OpenAdapt Architecture Evolution
January 17, 2026 ยท View on GitHub
Version: 3.0 Date: January 2026 Status: Living Document
Executive Summary
This document traces the evolution of OpenAdapt from its original alpha vision through the modern modular implementation, synthesizing state-of-the-art GUI agent research into a unified framework. OpenAdapt's core innovation is demonstration-conditioned automation: "show, don't tell."
Table of Contents
- Original Alpha Vision
- The Abstraction Ladder
- Core Innovation: Demo-Conditioned Agents
- Modern Architecture
- SOTA GUI Agent Integration
- Package Responsibilities
- Feedback Loops
- Implementation Status
- Architecture Evolution Diagrams
- Future Directions
1. Original Alpha Vision
The Three-Stage Pipeline (2023)
OpenAdapt was conceived as a three-stage pipeline for AI-first process automation:
+=====================+ +=====================+ +=====================+
| | | | | |
| RECORDING | --> | ANALYSIS | --> | REPLAY |
| | | | | |
| Capture human | | Convert to | | Generate and |
| demonstrations: | | tokenized format | | replay synthetic |
| - Screenshots | | for LMM | | input via model |
| - User input | | processing | | completions |
| | | | | |
+=====================+ +=====================+ +=====================+
Original Design Goals
From the legacy README:
"The goal is similar to that of Robotic Process Automation (RPA), except that we use Large Multimodal Models instead of conventional RPA tools."
Key Differentiators (Alpha):
- Model Agnostic - Works with any LMM
- Auto-Prompted - Learns from demonstration, not user prompts
- Grounded in Existing Processes - Mitigates hallucinations
- Universal GUI Support - Desktop, web, and virtualized (Citrix)
- Open Source - MIT license
Legacy Monolithic Implementation
The alpha codebase (legacy/openadapt/) implemented:
openadapt/
record.py # Screenshot/event capture
replay.py # Strategy-based playback
models.py # Recording, ActionEvent, Screenshot, WindowEvent
events.py # Event aggregation/processing
strategies/
base.py # BaseReplayStrategy abstract class
naive.py # Direct literal replay
stateful.py # GPT-4 + OS-level window data
vanilla.py # Full VLM reasoning per step
visual.py # FastSAM segmentation
visual_browser.py # DOM-based segments
adapters/
anthropic.py # Claude API integration
openai.py # GPT API integration
replicate.py # Open-source model hosting
privacy/
base.py # Scrubbing provider interface
providers/ # Presidio, AWS Comprehend, Private AI
The Strategy Pattern (Original)
The original architecture used a BaseReplayStrategy abstract class:
class BaseReplayStrategy(ABC):
"""Base class for implementing replay strategies."""
def __init__(self, recording: Recording) -> None:
self.recording = recording
self.action_events = []
self.screenshots = []
self.window_events = []
@abstractmethod
def get_next_action_event(
self,
screenshot: Screenshot,
window_event: WindowEvent,
) -> ActionEvent:
"""Get the next action based on current observation."""
pass
def run(self) -> None:
"""Execute the replay loop."""
while True:
screenshot = Screenshot.take_screenshot()
window_event = WindowEvent.get_active_window_event()
action_event = self.get_next_action_event(screenshot, window_event)
if action_event:
playback.play_action_event(action_event, ...)
This pattern evolved into the modern policy/grounding separation.
Alpha Data Model
class Recording:
"""Container for a demonstration session."""
id: int
timestamp: float
task_description: str
action_events: list[ActionEvent]
screenshots: list[Screenshot]
window_events: list[WindowEvent]
class ActionEvent:
"""A single user action (click, type, scroll, etc.)."""
name: str # "click", "type", "scroll", "press", "release"
timestamp: float
screenshot: Screenshot # Screenshot just before action
window_event: WindowEvent # Active window state
mouse_x, mouse_y: int # Mouse coordinates
key_char, key_name: str # Keyboard input
element_state: dict # Accessibility info
class Screenshot:
"""A captured screen image."""
timestamp: float
png_data: bytes
image: PIL.Image
2. The Abstraction Ladder
Core Concept: Progressive Abstraction
OpenAdapt processes demonstrations through ascending levels of abstraction, enabling generalization and transfer learning.
