystem-fr] · Practical workflow and operating boundaries · [AGI Jobs companion guide][jobs-ecosystem]

The public records identify 5 Alpha Ascension contracts deployed on 11 October 2026 and 24 AGI Jobs contract instances inventoried on 12 October 2026, including its coordinator and 10 fixed implementation contracts. Both records name the token above; it is a shared dependency, not another suite instance. [Alpha deployment evidence][alpha-deployment] · [AGI Jobs deployment evidence][jobs-deployment].

These dated records establish deployment, not a commissioned joint service. The Alpha deployment predates newer settlement safeguards; its MARK treasury targets its own fixed market. The Jobs record does not approve production or open live intake. Check the Alpha deployment guide and [current Jobs readiness guide][jobs-readiness] before live use. Exploring the guide and personal lab is free.

[ecosystem-page]: https://montrealai.github.io/AGI-Alpha-Agent-v0/ecosystem/ [ecosystem-fr]: https://montrealai.github.io/AGI-Alpha-Agent-v0/ecosystem/fr/ [jobs-ecosystem]: https://montrealai.github.io/AGIJobsv0/ecosystem/ [alpha-deployment]: https://github.com/MontrealAI/AGI-Alpha-Agent-v0/blob/755f678954be8e334e69e4d37f56f3fe10da2d84/deployments/ethereum-mainnet/2026-10-11/manifest.json [jobs-deployment]: https://github.com/MontrealAI/AGIJobsv0/blob/a1dea46d2f9dfa5680d11e24529f107d65cd4369/docs/deployments/ethereum-mainnet-2026-10-12.json [jobs-readiness]: https://github.com/MontrealAI/AGIJobsv0/blob/main/docs/production/readiness.md

Prefer a simple planner? Make your first plan.

Choose what matters, review a simple plan and keep a readable copy. No technical experience,

account, wallet, payment or installation is needed.

Visit the welcoming homepage ·

Français ·

Larger text ·

Simple guide · 1.26.3 release notes.

Contract maintenance · Version 1.28.1.

Follow the warning and verification guide

to distinguish source compilation, explorer verification and the immutable October 11 deployment.

Historical Ethereum mainnet deployment · 11 October 2026. Five contracts and the completed on-chain ownership handoff are recorded.

Etherscan source verification and Ledger custody are operator-reported; the public archive does not include the

Etherscan API transcript. Addresses, owner guide and read-only verification.

The deployed contracts predate the current settlement fixes; resolve that difference before admissions or funding.

SUCCESSOR Ω — 1.24.1. Build and examine a bounded mission capability, retain useful knowledge and require every successor to earn new proof and permission. Bilingual mission workspace · Quickstart and operator guide · Architecture and boundaries · Release notes and acceptance requirements. Local rehearsal is not independent qualification or production authority. Existing missions, research and flowcharts remain available.

Choose a current launch path: useful Alpha flagship, existing browser laboratory, minimal native installation, operator recovery and upgrades, or Hardhat deployment and migration guide. The repository guide explains which dependencies each path needs. Publication requires the exact-commit acceptance workflow; use the matching published release assets and manifest.

Current contract configuration: Solidity reference contracts pin the canonical $AGIALPHA address 0xa61a3b3a130a9c20768eebf97e21515a6046a1fa and 18 decimals. Environment variables do not override those Solidity constants. The historical alternate-network override instruction retained below is superseded by the current contract guide. Its local fixture is disposable and does not deploy to an external chain, migrate real tokens or establish production authority.

1.23.3 — Safer repairs and clearer browser workflows. Start with a useful result · Find your way around the repository · Release notes. Responsive Insight panels, refreshed browser dependencies, reliable MuZero controls and safer file-preserving repairs. Original content and flowcharts remain preserved.

1.23.1 — Complete demo catalog and repeatable evidence. Discover all 26 entries and retain each experiment with alpha-factory demo run --new-run. Walkthrough · Release notes. All original demos and flowcharts remain.

1.22.1 — Sovereign session cleanup. Locking clears imported drafts and review notes, retains saved mandates, and isolates delayed requests from the next session. Release notes.

