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.

Quick Demo

Non-technical users can try the project with zero setup. Simply visit https://montrealai.github.io/AGI-Alpha-Agent-v0/alpha_agi_insight_v1/ 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: https://montrealai.github.io/AGI-Alpha-Agent-v0/alpha_agi_insight_v1/

Browse the visual demo gallery: https://montrealai.github.io/AGI-Alpha-Agent-v0/

Explore all demos: https://montrealai.github.io/AGI-Alpha-Agent-v0/alpha_factory_v1/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

Each file can also be fetched individually using these official links:

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 model weights have SHA‑256 7c5d3f4b8b76583b422fcb9189ad6c89d5d97a094541ce8932dce3ecabde1421 for verification. See insight_browser_v1/index.md for additional details.

Manual Deployment

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 https://montrealai.github.io/AGI-Alpha-Agent-v0/alpha_agi_insight_v1/.

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

make gallery-deploy wraps scripts/deploy_gallery_pages.sh, which calls scripts/generate_gallery_html.py to refresh docs/index.html and update the docs/gallery.html redirect.

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 🚀 CI job verifies the Insight demo with linting, type checks, unit tests and a Docker build. Open Actions → 🚀 CI — Insight Demo, select the branch or tag to test in the drop‑down and click Run workflow to dispatch the pipeline. This workflow has no automatic triggers; only the repository owner can launch it manually from the GitHub UI. Each job begins by verifying the actor matches the repository owner, so non‑owners exit immediately before running the heavy steps. When the owner launches the workflow every job runs. Because the first job checks ${{ github.actor }} against ${{ github.repository_owner }}, you must own the repository to run the workflow successfully. 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

To publish a release, create a Git tag and run the same workflow on that tag. The Docker job pushes the agi-insight-demo image to GitHub Container Registry while the deploy stage attaches the built web client archive to a GitHub release. If any later step fails the workflow automatically restores the previous latest tag to avoid shipping a broken image.

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 CI workflow manually via Actions → 🚀 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, 3.12 and 3.13, 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

Successful verification proves the image came from this repository's CI.

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:

docker run --pull=always -p 8000:8000 ghcr.io/montrealai/alpha-factory:latest

The workflow publishes a separate image for each Python version with tags py311, py312 and py313. Only the Python 3.13 build also updates the latest tag so ghcr.io/montrealai/alpha-factory:latest always refers to the most recent Python 3.13 image.

Note The Dockerfiles in this repository pin the base image to Python 3.13. Keep this in sync with the highest Python version listed in the CI matrix when updating workflows.

Replace latest with a commit SHA to run that exact build:

docker run --pull=always -p 8000:8000 ghcr.io/montrealai/alpha-factory:<commit>

Set OPENAI_API_KEY and other required secrets in your environment or .env before launching the container. The orchestrator prints the project disclaimer when it starts.

Supported OS: Ubuntu 22.04+, Debian 12+, macOS 12+ and Windows 11 via WSL 2 (recommended for Windows users). Native Windows paths frequently break volume mounts. Clone this repository inside the WSL file system to avoid these issues.

wsl --install
wsl --set-default-version 2
wsl --update
# enable "Use the WSL 2 based engine" in Docker Desktop

Clone the repository inside your WSL home directory to avoid path translation errors.

See docs/INTRO_BASICS.md for the bare essentials or docs/QUICKSTART_BASICS.md for a minimal walkthrough.

Watch the run here: Quickstart video · Asciinema link

See the documentation for detailed steps and an overview of the project. For a concise high-level picture of how the main pieces fit together, see docs/ARCHITECTURE.md.

META-AGENTIC α‑AGI 👁️✨

v0.1.0‑alpha

Official and pioneering definition – Meta-Agentic (adj.): Describes an agent whose primary role is to create, select, evaluate, or re‑configure other agents and the rules governing their interactions, thereby exercising second‑order agency over a population of first‑order agents. The term was pioneered by Vincent Boucher, President of MONTREAL.AI.

flowchart TD
    Insight["🎖️ α‑AGI Insight 👁️✨"]
    Seeds["🌱💫 α-AGI Nova-Seeds 🔐"]
    Mark["α-AGI MARK 🔮🌌✨"]
    Sovereign["🎖️ α‑AGI Sovereign 👑✨"]
    Biz["🌸 α‑AGI Business 👁️✨"]
    Market["🪐 Marketplace 👁️✨"]
    Jobs["📜 α‑AGI Jobs 👁️✨"]
    Agents["👾👾👾🌌👾👾👾 α‑AGI Agents 👁️✨"]
    Reservoir["💎 α‑AGI Value Reservoir"]
    Architect["🎖️ α‑AGI Architect 🔱✨"]
    Council["🔐 α‑AGI Council 👁️✨"]
    Nodes["🖥️ α‑AGI Nodes 👁️✨"]

    Insight --> Seeds --> Mark --> Sovereign
    Sovereign --> Biz --> Market
    Market -->|spawn| Jobs --> Agents
    Agents -- success --> Reservoir
    Jobs -- ΔΣUSD --> Reservoir
    Reservoir -. reinvest .-> Seeds
    Reservoir -. fund .-> Market
    Agents <---> Nodes
    Architect <--> Sovereign
    Architect <--> Insight
    Council --> Sovereign

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---

Humanity’s Structured Rise to Economic Supremacy via Strategic AGI Mastery

🎖️ α‑AGI Insight 👁️✨ — Beyond Human Foresight

Where human foresight reaches its limits, α‑AGI Insight sees beyond. Humanity stands at the precipice of history’s most profound economic transformation. α‑AGI Insight identifies with pinpoint accuracy those sectors poised for imminent disruption by Artificial General Intelligence (AGI). With authoritative and rigorously validated projections estimating economic opportunities surpassing $15 Quadrillion (15 000 trillion USD), today’s strategic anticipation unlocks extraordinary economic advantages tomorrow.

• Precision Forecasting — Identify and proactively engage critical sectors before AGI disruption.
• First‑Mover Advantage — Maximize returns through strategic foresight and superior positioning. A static demo is available via GitHub Pages. See Quick Deployment for guidance on building the docs and publishing your own copy.

🎖️ α‑AGI Sovereign 👁️✨ — Autonomous Economic Transformation

Meta‑Agentic mastery at global scale. α‑AGI Sovereign represents a revolutionary class of autonomous, blockchain‑based enterprises deploying advanced Meta‑Agentic frameworks. Through dynamically evolving swarms of intelligent agents, these enterprises systematically identify and transform global inefficiencies into measurable economic value (“$AGIALPHA”), fundamentally reshaping market dynamics and strategically realigning global economic structures.

• α‑AGI Marketplace 👁️✨ — Decentralized global platform matching strategic AGI tasks with optimal execution.
  • α‑AGI Jobs 👁️✨ — Autonomous missions precisely targeting identified inefficiencies.
  • α‑AGI Agents 👁️✨ — Adaptive, self‑optimizing intelligent agents executing α‑Jobs, yielding immediate economic returns.

Strategic Edge:

• Decentralized autonomy ensures superior agility and resilience.
• Strategically validated methodologies guarantee consistent economic leadership.

Quick Start

Local: ./quickstart.sh   |   Docker: docker compose up --build

An interactive Colab notebook demonstrates the same zero‑data Insight search loop. Open colab_alpha_agi_insight_v1.ipynb in Google Colab to try it online.

Clone the repository at the v0.1.0-alpha tag and run the helper script to start the Insight demo locally:

git clone --branch v0.1.0-alpha https://github.com/MontrealAI/AGI-Alpha-Agent-v0.git
cd AGI-Alpha-Agent-v0
python -c "import alpha_factory_v1; print(alpha_factory_v1.__version__)"  # prints 0.1.0-alpha
python check_env.py --auto-install  # may run for several minutes
# NumPy and pandas are required for realistic results; omit or add
# `--allow-basic-fallback` to bypass this check.
# Abort with Ctrl+C and rerun with '--timeout 300' to fail fast
./quickstart.sh
Run `pre-commit run --all-files` after the dependencies finish installing.