+=========================================================================+
| |
| Level 4: GOAL (Task Specification) FUTURE |
| "Say hello to the customer" |
| |
| ^ |
| | Goal Composition (high-level planning) |
| | |
+=========================================================================+
| |
| Level 3: SEMANTIC (Intent Recognition) FUTURE |
| { action: "greet", target: "user" } |
| |
| ^ |
| | Process Mining (discover patterns) |
| | |
+=========================================================================+
| |
| Level 2: TEMPLATE (Parameterized Actions) PARTIAL |
| { type: "hi <firstname>" } |
| |
| ^ |
| | Anonymization (extract parameters) |
| | |
+=========================================================================+
| |
| Level 1: SYMBOLIC (Semantic Actions) IMPLEMENTED |
| { type: "hi bob" } |
| |
| ^ |
| | Reduction (aggregate consecutive events) |
| | |
+=========================================================================+
| |
| Level 0: LITERAL (Raw Events) IMPLEMENTED |
| { press: "h" }, { press: "i" }, { press: " " }, { press: "b" }, ... |
| |
+=========================================================================+
Abstraction Level Details
| Level | Name | Representation | Transformation | Status |
|---|---|---|---|---|
| 0 | Literal | Raw keypresses, mouse coords | None (raw capture) | Implemented |
| 1 | Symbolic | Aggregated actions (type "hello") | Event reduction | Implemented |
| 2 | Template | Parameterized (type "<greeting>") | Regex extraction | Partial |
| 3 | Semantic | Intent-level (greet user) | LLM intent recognition | Research |
| 4 | Goal | Task description ("Welcome customer") | Goal composition | Future |
Why Abstraction Matters
| Level | Enables | Example Use Case |
|---|---|---|
| Literal | Exact replay, debugging | Audit trails, regression tests |
| Symbolic | Human-readable logs | Training data visualization |
| Template | Parameterized replay | Same task, different data |
| Semantic | Cross-application transfer | Greeting in any messaging app |
| Goal | Natural language control | "Greet the next customer" |
Current Implementation
Literal to Symbolic (openadapt-capture):
- Event aggregation in
events.py - Consecutive keypresses become
typeactions - Mouse drags become
dragactions - Click sequences become
doubleclickortripleclick
Symbolic to Template (Partial):
- Regex-based parameter extraction
- User-defined placeholders
Template to Semantic (Research):
- LLM-based intent recognition
- Pattern library discovery
Semantic to Goal (Future):
- Process mining algorithms
- Cross-demo pattern extraction
3. Core Innovation: Demo-Conditioned Agents
The Fundamental Differentiator
OpenAdapt's core insight is demonstration-conditioned automation: "show, don't tell."
+-------------------------------------------------------------------+
| TRADITIONAL APPROACH |
+-------------------------------------------------------------------+
| |
| User: "Click the submit button" |
| |
| Agent: [Which submit button? What context? What state?] |
| [Multiple submit buttons on page?] |
| [Different applications have different buttons] |
| |
| Result: AMBIGUOUS -> Requires prompt engineering |
| |
+-------------------------------------------------------------------+
+-------------------------------------------------------------------+
| DEMO-CONDITIONED APPROACH |
+-------------------------------------------------------------------+
| |
| User: [Records clicking the blue "Submit Order" button |
| after filling out form fields] |
| |
| Agent: [Learns full context: |
| - Form state before action |
| - Button appearance and location |
| - Preceding actions in sequence |
| - Window/application context] |
| |
| Result: GROUNDED -> No prompt engineering needed |
| |
+-------------------------------------------------------------------+
Why Demo-Conditioning Works
- Captures Implicit Knowledge: Users demonstrate things they can't easily verbalize
- Grounded in Reality: Actions tied to actual UI states, not abstract descriptions
- Reduces Ambiguity: Visual context eliminates interpretation errors
- Lower Barrier: No prompt engineering skills required
Empirical Results
Demo conditioning improves first-action accuracy:
| Approach | First-Action Accuracy | Notes |
|---|---|---|
| Prompt-only | ~33% | Ambiguity in action selection |
| Demo-conditioned | ~100% | Full context from demonstration |
The "Show, Don't Tell" Principle
# Traditional: Prompt-driven
agent.execute("Click the submit button")