1.22.0 — Sovereign Workbench. Run a bounded portfolio, schedule and evidence workflow with explicit reviews, a private signed journal and verifiable exports. Start here · Release notes. Original source, presentation and diagrams remain preserved.

1.21.0 — Finance Alpha research terminal. Audit cash, costs, next-open fills and risk decisions in a local, reproducible paper experiment. Start here · Release notes. Original diagrams and research remain preserved.

1.20.1 — Planning lab reliability. Download reproducible reports, recover cleanly from failed runs, and follow corrected platform setup. Lab guide · Release notes. Existing flowcharts and research are preserved.

1.20.0 — Evidence & Planning Lab. Cited task evidence, optional local-model advice, trained MuZero search and measured action traces. Start the lab · Release notes. Original presentation and research preserved.

1.19.0 — MuZero Planning Lab. Train a small neural world model, inspect reward-aware tree search and compare measured baselines. Start the lab · Release notes. Original flowcharts and research remain preserved.

1.18.0 — Meta-Agentic AGI v3 Curriculum Lab. Generate tasks, evolve bounded solvers, inspect lineage and independently review the frozen candidate. Open the lab · Operating guide. Original flowcharts and research are preserved.

Version 1.17.0 adds the MATS Search Lab. Explore branching workflow designs, inspect every rollout, audit the search gap, and challenge a frozen candidate against five independent model gates. Open the lab · Guide · Release notes. Original diagrams, notebooks and research remain available in the preserved archive.

Version 1.16.0 adds the Experience Learning Lab. Act, observe, learn, and challenge a frozen candidate with reproducible outcomes and independent review gates. Open the lab · Guide · Release notes.

Version 1.15.0 adds the Insight Discovery Workbench. Review cross-sector hypotheses, inspect source assumptions, allocate review time and export reproducible dossiers, verification jobs and plaintext Nova-Seed drafts. Open the workbench · Guide · Release notes. Original research, diagrams and launchers remain available.

Version 1.14.0 adds a complete Governance Workbench. Review a proposal, inspect nine conditional gates, challenge adversarial cases, and export reproducible evidence with input-bound validator jobs. The original manuscripts, diagrams, simulator and research presentation remain available. Operating guide · Release notes.

Version 1.13.1 brings Business 3's research and decision visuals together. Run all preserved experiments through the canonical alpha_factory_v1/demos catalog, check prerequisites before launch, and follow the complete demo walkthrough. Business 3 adds an editable enterprise planner with exact portfolios, stress tests, a candidate-value comparison and portable evidence. Its redesigned research collection retains the original artwork, flowcharts and interactive replay. The release packages the original sample data and tests all 15 finite commands outside the source tree. Release notes. All original content, flowcharts and media remain.

Version 1.12.1 completes release delivery and evidence integrity.

Start here: verified installation, packaged operating guides, strict approved receipts and an Ascension Protocol Desk that works on both preserved routes. Release notes. All original diagrams and media remain.

Version 1.12.0 updates the complete Alpha-Factory package entry point.

Start with Alpha-Factory or the factory guide: packaged editable missions, cross-platform setup, exact FusionPlans and signed reviewed-work handoffs. All original diagrams and media remain. See release notes.

α-AGI Ascension — executable protocol

Version 1.11.0 connects the Ascension vision to a tested Solidity lifecycle. Open the Protocol Desk: cryptosealed ERC-721 Nova-Seeds, a validator risk oracle, MARK’s funding AMM, plan-restricted Sovereign treasuries and reputation-weighted job auctions with exact $AGIALPHA settlement and 1% payout burns. The native agent runs the delivered mission; local EVM receipts expose the entire sequence and capital recovery. Read the implementation and reproduction guide and release notes. This additive suite preserves all existing contracts, research and demos. Local execution is verified. The recorded mainnet deployment uses an earlier source profile; participant commissioning and review of the newer fixes remain outstanding.

$AGIALPHA Agent — release status

Version 1.10.1 strengthens signature verification, operator reliability and recovery. Legacy plugin signatures must verify even when listed in the registry. Inference has a total response deadline, expired workers cannot invalidate replacement work, and backup creation enforces its recovery limits. Use the current operator guide, release scope and release notes. The Decision Studio calculation policy remains 1.10.0, so existing replayable decisions retain their exact results.

