HUman notes:

Drone_4 works from seed to simulation for drones! I cheated a little bit in the make_fleet.py to rely less on LLM to create the fleet but I had to remind myself that this was a test of Gem 3.0 reasoning and not making things absolutely perfect to avoid going down a needless rabithole.

Quad has been uploaded. It is an attempt to create quadruped robot from user inference to simulation

End HUman notes

OpenForge: Neuro-Symbolic Manufacturing Engine

Running the entire search and construction loop using async calls:

sim_in_issac.py: Static simulation of quad robot. Simulation is very blocky. This is due to limited run of forge.py

seed.py: Understanding intent, generating model, creating search, and looking for parts:

seed_ecosystem.py: Analyzing Dependancies, creating search terms, and finding results:

refine_arsenal.py: Improving Data Integrity:

make_fleet.py: Generating drones from data that has been collected

Drone Simulated in Isaac Sim Python 5.0.0!!!

---

OpenForge is a research prototype exploring AI-driven design automation. It demonstrates how constraint propagation, multi-modal verification, and deterministic validation can work together to automate hardware engineering tasks.

Traditional AI tools operate on text. OpenForge operates on Physics and Supply Chains. It utilizes a multi-agent architecture to scour the global market for components, verify them using Computer Vision, procedurally generate valid engineering assemblies, and simulate them in a browser-based physics engine.

It transforms Natural Language Intent (e.g., "I need to inspect fences autonomously") into Verified Engineering Reality (BOMs, Blueprints, and Flight Simulations).

🚀 Key Capabilities

1. The Arsenal (Active Supply Chain)

• The Constraint Chain: We don't just search for "motors." An Architect Agent first translates user intent ("Brush Buster") into physics constraints ("Stator Volume > 2306", "Arm Thickness > 5mm").
• The Refinery: A high-agency background worker (refine_arsenal.py) that audits the database. If a spec is missing, it spins up a headless browser, clicks "Specifications" tabs, reads the DOM, and updates the record.
• Visual Forensics: Uses Vision AI to read PCB silkscreens and technical drawings to verify data that text scrapers miss.

2. The Fabricator (Logic-Gated Manufacturing)

• The Class Filter: Instead of random combinations, the engine intelligently buckets parts (e.g., "7-inch Long Range", "Heavy Lift"). It only attempts to build drones with components in the same class.
• The Logic Gate: A deterministic CompatibilityService that enforces hard physics rules before AI generation:
  • Voltage: Checks Battery Cells vs Motor KV (Prevents fires).
  • Protocol: Checks UART Count vs Peripherals (Prevents I/O bottlenecks).
  • Geometry: Checks Prop Size vs Frame Clearance (Prevents collisions).

3. The Virtual Prototyping Lab (Simulation)

• Browser-Based Game Engine: A fully interactive 3D simulator running on Three.js and Cannon.js.
• Real Physics: Flight characteristics (inertia, drag, thrust) are derived from the actual mass and motor curves of the sourced parts.
• Procedural Environment: The sim generates context-aware environments (Trees, Cows, Buildings) to test the drone's intended use case.

🛠️ Architecture

OpenForge separates Data Acquisition, Product Generation, and User Interaction into distinct layers.

graph TD
    %% --- STYLING ---
    classDef agent fill:#1a202c,stroke:#00ff88,stroke-width:2px,color:#fff,rx:5,ry:5;
    classDef data fill:#2d3748,stroke:#F6E05E,stroke-width:2px,color:#fff,shape:cylinder;
    classDef logic fill:#1a202c,stroke:#FF5555,stroke-width:2px,color:#fff,shape:rhombus;
    classDef output fill:#1a202c,stroke:#3182ce,stroke-width:2px,color:#fff;
    classDef cluster fill:#111,stroke:#444,stroke-width:1px,color:#ccc;

    %% --- LAYER 1 ---
    subgraph L1 [Layer 1: The Active Supply Chain]
        direction TB
        Seed[Seed Agent]:::agent -->|Broad Search| Raw[Raw Inventory]:::data
        Raw -->|Audit Missing Specs| Refinery[Refinery Agent]:::agent
        Refinery -->|Agentic Browsing / Clicks| Arsenal[(Verified Arsenal)]:::data
    end

    %% --- LAYER 2 ---
    subgraph L2 [Layer 2: The Factory]
        direction TB
        Arsenal --> Fabricator[Fabricator Script]:::agent
        Fabricator -->|Classify & Bucket| Gate{Logic Gate}:::logic
        Gate -- Physics Fail --> Discard[Discard]:::output
        Gate -- Pass --> Builder[AI Assembler]:::agent
        Builder -->|Mint SKU| Catalog[(Catalog JSON)]:::data
    end

    %% --- LAYER 3 ---
    subgraph L3 [Layer 3: The Interface]
        direction TB
        User((User)):::output -->|Natural Language| Architect[Architect Agent]:::agent
        Architect <-->|Query Matches| Catalog
        Architect -->|Select Anchor| Twin[Digital Twin Generator]:::output
        
        Twin --> Sim[Three.js Flight Sim]:::output
        Twin --> Docs[Assembly Guide]:::output
    end

    %% --- CONNECTIONS BETWEEN LAYERS ---
    %% (Implicitly handled by node placement, but enforced here for clarity)
    style L1 fill:#0d1117,stroke:#333,stroke-width:2px
    style L2 fill:#0d1117,stroke:#333,stroke-width:2px
    style L3 fill:#0d1117,stroke:#333,stroke-width:2px

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📦 Installation

Prerequisites

• Python 3.10+
• Playwright: Required for the active refinery agent.
• Google Gemini API Key: (Visual Reasoning & Logic)
• Google Custom Search API Key: (Web Recon)

Setup

1. Clone the repository:

   git clone https://github.com/your-username/openforge.git
   cd openforge

2. Install dependencies:

   pip install -r requirements.txt
   playwright install chromium

3. Configuration: Set your API keys in app/config.py or environment variables.

🖥️ Usage

1. Seed & Refine (The Data Loop)

Populate your local database and let the AI "fix" missing data.

# Step 1: Broad search based on constraints
python tools/seed_arsenal.py

# Step 2: Active investigation (Clicks tabs, reads screenshots)
python tools/refine_arsenal.py

# Step 3: Grade your data quality
python tools/inspect_data_quality.py

2. Design the Fleet (The Logic Loop)

Ask the AI to select optimal combinations from your refined inventory.

python tools/design_fleet.py

3. Fly (The Simulation)

Launch the physics engine.

python tools/fly_drone.py

🗺️ Roadmap

Feature	Status	Description
Constraint Solving	🟢 Done	Translates "intent" to "physics" before sourcing.
Active Refinery	🟢 Done	Agentic browsing to click UI elements and find hidden specs.
Physics Logic Gate	🟢 Done	Deterministic code prevents AI from building unflyable drones.
3D Simulator	🟢 Done	Procedural "Game Engine" based on real component specs.
Feedback Loop	🟡 In Progress	If a build fails, auto-trigger the Sourcer to find the missing part.
Cost Optimization	🔴 Planned	Genetic algorithm to find the cheapest valid configuration.

📄 License

This project is licensed under the AGPL v3.