# -> Which submit button? What state? What context?
# Demo-Conditioned: Demonstration-driven
demo = capture_demonstration() # User clicks specific submit button
agent = train_policy(demo) # Agent learns the full context
agent.execute(new_context) # Agent adapts to variations
4. Modern Architecture
Evolution: Monolith to Meta-Package
ALPHA (2023-2024) MODERN (2025+)
+====================+ +====================+
| | | openadapt |
| openadapt | | (meta-pkg) |
| (monolithic) | +=========+=========+
| | |
| - record.py | +-----------------+-----------------+
| - replay.py | | | | | |
| - strategies/ | +----+----+ +--+--+ +--+--+ +--+--+ +----+----+
| - models.py | |capture | | ml | |evals| |viewer| |optional |
| - adapters/ | +---------+ +-----+ +-----+ +------+ +---------+
| - privacy/ |
| - visualize.py | + grounding, retrieval, privacy
| |
+====================+
The Modern Three-Phase Architecture
Building on the alpha vision, the modern architecture formalizes three phases:
+=======================+ +=======================+ +=======================+
|| || || || || ||
|| DEMONSTRATE || --> || LEARN || --> || EXECUTE ||
|| || || || || ||
|| (Observation || || (Policy || || (Agent ||
|| Collection) || || Acquisition) || || Deployment) ||
|| || || || || ||
|| Packages: || || Packages: || || Packages: ||
|| - capture || || - ml || || - evals ||
|| - privacy || || - retrieval || || - grounding ||
|| || || || || ||
+=======================+ +=======================+ +=======================+
Phase 1: DEMONSTRATE (Observation Collection)
Purpose: Capture rich trajectories from human demonstrations.
Inputs:
- User performs task in target application
- Optional: Task description, success criteria, audio narration
Outputs:
- Screenshot sequences (PNG/JPEG)
- Input events (mouse, keyboard, touch)
- Accessibility tree snapshots (a11y)
- Window metadata (title, bounds, process)
- Audio transcription (optional)
Packages: openadapt-capture, openadapt-privacy
Phase 2: LEARN (Policy Acquisition)
Purpose: Transform demonstrations into executable agent policies.
Three Learning Paths:
| Path | Mechanism | Advantage | Package |
|---|---|---|---|
| A: Retrieval-Augmented | Index demos, retrieve similar | No training needed | openadapt-retrieval |
| B: Fine-Tuning | Train VLM on demo dataset | Specialized performance | openadapt-ml |
| C: Process Mining | Extract reusable patterns | Cross-task transfer | openadapt-ml (future) |
Phase 3: EXECUTE (Agent Deployment)
Purpose: Run trained/conditioned agents autonomously.