Version 1.10.0 turns operational examples into reviewable staffing, delivery and decision plans. Open the Decision Studio for eleven editable operating cases: capital allocation, invention portfolios, supplier resilience, delivery scheduling, demand planning, service staffing, microgrid dispatch, proof debt, Nova-Seeds pilot review and second-order pipeline trials. Import your records, calculate constrained choices, and export a decision brief, row-level plan, proof backlog and replayable dossier. All 26 original catalog entries lead to a relevant workspace and retain their research implementation links. See methods, input contracts and a five-minute walkthrough.

Version 1.8.1 aligns the project with the latest 198-page AGI ALPHA manuscript. Read the manuscript and implementation map. Enter the Compounding Lab to learn and freeze a capability, test genuinely different future tasks, inspect four comparator arms, account for learning and review costs, and export the complete Evidence Docket. Positive transfer, regime failure and archive ablation are computed locally. Independent validation and broader manuscript promotion remain explicit proof obligations. Use the field guide or alpha-agent transfer-run --output run.json.

Version 1.7.0 adds Nova-Seeds Proof Bloom: five connected, evidence-gated experiences. Enter the foundry to turn a claim into native jobs, replay returned evidence, review exact artifacts, promote a scoped capability and reuse it in the next mission. Explore Nova-Seeds, Sovereign Bloom, the Alpha Business Ω-Lattice, Invention Automation and Proof Debt → AGI Jobs. The Proof Bloom field guide explains the proof protocol, native return path and recovery.

Version 1.6.0 adds the connected Insight Atlas and a refined GitHub Pages experience. Explore the Living Treasure Map, Second-Order Agency lab and Alpha under trial: exact scenario allocations, inspectable architecture comparisons, native research exports and replay-verified recovery. The Atlas field guide explains the calculations, evidence gates and limits. Patched cryptography, complete operator dependency auditing and stricter publication/CI controls are documented in the release readiness guide. Explore the new flagship missions and implementation map: Insight, encrypted Nova-Seeds, funding curves, Sovereign schedules, Council settlement and Architect policy search. The governance observatory makes the paper’s equations interactive and documents necessary mathematical corrections. Start with the operator guide, capability matrix and validation evidence. Install the official release wheel and hash-locked runtime, then run alpha-agent init and alpha-agent serve. For the current container demo profiles, use Docker Compose 2.20 or later; the older Compose minimum in the preserved historical documentation below no longer covers those profiles.

Start in the GitHub Pages workspace for checked research, allocation, scheduling and forecasting, real local text generation, reviewed exports and verification of signed agent results. The workspace guide explains privacy, offline use and the handoff to your agent. The complete demo catalog labels every launch path, sample replay, research backend and historical integration template.

The original vision, flywheels, examples and demos below are preserved; CI badges explicitly track main. Historical AGI/ASI, economic and compliance aspirations are not release guarantees. This is the $AGIALPHA project, separate from AGI Jobs. The previous state is preserved in baseline-2026-09-24.

For repository administrators, current protection instructions use the actual check names Lint (ruff) and Smoke tests.

CI badges show live results and open their supporting checks. Health follows the latest watchdog workflow on main; Integration and Smoke read the current main commit's complete matrix results. Badge meanings and troubleshooting explain the scope of each check and where to find release evidence.

Smoke tests now run automatically across Linux, macOS and Windows on Python 3.11–3.13. Full browser builds include verified ONNX GPT-2 text generation that works offline after installation. GPT-2 is a small text-completion baseline, not an instruction-following assistant. The original PyTorch model assets remain available. Minimal browser builds retain the simulation; local chat reports missing model assets explicitly. Browser API credentials remain in memory.

See docs/DISCLAIMER_SNIPPET.md

This repository is a conceptual research prototype. References to "AGI" and "superintelligence" describe aspirational goals and do not indicate the presence of a real general intelligence. Use at your own risk. Nothing herein constitutes financial advice. MontrealAI and the maintainers accept no liability for losses incurred from using this software.