Offline example using a local wheelhouse:

WHEELHOUSE=$(pwd)/wheels AUTO_INSTALL_MISSING=1 ./quickstart.sh

If the default mirrors are blocked, set PYODIDE_BASE_URL or HF_GPT2_BASE_URL before running npm run fetch-assets:

export PYODIDE_BASE_URL="https://cdn.jsdelivr.net/pyodide/v0.28.0/full"
export HF_GPT2_BASE_URL="https://huggingface.co/openai-community/gpt2/resolve/main"
npm run fetch-assets

Or launch the full stack with Docker:

docker compose up --build

Minimal Install

The default requirements.txt pulls in a lean set of packages for the offline demos and tests:

pip install -r requirements.txt

Full Feature Install

Install the heavier extras for finance, graph back‑ends and large language models:

pip install -r alpha_factory_v1/requirements.txt
# or set ALPHA_FACTORY_FULL=1 when running `check_env.py --auto-install`

Detailed step‑by‑step instructions, including Colab usage, are available in the documentation.

For advanced options, see the 5‑Minute Quick‑Start and Docker Quickstart sections below.

Running the Insight Demo

For the browser-based version, see insight_browser_v1/index.md. It requires Node.js ≥22.17. Install the dependencies with npm ci and build the static assets with npm run build before launching. The repository includes a .nvmrc file so you can simply run nvm use to select the correct Node version.

The α‑AGI Insight demo ships with an offline‑friendly command line interface. After installation, launch the official demo via:

alpha-agi-insight-v1 --episodes 5
# Or run directly from the package
python -m alpha_factory_v1.demos.alpha_agi_insight_v1 --episodes 5

When API keys are configured the program automatically uses the OpenAI Agents runtime. Otherwise it falls back to the local Meta‑Agentic Tree Search. The orchestrator also cleans up the OpenAI runtime on exit to release resources.

For production use, invoke the official demo which automatically checks the environment, selects the best runtime and optionally starts the Google ADK gateway:

alpha-agi-insight-v1 --episodes 5

This wrapper transparently falls back to the offline Meta‑Agentic Tree Search when API credentials are absent, ensuring the demo runs anywhere.

For a guaranteed offline run without external dependencies, use:

AGI_INSIGHT_OFFLINE=1 alpha-agi-insight-v1 --episodes 5

Setting AGI_INSIGHT_OFFLINE=1 ensures the search loop never attempts network access.

When the host cannot reach the internet the environment checker prints a warning and the demos continue in offline mode using any cached data. Optional downloads are skipped automatically.

Several demos ship with small CSV snapshots for offline mode. These samples mirror data from the demo-assets repository and cover roughly March–April 2024.

Meta-Agentic Tree Search Demo

An offline-friendly reference implementation focused on recursive agent-to-agent rewrites lives in meta_agentic_tree_search_v0/README.md. It demonstrates the best‑first search behind the other examples and runs without external APIs.

Offline Mode

Follow these steps when working without internet access. See the documentation for a summary of required environment variables.

1. Build a wheelhouse on a machine with connectivity:

   ./scripts/build_offline_wheels.sh

   The script collects all required wheels under wheels/. Copy this directory to the offline host, for example using scp or a USB drive:

   scp -r wheels user@offline-host:/path/to/AGI-Alpha-Agent-v0/

   Then set the environment variable on the target machine:

   export WHEELHOUSE="/path/to/AGI-Alpha-Agent-v0/wheels"

2. Install from the wheelhouse and verify packages. The setup script automatically uses a wheels/ directory in the repository root when WHEELHOUSE is unset:

   AUTO_INSTALL_MISSING=1 ./codex/setup.sh
   python check_env.py --auto-install --wheelhouse "$WHEELHOUSE"
   pip check

When network access is unavailable, install packages directly from the wheelhouse:

pip install --no-index --find-links "$WHEELHOUSE" -r requirements.txt
# Install demo extras offline
pip install --no-index --find-links "$WHEELHOUSE" -r \
  alpha_factory_v1/demos/era_of_experience/requirements.lock

check_env.py uses the wheels under $WHEELHOUSE. Set WHEELHOUSE="$WHEELHOUSE" when running pre-commit or the tests so dependencies install from the local cache. See Offline Setup for more details. A short reference lives in the documentation. If package installation hangs for more than ten minutes, check_env.py will time out and suggest using --wheelhouse for offline installs.

Run the environment check again when the machine is completely air‑gapped:

python check_env.py --auto-install --wheelhouse "$WHEELHOUSE"

This mirrors the instructions in alpha_factory_v1/scripts/README.md.

See the documentation for a concise summary of the wheelhouse setup.

3. Download a .gguf weight and set LLAMA_MODEL_PATH:

   mkdir -p ~/.cache/llama
   curl -L -o ~/.cache/llama/TinyLlama-1.1B-Chat-v1.0.Q4_K_M.gguf \
     https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q4_K_M.gguf
   export LLAMA_MODEL_PATH=~/.cache/llama/TinyLlama-1.1B-Chat-v1.0.Q4_K_M.gguf

   Common weights and typical CPU throughput:

   Model	Size	~tokens/s
   TinyLlama‑1.1B‑Chat Q4_K_M	380 MB	~20
   Llama‑3‑8B‑Instruct Q4_K_M	4 GB	~5
   Mixtral‑8×7B‑Instruct Q4_0	7 GB	~3

   Install llama-cpp-python or ctransformers to enable offline inference.

4. Fetch and build the browser assets (requires Node.js) to run the Insight demo fully offline:

   cd alpha_factory_v1/demos/alpha_agi_insight_v1/insight_browser_v1
   npm run fetch-assets
   npm ci
   npm run build

   Skipping this step or running without Node.js prevents the service worker from being generated, so offline functionality is limited.

5. Bundle Pyodide for offline demos

   make gallery-build

   This command generates the site/ directory with the Pyodide runtime and demo assets so the browser examples work without a network connection. The service worker caches these files. Use a hard refresh (Ctrl+Shift+R) or clear site data to pick up new releases.

6. Skip browser downloads when running the web demo tests offline:

   PLAYWRIGHT_SKIP_BROWSER_DOWNLOAD=1 npm test

7. Enable offline inference by setting AGI_INSIGHT_OFFLINE=1 in .env or the environment (ensure llama-cpp-python or ctransformers is installed).

8. Disable broadcasting to avoid network calls:

   export AGI_INSIGHT_BROADCAST=0

9. Seed the lineage database from existing DGM logs using --import-dgm.

   python -m alpha_factory_v1.demos.alpha_agi_insight_v1.src.interface.cli \
     simulate --import-dgm path/to/dgm/logs

   Sample sector definitions live in

   alpha_factory_v1/demos/alpha_agi_insight_v1/docs/sectors.sample.json. Pass this file with --sectors-file to forecast specific industries.

   The built-in Sector-Shock-10 dataset ships with the package and is located using importlib.resources when running the demo. This allows simulate to score forecasts even when the repository layout is not available.

Example (using --sectors-file to customise the simulation):

AGI_INSIGHT_OFFLINE=1 AGI_INSIGHT_BROADCAST=0 \
python -m alpha_factory_v1.demos.alpha_agi_insight_v1.src.interface.cli simulate \
  --curve linear --k 8 --x0 0.0 --llama-model-path "$LLAMA_MODEL_PATH" \
  --offline --energy 2.0 --entropy 0.5 \
  --mut-rate 0.1 --xover-rate 0.5 \
  --sectors-file alpha_factory_v1/demos/alpha_agi_insight_v1/docs/sectors.sample.json

Produces output similar to:

OPENAI_API_KEY missing – offline mode enabled
year | capability | affected
-----+------------+---------
1    | 0.88       |
2    | 0.98       |
3    | 1.00       |
4    | 1.00       |
5    | 1.00       |

🎖️ α‑AGI Architect 👁️✨ — Foundational Operational Blueprint

Empowering Meta‑Agentic visionaries with strategic infrastructure. At the core of α‑AGI Ascension is α‑AGI Architect — the foundational operational framework for scalable global deployment. Rooted in the groundbreaking “Multi‑Agent AI DAO” model, α‑AGI Architect delivers immediate, scalable, and adaptive infrastructure ensuring continuous strategic evolution.