Execution Loop:
while not task_complete:
1. OBSERVE - Capture screenshot + a11y tree
2. GROUND - Localize UI elements (SoM, OmniParser)
3. PLAN - VLM reasoning with demo context
4. ACT - Execute via input synthesis
5. EVALUATE - Check success, decide next step
Packages: openadapt-evals, openadapt-grounding, openadapt-ml
5. SOTA GUI Agent Integration
Policy/Grounding Separation
From Claude Computer Use, UFO, and SeeAct research:
+====================+ +====================+
| | | |
| POLICY | --> | GROUNDING |
| | | |
| "What to do" | | "Where to do" |
| | | |
| - Observation | | - Element |
| encoding | | detection |
| - Action | | - Coordinate |
| selection | | mapping |
| - History | | - Bounding |
| context | | boxes |
| | | |
+====================+ +====================+
OpenAdapt Implementation:
- Policy:
openadapt-mladapters (Claude, GPT-4V, Qwen-VL) - Grounding:
openadapt-groundingproviders (OmniParser, Florence2, Gemini)
Set-of-Mark (SoM) Prompting
From Microsoft's Set-of-Mark paper:
Original Screenshot SoM-Annotated Screenshot
+---------------------+ +---------------------+
| [Login] [Help] | | [1] [2] |
| | -> | |
| Email: [________] | | Email: [3] |
| Pass: [________] | | Pass: [4] |
| [Submit] | | [5] |
+---------------------+ +---------------------+
Prompt: "Enter email in element [3], password in [4], click [5]"
OpenAdapt Implementation: openadapt-grounding.SoMPrompt
Safety Gates
From responsible AI patterns:
+------------------+ +------------------+ +------------------+
| | | | | |
| OBSERVE | --> | VALIDATE | --> | ACT |
| | | | | |
| Get current | | - Check bounds | | Execute if |
| state | | - Verify perms | | validated |
| | | - Rate limit | | |
+------------------+ +--------+---------+ +------------------+
|
v (rejected)
+------------------+
| ESCALATE |
| Human review |
+------------------+
Status: Planned in openadapt-evals safety module.
Research Alignment
| Research Paper | Key Contribution | OpenAdapt Integration |
|---|---|---|
| Claude Computer Use (Anthropic, 2024) | Production VLM agent API | API adapter in openadapt-ml |
| UFO (Microsoft, 2024) | Windows agent architecture | Prompt patterns adopted |
| OSWorld (CMU, 2024) | Cross-platform benchmark | Benchmark adapter planned |
| Set-of-Mark (Microsoft, 2023) | Visual grounding via labels | Core grounding mode |
| OmniParser (Microsoft, 2024) | Pure-vision UI parsing | Provider in openadapt-grounding |
| SeeAct (OSU, 2024) | Grounded action generation | Action space design |
| WebArena (CMU, 2023) | Web automation benchmark | Benchmark adapter implemented |
| AppAgent (Tencent, 2024) | Mobile GUI agent | Mobile support planned |
6. Package Responsibilities
Package-to-Phase Mapping
+===============================================================================+
| DEMONSTRATE PHASE |
+===============================================================================+
| Package | Responsibility | Key Exports |
+-------------------+----------------------------+------------------------------+
| openadapt-capture | GUI recording, storage | Recorder, CaptureSession |
| | | Action, Screenshot, Trajectory|
+-------------------+----------------------------+------------------------------+
| openadapt-privacy | PII/PHI scrubbing | Scrubber, Redactor |
| | (integrates at capture) | PrivacyFilter |
+===============================================================================+
+===============================================================================+
| LEARN PHASE |
+===============================================================================+
| Package | Responsibility | Key Exports |
+---------------------+--------------------------+------------------------------+
| openadapt-ml | Model training, | Trainer, AgentPolicy |
| | inference, adapters | QwenVLAdapter, ClaudeAdapter |
+---------------------+--------------------------+------------------------------+
| openadapt-retrieval | Demo embedding, | DemoIndex, Embedder |
| | similarity search | SearchResult |
+===============================================================================+
+===============================================================================+
| EXECUTE PHASE |
+===============================================================================+
| Package | Responsibility | Key Exports |
+----------------------+-------------------------+------------------------------+
| openadapt-evals | Benchmark evaluation, | BenchmarkAdapter, ApiAgent |