Ready to explore? Launch the α‑AGI Insight demo to see it in action.

Security Note

This project intentionally avoids reliance on Chainlink VRF or similar third-party subscription services. Randomness and related features are derived locally so the system remains self-contained and auditable.

Continuous Integration

The CI matrix is pinned to the canonical $AGIALPHA token contract (0xa61a3b3a130a9c20768eebf97e21515a6046a1fa, 18 decimals). Each workflow calls python scripts/check_agialpha_config.py to fail fast if the address, decimals, or workflow environment variables drift away from token.config.js or the Solidity constants. Run the same helper locally before dispatching CI to keep badges green and avoid PR surprises.

The canonical pull-request gate is ✅ PR CI. It runs Ruff plus the focused smoke test suite on pull requests, pushes to main, and merge queue runs.

The full 🚀 Integration CI — Insight Demo workflow is intentionally off the PR path. It runs on pushes to main, release tags (v / release-), and manual dispatch for maintainers. Push-to-main runs the full non-release validation matrix (actionlint, Ruff+Mypy matrix, pytest matrix, Insight Browser ESLint, Windows/macOS smoke, MkDocs, Docs Build, Docker build validation, and branch-protection guardrails). Tags/manual dispatch add release-only publish/deploy/signing stages.

🩺 CI Health hard-monitors the canonical required surface (✅ PR CI) and hard-monitors 🚀 Integration CI for merge-surface contexts. workflow_run watchdog executions remain rerun-only (no extra dispatch) because the upstream run already exists; scheduled/manual runs enforce both PR and merge surfaces with missing-run dispatch enabled. Self-monitoring remains opt-in (--include-self), and rerun support helps Repo-Healer progress beyond run-attempt-1 report-only triage. ADMIN_GITHUB_TOKEN enables branch-protection remediation while default tokens stay read-only for admin APIs.

Run CI locally

1. Create and activate a Python 3.11–3.13 virtual environment, then upgrade pip:

python3 -m venv .venv
   source .venv/bin/activate
   pip install -U pip

1. Install required and optional Python dependencies (set ALPHA_FACTORY_FULL=1 for heavier extras or --wheelhouse when offline):

python check_env.py --auto-install

1. Run formatting and lint hooks:

pre-commit run --all-files

1. Execute the tests:

pytest

1. Trigger workflows from GitHub Actions when needed:

Set branch protection required checks to:

• ✅ PR CI / Lint (ruff)
• ✅ PR CI / Smoke tests

python scripts/verify_branch_protection.py --apply --branch main

Track workflow health with:

python scripts/check_ci_status.py --wait-minutes 5 --stale-minutes 90 --pending-grace-minutes 45
add --dispatch-failed only for explicit remediation runs

See docs/CI_ENFORCEMENT.md for the up-to-date enforcement checklist.

Quick Demo

Non-technical users can try the project with zero setup. Simply visit in your browser. The README explains how this demo is built and deployed.

See Quick Deployment for build and deployment details. The 📚 Docs workflow is dispatched manually by the repository owner to publish the updated site to GitHub Pages.

Full documentation: https://montrealai.github.io/AGI-Alpha-Agent-v0/ (use the Docs link in the navigation bar)

The GitHub Pages site hosts the interactive demo under the alpha_agi_insight_v1/ directory. Click Docs in the navigation bar for the full manual.

View the interactive demo here:

Browse the visual demo gallery:

Explore all demos: – run ./scripts/open_subdir_gallery.py (or set AF_GALLERY_URL to your own mirror) for a local or online launch. Alternatively execute make subdir-gallery-open to build the gallery if needed and open it automatically. All browser demos include a mode toggle. Choose Offline to run a Pyodide simulation directly in your browser or switch to OpenAI API when you provide a key. The key is stored only in memory.

Smoke Test Workflow

Trigger the 🔥 Smoke Test job from the GitHub Actions tab to run linting, unit tests and a short 2‑year simulation in offline mode. The workflow verifies the ledger output and catches import errors or circular dependencies early.

