Packages Overview¶

OpenAdapt v1.0+ uses a modular meta-package architecture. The main openadapt package provides a unified CLI and depends on focused sub-packages via PyPI.

Architecture¶

graph TD
    OA[openadapt<br/>Meta-package]

    OA -->|capture| CAP[openadapt-capture]
    OA -->|ml| MLP[openadapt-ml]
    OA -->|evals| EVL[openadapt-evals]
    OA -->|viewer| VWR[openadapt-viewer]
    OA -->|grounding| GRD[openadapt-grounding]
    OA -->|retrieval| RET[openadapt-retrieval]
    OA -->|privacy| PRV[openadapt-privacy]

    OA -->|core| CORE[Core Bundle]
    CORE --> CAP
    CORE --> MLP
    CORE --> EVL
    CORE --> VWR

    OA -->|all| ALL[Full Bundle]
    ALL --> CORE
    ALL --> GRD
    ALL --> RET
    ALL --> PRV

    classDef meta fill:#2C3E50,stroke:#1A252F,color:#fff
    classDef core fill:#27AE60,stroke:#1E8449,color:#fff
    classDef optional fill:#E67E22,stroke:#A04000,color:#fff
    classDef bundle fill:#8E44AD,stroke:#5B2C6F,color:#fff

    class OA meta
    class CAP,MLP,EVL,VWR core
    class GRD,RET,PRV optional
    class CORE,ALL bundle

Core Packages¶

These packages provide the essential functionality for recording, training, evaluating, and visualizing.

Install all core packages:

pip install openadapt[core]

Optional Packages¶

These packages provide enhanced functionality for specific use cases.

Install all packages:

pip install openadapt[all]

Installation Options¶

Individual Packages¶

pip install openadapt[capture]     # GUI capture/recording
pip install openadapt[ml]          # ML training and inference
pip install openadapt[evals]       # Benchmark evaluation
pip install openadapt[viewer]      # HTML visualization
pip install openadapt[grounding]   # UI element localization
pip install openadapt[retrieval]   # Demo search/retrieval
pip install openadapt[privacy]     # PII/PHI scrubbing

Multiple Packages¶

pip install openadapt[capture,ml,evals]

Bundles¶

pip install openadapt[core]        # capture + ml + evals + viewer
pip install openadapt[all]         # Everything

Data Flow¶

flowchart LR
    subgraph Record["1. Record"]
        A[User Demo] --> B[Capture Session]
        B --> C[Screenshots + Events]
    end

    subgraph Store["2. Store"]
        C --> D[JSON/Parquet Files]
        D --> E[Demo Library]
    end

    subgraph Train["3. Train"]
        E --> F[Data Loading]
        F --> G[Model Training]
        G --> H[Checkpoint]
    end

    subgraph Deploy["4. Deploy"]
        H --> I[Agent Policy]
        I --> J[Inference]
        J --> K[Action Replay]
    end

    subgraph Evaluate["5. Evaluate"]
        I --> L[Benchmark Runner]
        L --> M[Metrics]
        M --> N[Results Report]
    end

    GROUND[Grounding] -.-> J
    RETRIEVE[Retrieval] -.-> F
    PRIV[Privacy] -.-> C

Package Repositories¶

Each package is maintained in its own repository:

Contributing¶

To contribute to a specific package:

1. Fork and clone the package repository
2. Install in development mode: pip install -e ".[dev]"
3. Make your changes
4. Submit a pull request

See Contributing for more details.