| | metrics collection | evaluate_agent_on_benchmark |
+----------------------+-------------------------+------------------------------+
| openadapt-grounding | UI element detection, | ElementDetector, SoMPrompt |
| | coordinate mapping | OmniParser, GeminiGrounder |
+===============================================================================+
+===============================================================================+
| CROSS-CUTTING |
+===============================================================================+
| Package | Responsibility | Key Exports |
+-------------------+----------------------------+------------------------------+
| openadapt-viewer | HTML visualization, | PageBuilder, HTMLBuilder |
| | trajectory replay | TrajectoryViewer |
+-------------------+----------------------------+------------------------------+
| openadapt | Unified CLI, | cli.main, lazy imports |
| (meta-package) | dependency coordination | |
+===============================================================================+
Package Dependency Matrix
capture ml evals viewer grounding retrieval privacy
openadapt-capture - - - - - - O
openadapt-ml R - - - O O -
openadapt-evals - R - O O O -
openadapt-viewer O O O - - - O
openadapt-grounding - - - - - - -
openadapt-retrieval R - - - - - -
openadapt-privacy - - - - - - -
Legend: R = Required, O = Optional, - = None
7. Feedback Loops
System-Level Feedback Architecture
DEMONSTRATE
|
| Human demonstrations
v
+-----------------------------> LEARN <----------------------------+
| | |
| | Trained policies |
| +-----------------------------|---------------------+ |
| | v | |
| | +-----------------> EXECUTE <--------------+ | |
| | | | | | |
| | | Retry on | Success/Failure | | |
| | | recoverable | outcomes | | |
| | | errors v | | |
| | | +-------+-------+ | | |
| | | | | | | |
| | +---------------+ EVALUATE +-----------+ | |
| | (Loop 1: Retry) | | | |
| | +-------+-------+ | |
| | | | |
| | | Execution traces | |
| | v | |
| | Demo library grows | |
| | | | |
| +---------------------------+ | |
| (Loop 2: Library Growth) | |
| | |
| Failure analysis identifies gaps | |
| | | |
| v | |
| Human correction | |
| | | |
+--------------------+ | |
(Loop 3: Human-in-Loop) | |
| |
Self-improvement loop | |
(execution traces -> training) | |
| | |
+------------------------+ |
(Loop 4: Self-Improvement) |
|
Benchmark-driven development |
(eval results -> architecture improvements) |
| |
+-----------------------------------+
(Loop 5: Benchmark-Driven)
Feedback Loop Details
| Loop | Name | Trigger | Outcome | Status |
|---|---|---|---|---|
| 1 | Retry | Recoverable error | Re-attempt action | Implemented |
| 2 | Library Growth | Successful execution | New demo added | Implemented |
| 3 | Human-in-Loop | Unrecoverable failure | Human correction -> demo | Implemented |
| 4 | Self-Improvement | Execution traces | Fine-tuning | Research |
| 5 | Benchmark-Driven | Eval metrics | Architecture changes | Active |
8. Implementation Status
What's Implemented vs Future Work
+==============================================================================+
| IMPLEMENTED (Solid) |
+==============================================================================+
| Component | Package | Notes |
+--------------------------+------------------+--------------------------------+
| Screen capture | capture | macOS, Windows, Linux |
| Event recording | capture | Mouse, keyboard, touch |
| Event aggregation | capture | Literal -> Symbolic |
| A11y tree capture | capture | macOS, Windows |
| Demo storage | capture | JSON/Parquet/PNG |
| Privacy scrubbing | privacy | Presidio, AWS Comprehend |
| Demo embedding | retrieval | CLIP, SigLIP |
| Vector indexing | retrieval | FAISS, Annoy |
| Similarity search | retrieval | Top-k retrieval |
| API model adapters | ml | Claude, GPT-4V, Gemini |
| Element detection | grounding | OmniParser, Florence2 |
| SoM annotation | grounding | Numbered element labels |
| WAA benchmark | evals | Full integration |
| Mock benchmark | evals | Testing infrastructure |
| HTML visualization | viewer | Trajectory replay |
| Unified CLI | openadapt | capture/train/eval/view |
+==============================================================================+
+==============================================================================+
| IN PROGRESS (Dashed) |
+==============================================================================+
| Component | Package | Notes |