• Robust feedback loops driving continuous refinement between Sovereign operations and Architect infrastructure.
• Engineered for rapid global scalability and strategic responsiveness.

flowchart TD
    %% ───────────────────────────  CORE LAYERS  ───────────────────────────
    A[🚀 🎖️ α-AGI Ascension 🌌]
    B[🎖️ α-AGI Insight 👁️✨]
    C[🎖️ α-AGI Sovereign 👁️✨]
    D[🎖️ α-AGI Marketplace 👁️✨]
    E[🎖️ α-AGI Jobs 👁️✨]
    F[🎖️ α-AGI Agents 👁️✨]
    G[🎖️ α-AGI Architect 👁️✨]
    V[💎 Infinite Value Reservoir]

    %% ───────────────────────────  PRIMARY FLOWS  ─────────────────────────
    A --> B
    B --> C
    C --> D
    D --> E
    D --> F
    C --> G
    G -.↺ Continuous optimisation .-> C

    %% ───────────────────────  WEALTH FEEDBACK LOOPS  ─────────────────────
    E -- Harvest ΔΣUSD --> V
    F -- Compound returns --> V
    V -- Reinvest capital --> D

    %% ──────────────────────────────  STYLE  ──────────────────────────────
    classDef asc     fill:#0f172a,color:#ffffff,font-weight:bold,stroke-width:0px
    classDef insight fill:#1e3a8a,color:#ffffff,stroke-width:0px
    classDef market  fill:#0e7490,color:#ffffff,stroke-width:0px
    classDef value   fill:#fde047,color:#000000,font-weight:bold,stroke-width:0px

    class A asc
    class B insight
    class C,G insight
    class D,E,F market
    class V value

    linkStyle default stroke-width:2px

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---

---

Deploy Now

Open‑source framework for immediate strategic action: github.com/MontrealAI/AGI-Alpha- Agent-v0

---

🔱✨ Conclusion

[ 🎖️ α‑AGI Ascension 🌌 ] launches humanity into an entirely new economic epoch. By systematically harnessing AGI’s transformative capabilities, it rewrites global economic structures, implicitly realigning international power dynamics and propelling humanity toward unprecedented sovereign economic prosperity.

---

---

---

Mission 🎯  Identify 🔍 → Learn 📚 → Think 🧠 → Design 🎨 → Strategise ♟️ → Execute ⚡ — compounding real‑world α across all industries.

Global markets seep USD ✧ trillions/yr in latent opportunity — “alpha” in the broadest sense:
pricing dislocations • supply‑chain entropy • novel drug targets • policy loopholes • undiscovered materials.

Alpha‑Factory v1 is an antifragile constellation of self‑improving Agentic α‑AGI Agents 👁️✨ orchestrated to spot live alpha across any industry and transmute it into compounding value.

Definition: An α‑AGI Business 👁️✨ is an on‑chain autonomous enterprise (.a.agi.eth) that unleashes a swarm of self‑improving agentic α‑AGI agents 👁️✨ (.a.agent.agi.eth) to hunt down inefficiencies across any domain and transmute them into $AGIALPHA.

Built atop OpenAI Agents SDK, Google ADK, A2A protocol, and Anthropic’s Model Context Protocol, the stack runs cloud‑native or air‑gapped, hot‑swapping between frontier LLMs and distilled local models.

TL;DR Quick Start

Check out the v0.1.0-alpha tag for a reproducible environment.

git clone --branch v0.1.0-alpha https://github.com/MontrealAI/AGI-Alpha-Agent-v0.git
cd AGI-Alpha-Agent-v0
python3 -m venv .venv
source .venv/bin/activate
# Install runtime dependencies
# Install runtime dependencies
pip install -r requirements.lock  # pinned versions for deterministic setup
# Optional ADK/MCP integration
pip install google-adk mcp
# Requires Python 3.11–3.13 (<3.14)
./quickstart.sh
Run `pre-commit run --all-files` after the dependencies finish installing.
# Open http://localhost:8000/docs in your browser

The adapters initialise automatically when these optional packages are present.

Optional Packages

Install these extras to unlock additional features:

• pip install gradio – enables the MuZero planning dashboard.
• pip install openai-agents==0.0.17 – activates the official Agents runtime used for commentary.
• pip install google-adk and set ALPHA_FACTORY_ENABLE_ADK=true – starts the Google ADK gateway for cross‑organisation agent exchange.
• Install domain‑specific extras as needed (e.g. httpx, feedparser, networkx, lightgbm, kafka-python, tldextract). Each agent logs a warning when a library is missing and continues in degraded mode.

Offline installations can omit these lines from the relevant requirements.txt files if the Agents SDK or ADK gateway are not needed.

To regenerate requirements.lock from requirements.txt with hashes, run:

pip-compile --generate-hashes --output-file requirements.lock requirements.txt

Once the API server is running you can launch a simulation:

curl -X POST http://localhost:8000/simulate \
  -H "Authorization: Bearer $API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"horizon": 5, "pop_size": 6, "generations": 3, "mut_rate": 0.1, "xover_rate": 0.5, "curve": "linear", "energy": 1.0, "entropy": 1.0}'

Further Reading

• Full documentation is available at https://montrealai.github.io/AGI-Alpha-Agent-v0/ — click Docs in the navigation bar.

---

Contributing

See AGENTS.md for the full contributor guide.

Pre‑commit Hooks

After running ./codex/setup.sh, which ensures pre-commit==4.2.0 is installed, install the hooks and run a full check:

# Install the exact version if the setup script didn't already
pip install pre-commit==4.2.0
pre-commit install
pre-commit run --all-files   # verify hooks after setup
pre-commit run --files <paths>   # before each commit

Run pre-commit run --all-files once after the setup script to confirm everything is formatted correctly. These commands mirror the steps in AGENTS.md and keep commits consistent. After editing .github/workflows/ci.yml, run:

pre-commit run --files .github/workflows/ci.yml
python tools/update_actions.py

to validate the workflow with actionlint before committing. Before opening a pull request, run pre-commit run --all-files to ensure all hooks succeed. Run python check_env.py --auto-install before invoking these commands so optional hook dependencies are installed. When working offline, pass --wheelhouse <dir> or set WHEELHOUSE to install from a local cache. If pre-commit isn't found or the version differs, install it with pip install pre-commit==4.2.0.

When editing the web UI, preserve existing ARIA labels so the interface remains accessible.

Development Setup

Install the Python dependencies with the helper script:

scripts/setup_env.sh

The script checks for Python 3.11–3.13 (<3.14) and installs requirements.txt and requirements-dev.txt.

When preparing an offline environment, build a wheelhouse on a machine with internet access:

./scripts/build_offline_wheels.sh

Copy the resulting wheels/ directory to the target host and set WHEELHOUSE=$(pwd)/wheels before running check_env.py or the tests so packages install from the local cache. The repository does not ship these prebuilt wheels.

📜 Table of Contents

0. Design Philosophy
1. System Topology 🗺️
2. World‑Model & Planner 🌌
3. Agent Gallery 🖼️ (12 agents)
4. Demo Showcase 🎬 (14 demos)
5. Memory & Knowledge Fabric 🧠
6. 5‑Minute Quick‑Start 🚀 6.1. Running Tests 🧪 6.2. Marketplace Demo Example 🛒 6.3. Offline Mode
   • Set LLAMA_MODEL_PATH to the downloaded .gguf weight
   • AGI_INSIGHT_BROADCAST=0 disables blockchain broadcasting
   • Example:

     AGI_INSIGHT_OFFLINE=1 AGI_INSIGHT_BROADCAST=0
       python -m alpha_factory_v1.demos.alpha_agi_insight_v1.src.interface.cli
       simulate --offline --energy 2.0 --entropy 0.5 \
       --mut-rate 0.1 --xover-rate 0.5 \
       --llama-model-path "$LLAMA_MODEL_PATH"

7. Deployment Recipes 🍳 7.1. Deploying securely 🚀
8. Governance & Compliance ⚖️
9. Observability 🔭
10. Safety & Security 🛡️
11. Extending the Mesh 🔌
12. Troubleshooting 🛠️
13. Roadmap 🛣️
14. Credits 🌟
15. License 📝

---

0 · Design Philosophy

“We have shifted from big‑data hoarding to big‑experience compounding.” — Era of Experience.