Important: Run npm run fetch-assets before npm install or executing ./setup.sh to download the browser demo assets. Run this command in a fresh checkout—or delete existing wasm*/ files first—so placeholder files are replaced. After the download, verify checksums with python scripts/fetch_assets.py --verify-only. The helper retrieves the official Pyodide runtime from the jsDelivr CDN and the GPT‑2 small checkpoint directly from the Hugging Face CDN. The legacy wasm-gpt2.tar bundle is no longer used. Override HF_GPT2_BASE_URL to change the mirror, for example:

export HF_GPT2_BASE_URL="https://huggingface.co/openai-community/gpt2/resolve/main"

If npm run fetch-assets fails with a 401 or 404 error, download the GPT‑2 files manually:

python scripts/download_hf_gpt2.py models/gpt2

curl -O https://huggingface.co/openai-community/gpt2/resolve/main/pytorch_model.bin
curl -O https://huggingface.co/openai-community/gpt2/resolve/main/vocab.json
curl -O https://huggingface.co/openai-community/gpt2/resolve/main/merges.txt
curl -O https://huggingface.co/openai-community/gpt2/resolve/main/config.json

The repository owner triggers the Docs workflow from the GitHub Actions tab. Simply click Run workflow to start the deployment. The job runs scripts/edge_human_knowledge_pages_sprint.sh to rebuild the Insight demo and MkDocs site, publishing the result to GitHub Pages. Once it finishes the live demo is available at .

Publish Demo Gallery

Ensure Python 3.11–3.13 (<3.14) and Node 22.17.1 are installed, then deploy the gallery and docs with a single command:

make gallery-deploy

See docs/GITHUB_PAGES_DEMO_TASKS.md for a detailed walkthrough. Once the build finishes, open the gallery locally with:

make gallery-open

Run make gallery-build to regenerate the site without deploying and open it in one step.

Open an individual demo directly:

make demo-open DEMO=alpha_agi_business_v1

Edge-of-Human-Knowledge Sprint

Run the wrapper to build and deploy the full GitHub Pages site with environment checks and offline validation. Use the shell or Python version:

./scripts/edge_human_knowledge_pages_sprint.sh
python scripts/edge_human_knowledge_pages_sprint.py

Ensure Python 3.11–3.13 (<3.14), Node 22.17.1 and mkdocs are installed. The script mirrors the Docs workflow used for manual deployment.

Browser Size Workflow

The 📦 Browser Size job ensures the Insight archive stays under 500 MiB (with a 750 MiB hard cap). Open Actions → 📦 Browser Size and click Run workflow to start the check. Repository owners can leave run_token blank. Others must provide the run_token that matches the DISPATCH_TOKEN secret. The workflow caches pip and npm dependencies using actions/setup-python and actions/setup-node. Node uses cache-dependency-path to reference alpha_factory_v1/demos/alpha_agi_insight_v1/insight_browser_v1/package-lock.json, alpha_factory_v1/core/interface/web_client/package-lock.json, alpha_factory_v1/core/interface/web_client/staking/package-lock.json and alpha_factory_v1/demos/alpha_agi_insight_v1/src/interface/web_client/package-lock.json so repeat runs skip redundant downloads and avoid the “Dependencies lock file is not found” warning. ci.yml stores these paths in the NODE_LOCKFILES environment variable so every setup-node step uses the same list. It applies the same paths for the tests, docs-build and Docker jobs. It preinstalls numpy, pandas, pytest and PyYAML so the environment check passes without network hiccups. During the run it also updates the browserslist cache inside alpha_factory_v1/demos/alpha_agi_insight_v1/insight_browser_v1 and alpha_factory_v1/core/interface/web_client using update-browserslist-db to ensure the generated assets target the latest browsers. When running offline or if the update fails, set BROWSERSLIST_IGNORE_OLD_DATA=true to continue without refreshing the cache. Update the version in .github/workflows/ci.yml and rerun npx update-browserslist-db --update-db --yes whenever dependencies change.

