Out‑learn · Out‑think · Out‑design · Out‑strategise · Out‑execute

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 (<name>.a.agi.eth) that unleashes a swarm of self‑improving agentic α‑AGI agents 👁️✨ (<name>.a.agent.agi.eth) to hunt down inefficiencies across any domain and transmute them into $AGIALPHA.

Official 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.

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.

0. Design Philosophy
1. System Topology 🗺️
2. World‑Model & Planner 🌌
3. Agent Gallery 🖼️ (12 agents)
4. Demo Showcase 🎬 (12 demos)
5. Memory & Knowledge Fabric 🧠
6. 5‑Minute Quick‑Start 🚀
7. Deployment Recipes 🍳
8. Governance & Compliance ⚖️
9. Observability 🔭
10. Extending the Mesh 🔌
11. Troubleshooting 🛠️
12. Roadmap 🛣️
13. Credits 🌟

“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.

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)]

• 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.

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{{"📊"}}

%% 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;

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

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

Paper: Multi-Agent AGENTIC α-AGI World-Model Demo 🥑

┌──────────────────────────────── 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)                    │
└────────────────────────────────────────────────────────────────────────────────────────┘

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;

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

[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

git clone https://github.com/MontrealAI/AGI-Alpha-Agent-v0.git
cd AGI-Alpha-Agent-v0/alpha_factory_v1
pip install -r requirements.txt

export ALPHA_KAFKA_BROKER=localhost:9092
python -m backend.orchestrator
open http://localhost:8000/docs

No GPU → falls back to GGML Llama‑3‑8B‑Q4.
No OPENAI_API_KEY → switches to local SBERT + heuristics.

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

from backend.agent_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.

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

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.

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