• Experience‑First Loop — Sense → Imagine (MuZero‑style latent planning) → Act → Adapt.
• AI‑GA Autogenesis — The factory meta‑evolves new agents and curricula inspired by Clune’s AI‑Generating Algorithms.
• Graceful Degradation — GPU‑less? No cloud key? Agents fall back to distilled local models & heuristics.
• Zero‑Trust Core — SPIFFE identities, signed artefacts, guard‑rails, exhaustive audit logs.
• Polyglot Value — Everything is normalised to a common alpha Δ∑USD lens.

---

1 · System Topology 🗺️

flowchart LR
  ORC([🛠️ Orchestrator])
  WM[(🌌 World‑Model)]
  MEM[(🔗 Vector‑Graph Memory)]
  subgraph Agents
    FIN(💰)
    BIO(🧬)
    MFG(⚙️)
    POL(📜)
    ENE(🔋)
    SUP(📦)
    RET(🛍️)
    CYB(🛡️)
    CLM(🌎)
    DRG(💊)
    SCT(⛓️)
    TAL(🧑‍💻)
  end
  ORC -- A2A --> Agents
  Agents -- experience --> WM
  WM -- embeddings --> MEM
  ORC -- Kafka --> DL[(🗄️ Data Lake)]

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• Orchestrator auto‑discovers agents (see backend/agents/__init__.py) and exposes a unified REST + gRPC facade.
• World‑Model uses MuZero‑style latent dynamics for counterfactual planning.
• Memory Fabric = pgvector + Neo4j for dense & causal recall.

---

2 · World‑Model & Planner 🌌

Component	Source Tech	Role
Latent Dynamics	MuZero++	Predict env transitions & value
Self‑Play Curriculum	POET‑XL	Generates alpha‑labyrinth tasks
Meta‑Gradient	AI‑GA	Evolves optimiser hyper‑nets
Task Selector	Multi‑Armed Bandit	Schedules agent ↔ world‑model interactions

---

3 · Agent Gallery 🖼️

flowchart TD
    ORC["🛠️ Orchestrator"]
    GEN{{"🧪 Env‑Generator"}}
    LRN["🧠 MuZero++"]

    subgraph Agents
        FIN["💰"]
        BIO["🧬"]
        MFG["⚙️"]
        POL["📜"]
        ENE["🔋"]
        SUP["📦"]
        RET["🛍️"]
        MKT["📈"]
        CYB["🛡️"]
        CLM["🌎"]
        DRG["💊"]
        SMT["⛓️"]
    end

    %% message flows
    GEN -- tasks --> LRN
    LRN -- policies --> Agents
    Agents -- skills --> LRN

    ORC -- A2A --> FIN
    ORC -- A2A --> BIO
    ORC -- A2A --> MFG
    ORC -- A2A --> POL
    ORC -- A2A --> ENE
    ORC -- A2A --> SUP
    ORC -- A2A --> RET
    ORC -- A2A --> MKT
    ORC -- A2A --> CYB
    ORC -- A2A --> CLM
    ORC -- A2A --> DRG
    ORC -- A2A --> SMT
    ORC -- A2A --> GEN
    ORC -- A2A --> LRN

    ORC -- Kafka --> DATALAKE["🗄️ Data Lake"]
    FIN -.->|Prometheus| GRAFANA{{"📊"}}

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#	Agent	Path	Prime Directive	Status	Key Env Vars
1	Finance 💰	finance_agent.py	Multi‑factor alpha & RL execution	Prod	BROKER_DSN
2	Biotech 🧬	biotech_agent.py	CRISPR & assay proposals	Prod	OPENAI_API_KEY
3	Manufacturing ⚙️	manufacturing_agent.py	CP‑SAT optimiser	Prod	SCHED_HORIZON
4	Policy 📜	policy_agent.py	Statute QA & diffs	Prod	STATUTE_CORPUS_DIR
5	Energy 🔋	energy_agent.py	Spot‑vs‑forward arbitrage	Beta	ISO_TOKEN
6	Supply‑Chain 📦	supply_chain_agent.py	Stochastic MILP routing	Beta	SC_DB_DSN
7	Retail Demand 🛍️	retail_demand_agent.py	SKU forecast & pricing	Beta	POS_DB_DSN
8	Cyber‑Sec 🛡️	cyber_threat_agent.py	Predict & patch CVEs	Beta	VT_API_KEY
9	Climate Risk 🌎	climate_risk_agent.py	ESG stress tests	Beta	NOAA_TOKEN
10	Drug‑Design 💊	drug_design_agent.py	Diffusion + docking	Incub	CHEMBL_KEY
11	Smart‑Contract ⛓️	smart_contract_agent.py	Formal verification	Incub	ETH_RPC_URL
12	Talent‑Match 🧑‍💻	talent_match_agent.py	Auto‑bounty hiring	Incub	—

%% Legend
%%  solid arrows  = primary value‑flow
%%  dashed arrows = secondary / supporting influence
%%  node emojis   = domain archetypes

graph TD
    %% Core pillars
    FIN["💰 Finance"]
    BIO["🧬 Biotech"]
    MFG["⚙️ Manufacturing"]
    POL["📜 Policy / Reg‑Tech"]
    ENE["🔋 Energy"]
    SUP["📦 Supply‑Chain"]
    RET["🛍️ Retail / Demand"]
    CYB["🛡️ Cyber‑Security"]
    CLM["🌎 Climate"]
    DRG["💊 Drug Design"]
    SMT["⛓️ Smart Contracts"]
    TLT["🧑‍💼 Talent"]

    %% Derived transversal competences
    QNT["📊 Quant R&D"]
    RES["🔬 Research Ops"]
    DSG["🎨 Design"]
    OPS["🔧 DevOps"]

    %% Primary value‑creation arcs
    FIN -->|Price discovery| QNT
    FIN -->|Risk stress‑test| CLM
    BIO --> DRG
    BIO --> RES
    MFG --> SUP
    ENE --> CLM
    RET --> FIN
    POL --> CYB
    SMT --> FIN

    %% Cross‑pollination (secondary, dashed)
    FIN -.-> POL
    SUP -.-> CLM
    CYB -.-> OPS
    DRG -.-> POL
    QNT -.-> RES
    RET -.-> DSG

    %% Visual grouping
    subgraph Core
        FIN
        BIO
        MFG
        POL
        ENE
        SUP
        RET
        CYB
        CLM
        DRG
        SMT
        TLT
    end
    classDef core fill:#0d9488,color:#ffffff,stroke-width:0px;

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Each agent exports a signed proof‑of‑alpha message to the Kafka bus, enabling cross‑breeding of opportunities.

sequenceDiagram
    participant User
    participant ORC as Orchestrator
    participant FIN as 💰
    participant GEN as 🧪
    User->>ORC: /alpha/run
    ORC->>GEN: new_world()
    GEN-->>ORC: env_json
    ORC->>FIN: act(env)
    FIN-->>ORC: proof(ΔG)
    ORC-->>User: artefact + KPI