CI Workflow

The 🚀 Integration CI workflow verifies the Insight demo with linting, type checks, unit tests and a Docker build. Open Actions → 🚀 Integration CI — Insight Demo, select the branch or tag to test in the drop‑down and click Run workflow to dispatch the pipeline. This workflow also runs automatically on pushes to main and release tags (v, release-), while staying off pull requests. Push-to-main runs now execute the full non-release validation matrix, while release tags/manual dispatch append publish/deploy tasks. Each job begins by verifying the actor matches the repository owner only for manual dispatches; push and tag runs execute automatically for normal integration validation. Because the first job checks ${{ github.actor }} against ${{ github.repository_owner }}, you must own the repository to run the workflow from manual dispatch. Jobs following the main test stage include if: always() so the Windows and macOS smoke tests, documentation build and Docker jobs execute even when the lint or unit tests fail. This behavior helps confirm that cross‑platform builds and docs generation succeed while debugging failures. Dependency hashes are fully locked, including setuptools, so pip install -r requirements.lock succeeds across Python versions. The Windows smoke job now builds the Insight browser before running tests, ensuring the service worker is present for the cache version check. Browser assets are cached across jobs using a deterministic hash derived from the expected checksums, so later stages reuse the same files. The docs and Docker jobs automatically fetch any missing assets and open a pull request when checksums drift. See CI_WORKFLOW.md for a detailed job overview. All Node.js steps pin the same lockfiles via cache-dependency-path so actions/setup-node caches npm packages correctly and avoids "Dependencies lock file is not found" warnings. The paths are:

alpha_factory_v1/demos/alpha_agi_insight_v1/insight_browser_v1/package-lock.json
alpha_factory_v1/core/interface/web_client/package-lock.json
alpha_factory_v1/core/interface/web_client/staking/package-lock.json
alpha_factory_v1/demos/alpha_agi_insight_v1/src/interface/web_client/package-lock.json

Build & Test Workflow

The 🐳 Build & Test job runs linting, tests and container builds. Open Actions → 🐳 Build & Test and click Run workflow to start the pipeline. Only the repository owner can run this workflow. Each job verifies the actor first and exits immediately for non‑owners, keeping the rest of the jobs from being skipped. See ADMIN_ACTIONS.md for details on the manual workflow restrictions and protected environments.

Docker image tags must use all lowercase characters. The workflow's "Prepare lowercase image name" step sets REPO_OWNER_LC to the lowercased repository owner so tags like ghcr.io/montrealai are valid.

CI Quick Start

To replicate the CI pipeline locally:

python scripts/check_python_deps.py
python check_env.py --auto-install
pytest --cov --cov-report=xml
pre-commit run --all-files

Launch the integration workflow manually via Actions → 🚀 Integration CI — Insight Demo and click Run workflow as described in AGENTS.md. The workflow performs linting, type checks, the full unit test matrix on Python 3.11 and 3.12, Windows and macOS smoke tests, documentation builds, Docker builds and an optional deploy step for tagged releases.

Verify Docker image signature

The release workflow signs the container using cosign. Validate the signature before deploying a new version:

cosign verify ghcr.io/montrealai/agi-insight-demo:latest

Quickstart

./quickstart.sh
or using Docker
docker compose up --build
or one-click image
./run_quickstart.sh

Run npm run fetch-assets before npm install or executing ./setup.sh to download the Insight demo assets. Delete any old wasm*/ directories or start from a clean checkout so placeholders are replaced. After fetching, verify the files with python scripts/fetch_assets.py --verify-only. The helper retrieves the official Pyodide runtime from the jsDelivr CDN and the GPT‑2 small checkpoint from Hugging Face. Override HF_GPT2_BASE_URL or PYODIDE_BASE_URL to use alternate mirrors. See insight_browser_v1/index.md for a detailed guide. You can also run python scripts/download_gpt2_small.py to retrieve the model directly:

python scripts/download_gpt2_small.py models/
downloads https://huggingface.co/openai-community/gpt2/resolve/main/pytorch_model.bin ...

As a last resort use python scripts/download_openai_gpt2.py 124M.

Requires Python 3.11–3.13 (<3.14) and Docker Compose ≥2.5.

Alternatively, run the pre-built image directly: ```bash docker run --pull=always -p 8000:8000 ghcr.io/montreal

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