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---

4 · Demo Showcase 🎬

#	Folder	Emoji	Lightning Pitch	Alpha Contribution	Start Locally
1	aiga_meta_evolution	🧬	Agents evolve new agents; genetic tests auto‑score fitness.	Expands strategy space, surfacing fringe alpha.	cd alpha_factory_v1/demos/aiga_meta_evolution && ./run_aiga_demo.sh
2	alpha_agi_business_v1	🏦	Auto‑incorporates a digital‑first company end‑to‑end.	Shows AGI turning ideas → registered business.	./alpha_factory_v1/demos/alpha_agi_business_v1/run_business_v1_demo.sh [--pull] [--gpu] (docs: http://localhost:8000/docs)
3	alpha_agi_business_2_v1	🏗	Iterates business model with live market data RAG.	Continuous adaptation → durable competitive alpha.	./alpha_factory_v1/demos/alpha_agi_business_2_v1/run_business_2_demo.sh
4	alpha_agi_business_3_v1	📊	Financial forecasting & fundraising agent swarm.	Optimises capital stack for ROI alpha.	./alpha_factory_v1/demos/alpha_agi_business_3_v1/run_business_3_demo.sh
5	alpha_agi_marketplace_v1	🛒	Peer‑to‑peer agent marketplace simulating price discovery.	Validates micro‑alpha extraction via agent barter.	docker compose -f demos/docker-compose.marketplace.yml up
6	alpha_asi_world_model	🌌	Scales MuZero‑style world‑model to an open‑ended grid‑world.	Stress‑tests anticipatory planning for ASI scenarios.	docker compose -f demos/docker-compose.asi_world.yml up
7	cross_industry_alpha_factory	🌐	Full pipeline: ingest → plan → act across 4 verticals.	Proof that one orchestrator handles multi‑domain alpha.	./alpha_factory_v1/demos/cross_industry_alpha_factory/deploy_alpha_factory_cross_industry_demo.sh
8	era_of_experience	🏛	Lifelong RL stack blending real & synthetic experience streams.	Showcases sensor-motor tools, grounded rewards & non-human reasoning.	cd alpha_factory_v1/demos/era_of_experience && ./run_experience_demo.sh
9	finance_alpha	💹	Live momentum + risk‑parity bot on Binance test‑net.	Generates real P&L; stress‑tested against CVaR.	./alpha_factory_v1/demos/finance_alpha/deploy_alpha_factory_demo.sh
10	macro_sentinel	🌐	GPT‑RAG news scanner auto‑hedges with CTA futures.	Shields portfolios from macro shocks.	docker compose -f demos/docker-compose.macro.yml up
11	muzero_planning	♟	MuZero in 60 s; online world‑model with MCTS.	Distills planning research into a one‑command demo.	./alpha_factory_v1/demos/muzero_planning/run_muzero_demo.sh
12	self_healing_repo	🩹	CI fails → agent crafts patch ⇒ PR green again.	Maintains pipeline uptime alpha.	docker compose -f demos/docker-compose.selfheal.yml up
13	meta_agentic_tree_search_v0	🌳	Recursive agent rewrites via best‑first search.	Rapidly surfaces AGI-driven trading alpha.	mats-bridge --episodes 3
14	alpha_agi_insight_v1	👁️	Zero‑data search ranking AGI‑disrupted sectors.	Forecasts sectors primed for AGI transformation.	alpha-agi-insight-v1 --episodes 5

Colab? Each folder ships an *.ipynb that mirrors the Docker flow with free GPUs.

The official Docker image bundles PyTorch 2.2.x and Ray 2.10.0. The notebooks install PyTorch from the PyTorch wheel index and pin Ray to the same version for compatibility.

• Solving AGI Governance — Monte‑Carlo governance simulation with optional OpenAI‑Agents/ADK integration. Colab
• Self‑Healing Repo — agents automatically craft patches when CI fails. The underlying MetaRefinementAgent only simulates improvement by generating placeholder diffs. We hope to replace this with genuine optimisation based on real performance metrics—contributions are warmly welcomed.
• Note: The alpha_agi_business_3_v1 demo is intentionally left out of the published package. Clone this repository to run it from source.

USE_GPU	PyTorch wheel URL
True	https://download.pytorch.org/whl/cu118
False	https://download.pytorch.org/whl/cpu

4.1 · [α-ASI World-Model Demo 👁️✨](

https://github.com/MontrealAI/AGI-Alpha-Agent-v0/tree/main/alpha_factory_v1/demos/alpha_asi_world_model)

Paper: [Multi-Agent AGENTIC α-AGI World-Model Demo 🥑](https://github.com/MontrealAI/AGI-Alpha- Agent-v0/blob/main/alpha_factory_v1/demos/alpha_asi_world_model/Alpha_ASI_World_Model.pdf)

┌──────────────────────────────── Alpha-Factory Bus (A2A) ───────────────────────────────┐
│                                                                                        │
│   ┌──────────────┐   curriculum   ┌───────────┐   telemetry   ┌────────────┐          │
│   │ StrategyAgent│───────────────►│ Orchestr. │──────────────►│   UI / WS  │          │
│   └──────────────┘                │  (loop)   │◄──────────────│  Interface │          │
│          ▲  ▲                     └───────────┘    commands   └────────────┘          │
│          │  │ new_env/reward                     ▲                                   │
│   plans  │  │ loss stats                        │ halt                              │
│          │  └──────────────────────┐            │                                   │
│   ┌──────┴───────┐   context       │            │                                   │
│   │ ResearchAgent│───────────────► Learner (MuZero) ◄─ SafetyAgent (loss guard)      │
│   └──────────────┘                │   ▲                                             │
│              code patches         │   │                                             │
│   ┌──────────────┐                │   │ gradients                                   │
│   │ CodeGenAgent │────────────────┘   │                                             │
│   └──────────────┘                    │                                             │
│                                       ▼                                             │
│                            POET Generator → MiniWorlds (env pool)                    │
└────────────────────────────────────────────────────────────────────────────────────────┘

4.2 · [🏛️ Large‑Scale α‑AGI Business 3 Demo 👁️✨ — Omega‑Grade Edition](

https://github.com/MontrealAI/AGI-Alpha-Agent-v0/tree/main/alpha_factory_v1/demos/alpha_agi_business_3_v1)

Alpha‑Factory v1 → Ω‑Lattice v0
Transmuting cosmological free‑energy gradients into compounding cash‑flows.

Multi‑Scale Energy‑Landscape Diagram:

flowchart TB
  subgraph Macro["Macro‑Finance Δβ"]
    FIN[FinanceAgent]:::agent
    ENE[EnergyAgent]:::agent
  end
  subgraph Meso["Supply‑Chain ΔS"]
    MFG[ManufacturingAgent]:::agent
    LOG[LogisticsAgent]:::agent
  end
  subgraph Micro["Bio/Chem ΔH"]
    BIO[BiotechAgent]:::agent
    MAT[MaterialsAgent]:::agent
  end
  FIN & ENE -->|β feed| ORC
  MFG & LOG -->|entropy ΔS| ORC
  BIO & MAT -->|latent ΔH| ORC
  classDef agent fill:#cffafe,stroke:#0369a1;

Loading

Cells with (Δ\mathcal F < 0) glow 🔵 on Grafana; Ω‑Agents race to harvest.

---

5 · Memory & Knowledge Fabric 🧠

[Event] --embedding--> PGVector DB
                   \--edge--> Neo4j (CAUSES, SUPPORTS, RISK_OF)

• Agents query mem.search("supply shock beta>0.2")
• Planner asks Neo4j: MATCH (a)-[:CAUSES]->(b) WHERE b.delta_alpha > 5e6 RETURN path
• SQLite vector store fallback requires numpy
• Realistic operation also relies on pandas

---

6 · 5‑Minute Quick‑Start 🚀

This guide assumes the repository is cloned at v0.1.0-alpha. The walkthrough requires the numpy, yaml and pandas packages which check_env.py installs automatically when run with --auto-install.

git clone --branch v0.1.0-alpha https://github.com/MontrealAI/AGI-Alpha-Agent-v0.git
cd AGI-Alpha-Agent-v0
./quickstart.sh --preflight   # optional environment check
python check_env.py --auto-install  # verify & auto-install deps (10 min timeout)
# Install heavy optional packages such as openai_agents, gymnasium and google_adk.
# Running tests without these extras will skip or fail the modules that depend on
# them. Use ALPHA_FACTORY_FULL=1 with check_env.py to ensure they install. See
# `tests/README.md` for detailed instructions.
ALPHA_FACTORY_FULL=1 python check_env.py --auto-install
# Install runtime dependencies
pip install -r requirements.lock
# (If this fails with a network error, create a wheelhouse and rerun
#  with --wheelhouse <path> or place the wheels under ./wheels)
# Build a wheelhouse if the machine has no internet access:
#   ./scripts/build_offline_wheels.sh
./quickstart.sh               # creates venv, installs deps, launches
# Use `--wheelhouse /path/to/wheels` to install offline packages when
# the host has no internet access. The setup script automatically
# sets `WHEELHOUSE` to `./wheels` when that directory exists. When
# working offline, run `python check_env.py --auto-install --wheelhouse
# /path/to/wheels` to verify and install packages. The setup script
# exits with a message if neither network nor a wheelhouse are available.
# Example offline workflow:
#   export WHEELHOUSE=$(pwd)/wheels
#   python check_env.py --auto-install --wheelhouse "$WHEELHOUSE"
#   WHEELHOUSE=$WHEELHOUSE ./quickstart.sh
#   WHEELHOUSE=$WHEELHOUSE pytest -q
# Open http://localhost:8000/docs in your browser
# Alternatively, ``python alpha_factory_v1/quickstart.py`` provides the same
# workflow on Windows and other systems without Bash.

# Deploy instantly with Docker (prebuilt image)
docker run --pull=always -p 8000:8000 ghcr.io/montrealai/alpha-factory:latest

# Pull a specific build by commit SHA
docker run --pull=always -p 8000:8000 ghcr.io/montrealai/alpha-factory:<commit>

# The `alpha-factory` CLI also works when the package is installed:
# A short warning is printed before startup.
#   pip install -e .
#   alpha-factory --list-agents
#   alpha-asi-demo --demo   # launch the α‑ASI world‑model UI
#   alpha-agi-insight-v1 orchestrator   # run the Insight orchestrator
#
# Or install directly from GitHub for a quick test:
#   pip install git+https://github.com/MontrealAI/[email protected]
#   alpha-factory --list-agents

# Automated one-click setup (builds & starts Docker stack)
./alpha_factory_v1/scripts/one_click_install.sh --deploy

# Verify the Ω‑Lattice demo locally
python alpha_factory_v1/demos/alpha_agi_business_3_v1/alpha_agi_business_3_v1.py --loglevel info
# The entrypoint automatically verifies dependencies via `check_env.py`.

Adjust alpha_factory_v1/demos/alpha_asi_world_model/config.yaml to tune the world-model loop. Key options include env_batch (parallel environments), hidden (latent state size) and mcts_simulations (MCTS rollouts per action).

Insight Browser Demo

A browser-only Pareto explorer lives under alpha_factory_v1/demos/alpha_agi_insight_v1/insight_browser_v1. Run npm run build in that directory to generate the dist/ assets (they are not stored in Git) then open dist/index.html to run the demo locally. The quick-start guide is available from the documentation and is copied to dist/insight_browser_quickstart.pdf during the build so it is available alongside the compiled assets. Set window.DEBUG = true before loading the page to expose debugging helpers such as window.pop.

For evolutionary experiments you can run the optional evolution worker container and POST a tarball of agent code to /mutate.

Docker Quickstart

Start the full stack using Docker Compose:

docker compose up --build

Browse the dashboard at http://localhost:8080.

One-Click Docker Quickstart

Run the minimal image directly:

./run_quickstart.sh

The script prints the project disclaimer, builds docker/quickstart/Dockerfile and launches the container with your .env file mounted.

The same configuration can be installed via Helm:

helm upgrade --install alpha-demo ./infrastructure/helm-chart \
  --values ./infrastructure/helm-chart/values.yaml \
  --set env.RUN_MODE=web

This deploys the services to your local Kubernetes cluster.

Generate TLS certificates for the gRPC bus using the bundled helper:

./infrastructure/gen_bus_certs.sh > .env.bus
source .env.bus

The script prints AGI_INSIGHT_BUS_CERT, AGI_INSIGHT_BUS_KEY and AGI_INSIGHT_BUS_TOKEN which you can append to your .env file.

.env Setup & Security

Before running the orchestrator, copy alpha_factory_v1/.env.sample to .env and replace all placeholder values with strong secrets. The sample sets NEO4J_PASSWORD=REPLACE_ME as a placeholder—generate a random password for services like Neo4j and Postgres using openssl rand -base64 18 or a similar tool and never deploy with the defaults. The orchestrator will refuse to start if NEO4J_PASSWORD remains REPLACE_ME or is missing. Set API_TOKEN to a strong secret so that the REST API can authenticate incoming requests. Clients must send Authorization: Bearer <token>. The server aborts if API_TOKEN equals REPLACE_ME_TOKEN.

API Token Requirement

Before starting the API server or running the test suite, ensure API_TOKEN is set to a non-default value. The examples in tests/test_api_status.py use test-token as a reference token for local runs. Use API_RATE_LIMIT to limit requests per minute per IP (default 60). If more than 5% of requests return HTTP 429 within a minute, the server calls utils.alerts.send_alert to report excessive throttling. Avoid storing private keys directly in .env. Instead set AGI_INSIGHT_SOLANA_WALLET_FILE to a file containing your hex-encoded wallet key and keep that file readable only by the orchestrator. To enable secure gRPC transport set AGI_INSIGHT_BUS_CERT, AGI_INSIGHT_BUS_KEY and AGI_INSIGHT_BUS_TOKEN. If these values are omitted and AGI_INSIGHT_ALLOW_INSECURE=1, the bus starts without TLS. See the documentation for instructions and example volume mounts.

.env.sample notes that paths on Windows may require quotes (e.g., C:\\path\\to\\file).

Supported Environment Variables

Variable	Default	Purpose
OPENAI_API_KEY	(empty)	API key for hosted models. Offline mode is used when empty.
OPENAI_TIMEOUT_SEC	30	Timeout for OpenAI API requests in seconds.
NO_LLM	0	Set to 1 to skip the LLM planner even when OPENAI_API_KEY is provided.
ALPHA_ASI_LLM_MODEL	gpt-4o-mini	Planner model name used by the world model demo.
ALPHA_ASI_SEED	42	Deterministic RNG seed for the demo (can also be set via general.seed in config.yaml).
ALPHA_ASI_MAX_STEPS	100000	Learner steps before auto-stop.
ALPHA_ASI_BUFFER_LIMIT	50000	Replay-buffer length.
ALPHA_ASI_TRAIN_BATCH	128	SGD mini-batch size.
ALPHA_ASI_MAX_GRID	64	Safety clamp on generated mazes.
ALPHA_ASI_HOST	0.0.0.0	FastAPI bind address for the demo.
ALPHA_ASI_PORT	7860	FastAPI port for the demo.
NEO4J_PASSWORD	REPLACE_ME	Database password required by the orchestrator.
RUN_MODE	api	Launch mode for Compose or Helm (api, cli, web).
PORT	8000	REST API port.
AGI_INSIGHT_OFFLINE	0	Set to 1 to force local inference models.
AGI_INSIGHT_BUS_PORT	6006	gRPC bus port used by the demo.
AGI_INSIGHT_LEDGER_PATH	./ledger/audit.db	Path to the local audit ledger.
AGI_INSIGHT_SECRET_BACKEND	(empty)	Set to vault, aws or gcp to load secrets from an external manager.
VAULT_ADDR/VAULT_TOKEN	(empty)	Connection details for HashiCorp Vault when using the vault backend.
AWS_REGION/OPENAI_API_KEY_SECRET_ID	(empty)	AWS Secrets Manager region and secret ID when using the aws backend.
GCP_PROJECT_ID/OPENAI_API_KEY_SECRET_ID	(empty)	GCP project and secret name when using the gcp backend.
AGI_INSIGHT_BUS_CERT	(empty)	Path to the gRPC bus certificate.
AGI_INSIGHT_BUS_KEY	(empty)	Private key matching AGI_INSIGHT_BUS_CERT.
AGI_INSIGHT_BUS_TOKEN	(empty)	Shared secret for bus authentication.
AGI_INSIGHT_ALLOW_INSECURE	0	Set to 1 to run the bus without TLS when no certificate is provided.
API_TOKEN	REPLACE_ME_TOKEN	Bearer token required by the REST API. Startup fails if unchanged.
API_CORS_ORIGINS	*	Comma-separated list of allowed CORS origins.
SANDBOX_CPU_SEC	2	CPU time limit for sandboxed code.
SANDBOX_MEM_MB	256	Memory cap for sandboxed code in MB.
MAX_RESULTS	100	Maximum stored simulation results.
MAX_SIM_TASKS	4	Maximum concurrent simulation tasks.
IPFS_GATEWAY	https://ipfs.io/ipfs	Base URL for fetching pinned Insight demo runs. Not used for asset downloads.
HF_GPT2_BASE_URL	https://huggingface.co/openai-community/gpt2/resolve/main	Base URL for the GPT‑2 checkpoints.
PYODIDE_BASE_URL	https://cdn.jsdelivr.net/pyodide/v0.28.0/full	Base URL for the Pyodide runtime files.
FETCH_ASSETS_ATTEMPTS	3	Download retry count for fetch_assets.py.
FETCH_ASSETS_BACKOFF	1	Base delay between retries in seconds.
OTEL_ENDPOINT	(empty)	OTLP endpoint for anonymous telemetry.
SKIP_WEBKIT_TESTS	(empty)	Skip WebKit browser tests when set.
ALPHA_FACTORY_ENABLE_ADK	false	Set to true to start the Google ADK gateway.
ALPHA_FACTORY_ADK_PORT	9000	Port for the ADK gateway when enabled.
ALPHA_FACTORY_ADK_TOKEN	(empty)	Optional auth token for the ADK gateway.

Troubleshooting Asset Downloads

If scripts/fetch_assets.py or npm run fetch-assets returns 401 or 404, download the checkpoint directly:

python scripts/download_hf_gpt2.py models/gpt2
# Or fetch manually from Hugging Face
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

For a production-ready ADK setup see PRODUCTION_GUIDE.md.

Finance Demo Quick‑Start

Launch the finance alpha demo directly from your terminal:

curl -L https://raw.githubusercontent.com/MontrealAI/AGI-Alpha-Agent-v0/main/alpha_factory_v1/demos/finance_alpha/deploy_alpha_factory_demo.sh | bash

The script pulls the signed demo container, runs a BTC/GLD strategy, prints open positions and P&L, and exposes the trace‑graph UI at http://localhost:${TRACE_WS_PORT}.

Need a different pair or port? Use environment variables: STRATEGY=my_pair PORT_API=8001 TRACE_WS_PORT=9000 bash deploy_alpha_factory_demo.sh

No GPU → falls back to GGML Llama‑3‑8B‑Q4. No OPENAI_API_KEY → switches to local SBERT + heuristics. AF_LLM_CACHE_SIZE caps in-memory LLM cache entries (default 1024). AF_PING_INTERVAL sets the ping frequency in seconds (default 60, minimum 5). AF_DISABLE_PING_AGENT=true disables the built‑in ping agent.

---

6.1 · Running Tests 🧪

Unit tests can be executed with the bundled helper script:

python -m alpha_factory_v1.scripts.run_tests

Always run python check_env.py --auto-install beforehand so optional dependencies like openai_agents and gymnasium are present. Use ALPHA_FACTORY_FULL=1 to install heavy extras.

The helper validates the target directory, prefers pytest when available and otherwise falls back to unittest. Ensure all tests pass before deploying changes.

Install the optional test dependencies with:

pip install -r requirements-dev.txt
pip install -r requirements.lock  # pinned versions for deterministic setup
pip install -r alpha_factory_v1/backend/requirements-lock.txt  # includes RDKit

Install the project in editable mode so tests resolve imports:

pip install -e .
python check_env.py --auto-install  # times out after 10 minutes
# Install heavier extras so tests needing torch or faiss work:
ALPHA_FACTORY_FULL=1 python check_env.py --auto-install

The run_tests helper automatically executes python check_env.py --auto-install before running pytest. When offline, set WHEELHOUSE or pass --wheelhouse <dir> so packages install from the local wheel cache. The repository ships with a wheels/ directory that can be used as this cache. The full test suite relies on optional packages including numpy, torch, pandas, prometheus_client, gymnasium, playwright, httpx, uvicorn, git, hypothesis and requests_mock.

Tests that depend on optional heavy packages skip automatically when those libraries are missing. Set environment variables like SKIP_WEBKIT_TESTS=1 or PYTEST_NET_OFF=1 to bypass browser or network tests if the necessary dependencies aren't installed.

Wheelhouse Setup

Tests install packages from PyPI unless a local wheelhouse is provided. Build one from requirements.lock and point WHEELHOUSE to it before verifying the environment and running the suite:

mkdir -p wheels
pip wheel -r requirements.lock -w wheels
export WHEELHOUSE=$(pwd)/wheels
python check_env.py --auto-install --wheelhouse "$WHEELHOUSE"
WHEELHOUSE="$WHEELHOUSE" pytest -q

If network access is unavailable and the variable is unset these commands fail instead of falling back to PyPI.

Offline or Restricted Environments

Run ./scripts/build_offline_wheels.sh to populate a wheelhouse on a machine with internet access, then set WHEELHOUSE=<path> before executing the tests so dependencies install from this local cache. If npx playwright install fails to download WebKit, set SKIP_WEBKIT_TESTS=1 so browser checks skip gracefully.

Test Runtime

Running pytest may take several minutes on the first run while caches are created. Execute the suite in verbose mode to see ongoing progress:

pytest -vv

After completion pytest prints a summary such as ### passed in 120.00s.

The suite includes tests/test_api_rate_limit.py which spins up api_server.app with API_RATE_LIMIT=2 and verifies that exceeding the limit returns HTTP 429.

6.2 · Marketplace Demo Example 🛒

A minimal snippet queues the sample job once the orchestrator is running:

alpha-factory --enabled finance,manufacturing &
python - <<'PY'
import subprocess, time
from alpha_factory_v1.demos import alpha_agi_marketplace_v1 as market
time.sleep(5)
subprocess.run(["bash", str(market.POST_JOB_SCRIPT), str(market.SAMPLE_JOB)], check=True)
marketplace_args = ["python", "-m", "alpha_factory_v1.demos.alpha_agi_marketplace_v1.marketplace", str(market.SAMPLE_JOB)]
subprocess.run(marketplace_args, check=True)
PY

---

6.2 · Cross-Industry Demo Quick‑Start 🌐

Clone the stable v0.1.0-alpha release:

git clone --branch v0.1.0-alpha https://github.com/MontrealAI/AGI-Alpha-Agent-v0.git
cd AGI-Alpha-Agent-v0/alpha_factory_v1/demos/cross_industry_alpha_factory
# Set AUTO_COMMIT=1 to save generated assets back to the repo
./deploy_alpha_factory_cross_industry_demo.sh

---

6.3 · Signing Agent Wheels 🔑

Sign wheels dropped into $AGENT_HOT_DIR with the project ED25519 key. You need OpenSSL to create and verify signatures. Install it with brew install openssl on macOS or from the OpenSSL Windows binaries. Generate <wheel>.whl.sig via:

openssl dgst -sha512 -binary <wheel>.whl |
  openssl pkeyutl -sign -inkey agent_signing.key |
  base64 -w0 > <wheel>.whl.sig

Keep <wheel>.whl.sig next to the wheel in $AGENT_HOT_DIR.

Verify the signature (PowerShell example):

Get-Content <wheel>.whl -Encoding Byte |
  openssl dgst -sha512 -binary |
  openssl pkeyutl -verify -pubin -inkey $env:AGENT_WHEEL_PUBKEY -sigfile <wheel>.whl.sig

Add the base64 signature to _WHEEL_SIGS in alpha_factory_v1/backend/agents/__init__.py. Wheels failing verification are ignored.

6.4 · Web Dashboard Quick-Start 📊

Launch the local web interface:

uvicorn alpha_factory_v1.demos.alpha_agi_insight_v1.src.interface.api_server:app --reload
streamlit run alpha_factory_v1/demos/alpha_agi_insight_v1/src/interface/web_app.py
# React client
cd alpha_factory_v1/demos/alpha_agi_insight_v1/src/interface/web_client
npm ci          # use the lock file for reproducible installs
npm run dev       # http://localhost:5173
# build production assets
npm run build
python -m http.server --directory dist 9000

Alternatively run inside Docker:

# build the web client first so `dist/` exists
make build_web
# regenerate protobuf modules and Go stubs
./tools/gen_proto_stubs.sh  # updates alpha_factory_v1/core/utils/a2a_pb2.py and tools/go_a2a_client/a2a.pb.go
make compose-up  # builds and waits for healthy services

Run ./tools/gen_proto_stubs.sh whenever alpha_factory_v1/core/utils/a2a.proto changes to keep the Python and Go stubs up to date. Open http://localhost:8080 in your browser. When RUN_MODE=web, the container serves the static files from alpha_factory_v1/demos/alpha_agi_insight_v1/src/interface/web_client/dist using python -m http.server. The FastAPI demo also mounts this folder at / when present so the dashboard is reachable without additional tooling.

Once running, Docker Compose marks the services healthy when:

• http://localhost:8000/healthz returns status 200 for the orchestrator container.
• http://localhost:8000/status exposes agent heartbeats and restart counts. Use alpha-agi-insight-v1 agents-status to view the same data from the CLI.
• http://localhost:8080/ returns status 200 for the web container.

The dashboard now plots a 3‑D scatter chart of effectiveness vs. risk vs. complexity from the final population.

If Streamlit isn't installed or you're running on a headless server, use:

python -m alpha_factory_v1.demos.alpha_agi_insight_v1.src.interface.minimal_ui --text

to display the forecast results directly in the console.

---

7 · Deployment Recipes 🍳

The repository bundles a lightweight edge_runner.py helper for running Alpha‑Factory on air‑gapped or resource‑constrained devices. The script forwards to alpha_factory_v1.edge_runner and exposes additional flags like --cycle, --loglevel and --version. It prints the same warning as the main CLI before launching.

Build the demo containers locally:

cp .env.sample .env  # fill in NEO4J_PASSWORD, API_TOKEN and optional PINNER_TOKEN
chmod 600 alpha_factory_v1/.env
cd infrastructure
docker build -t alpha-demo .
docker compose up -d
# Dashboard available at <http://localhost:8080>

The Compose stack restricts the agents worker using Docker resource limits. The agents service runs with mem_limit: 8g, pids_limit: 512 and network_mode: none to prevent outbound traffic.

The Helm chart under infrastructure/helm-chart mirrors this Compose setup:

helm upgrade --install alpha-demo ./infrastructure/helm-chart \
  --values ./infrastructure/helm-chart/values.yaml \
  --set env.RUN_MODE=web
# Enable persistent storage for the audit ledger
#   --set persistence.enabled=true --set persistence.size=5Gi
# → browse to <http://localhost:8080>

values.example.yaml demonstrates typical overrides such as API tokens, service ports and replica counts.

The Helm charts ship with placeholders like NEO4J_PASSWORD and adminPassword set to REPLACE_ME. Replace these with strong secrets in values.yaml or via --set before deploying.

Terraform scripts in infrastructure/terraform provide GCP and AWS examples. Update the placeholder image and networking variables, then initialise and apply:

cd infrastructure/terraform
terraform init
terraform apply

Target	Command	Notes
Docker Compose	docker compose up -d	Web UI on localhost:8080
Helm (K8s)	helm install af helm/alpha-factory	--set env.RUN_MODE=web
AWS Fargate	./infra/deploy_fargate.sh	set container_image & subnets
IoT Edge	python edge_runner.py --agents manufacturing,energy	Jetson Nano

🚀 Deploying securely

See the documentation for TLS setup, API tokens and Vault usage. Mount secrets via Docker or Kubernetes and never commit them.

---

8 · Governance & Compliance ⚖️

• MCP envelopes (SHA‑256, ISO‑8601, policy hash)
• Red‑Team Suite fuzzes prompts & actions
• Attestations — W3C Verifiable Credentials at every Actuator call

---

9 · Observability 🔭

Signal	Sink	Example
Metrics	Prometheus	alpha_pnl_realised_usd
Traces	OpenTelemetry	trace_id
Dashboards	Grafana	alpha-factory/trade-lifecycle.json

Prometheus scrapes metrics from the API server at /metrics.

By default traces and metrics print to stdout. To export to a collector such as Jaeger, set OTEL_EXPORTER_OTLP_ENDPOINT and start Jaeger locally:

docker run -p 16686:16686 -p 4317:4317 jaegertracing/all-in-one

Set OTEL_ENDPOINT to enable anonymous dashboard telemetry. Users are prompted for consent before any metrics are sent.

Telemetry Queue

Anonymous usage metrics are buffered in the browser under the telemetryQueue key in localStorage. Each record includes:

• ts – the timestamp when the entry was recorded.
• session – a deterministic SHA‑256 hash identifying the session.
• generations – how many runs were executed.
• shares – how many times results were shared.

When the browser is online the queue is flushed to OTEL_ENDPOINT using navigator.sendBeacon with a fetch fallback. The queue holds at most 100 entries and is persisted across page loads until sent. No personal data or IP addresses are stored.

Telemetry can be disabled from the Analytics panel by clicking Disable telemetry. Clearing the telemetryConsent and telemetryQueue entries in browser storage also stops all transmissions.

---

10 · Safety & Security 🛡️

The policy runbook outlines sandbox resource limits, timeout behaviour, required human review and rollback steps. Operational tips for the governance module reside in the documentation.

---

11 · Extending the Mesh 🔌

from backend.agents.base import AgentBase

class MySuperAgent(AgentBase):
    NAME = "super"
    CAPABILITIES = ["telemetry_fusion"]
    COMPLIANCE_TAGS = ["gdpr_minimal"]

    async def run_cycle(self):
        ...

# setup.py entrypoint
[project.entry-points."alpha_factory.agents"]
super = my_pkg.super_agent:MySuperAgent

pip install . → orchestrator hot‑loads at next boot.

---

12 · Troubleshooting 🛠️

Symptom	Cause	Fix
ImportError: faiss	FAISS missing	pip install faiss-cpu
Agent quarantined	exceptions	Check logs, clear flag
Kafka refuse	broker down	unset ALPHA_KAFKA_BROKER

---

13 · Roadmap 🛣️

1. RL‑on‑Execution — slippage‑aware order routing
2. Federated Mesh — cross‑org agent exchange via ADK federation
3. World‑Model Audits — interpretable probes of latents
4. Industry Packs — Health‑Care, Gov‑Tech
5. Provable Safety ℙ — Coq proofs for Actuators

---

14 · Credits 🌟

Vincent Boucher—pioneer in AI and President of MONTREAL.AI since 2003—dominated the OpenAI Gym with AI Agents in 2016 and unveiled the seminal “Multi‑Agent AI DAO” in 2017.

Our AGI ALPHA AGENT, fuelled by the strictly‑utility $AGIALPHA token, now taps that foundation to unleash the ultimate α‑signal engine.

15 · License

This project is distributed under the Apache 2.0 license. All community members are expected to follow our Code of Conduct. Please report security issues via the process outlined in our Security Policy.

Release Tweet

🚀 New Alpha-Factory release! Offline dashboard, responsive UI and automated visual tests powered by Percy.

16 · Final Note

Please ensure all usage and contributions align with the project's Apache 2.0 license.

Made with ❤️ by the Alpha‑Factory Agentic Core Team — forging the tools that forge tomorrow.