+--------------------------+------------------+--------------------------------+
| Training pipeline | ml | Qwen-VL fine-tuning |
| LoRA adapters | ml | Parameter-efficient training |
| Template extraction | capture | Regex-based parameterization |
| WebArena benchmark | evals | Browser automation |
| Training dashboard | viewer | Loss/metrics visualization |
| Audio transcription | capture | Whisper integration |
+--------------------------+------------------+--------------------------------+
+==============================================================================+
| FUTURE WORK (Dotted) |
+==============================================================================+
| Component | Package | Notes |
+--------------------------+------------------+--------------------------------+
| Process mining | ml (future) | Semantic action discovery |
| Goal composition | ml (future) | High-level task planning |
| Self-improvement | ml (future) | Training on execution traces |
| OSWorld benchmark | evals | Cross-platform desktop |
| Multi-agent collaboration| ml (future) | Agent coordination |
| Active learning | ml (future) | Human feedback integration |
| Mobile platform | capture | iOS, Android |
| Safety gates | evals | Action validation layer |
+==============================================================================+
Abstraction Ladder Implementation Status
| Level | Name | Status | Implementation |
|---|---|---|---|
| 0 | Literal | Implemented | Raw event recording in capture |
| 1 | Symbolic | Implemented | Event aggregation in capture |
| 2 | Template | Partial | Regex extraction in capture |
| 3 | Semantic | Research | LLM intent recognition |
| 4 | Goal | Future | Process mining |
9. Architecture Evolution Diagrams
Era 1: Alpha Monolith (2023)
+=========================================================================+
| ALPHA ARCHITECTURE (2023) |
+=========================================================================+
| |
| +------------------------------------------------------------------+ |
| | openadapt (monolithic) | |
| +------------------------------------------------------------------+ |
| | | |
| | +-------------+ +-------------+ +-------------+ | |
| | | record | -> | visualize | -> | replay | | |
| | +-------------+ +-------------+ +-------------+ | |
| | | | | | |
| | v v v | |
| | +-------------+ +-------------+ +------------------+ | |
| | | models | | plotting | | strategies/ | | |
| | | - Recording | | - HTML gen | | - base.py | | |
| | | - ActionEvt | | | | - naive.py | | |
| | | - Screenshot| | | | - vanilla.py | | |
| | | - WindowEvt | | | | - visual.py | | |
| | +-------------+ +-------------+ +------------------+ | |
| | | | | |
| | v v | |
| | +-------------+ +---------------+ | |
| | | db/ | | adapters/ | | |
| | | - SQLite | | - anthropic | | |
| | | - CRUD ops | | - openai | | |
| | +-------------+ | - replicate | | |
| | +---------------+ | |
| +------------------------------------------------------------------+ |
| |
+=========================================================================+
Characteristics:
- Single repository, single package
- Tightly coupled components
- Strategy pattern for replay variants
- SQLite + Alembic migrations
- Prompts embedded in code
Era 2: Transition (2024)
+=========================================================================+
| TRANSITION ARCHITECTURE (2024) |
+=========================================================================+
| |
| Legacy codebase frozen -> /legacy/ |
| |
| New modular packages designed: |
| |
| +-------------+ +-------------+ +-------------+ +-------------+ |
| | capture | | ml | | evals | | viewer | |
| +-------------+ +-------------+ +-------------+ +-------------+ |
| | privacy | | retrieval | | grounding | |
| +-------------+ +-------------+ +-------------+ |
| |
| Key changes: |
| - Separate PyPI packages |
| - Lazy imports for optional deps |
| - Unified CLI in meta-package |
| - Policy/grounding separation |
| - Benchmark-first development |
| |
+=========================================================================+
Era 3: Modern Meta-Package (2025+)
+=========================================================================+
| MODERN ARCHITECTURE (2025+) |
+=========================================================================+
| |
| +------------------+ |
| | User Layer | |
| | CLI / Web UI | |
| +--------+---------+ |
| | |
| v |
| +------------------+ |
| | openadapt | |
| | (meta-package) | |
| +--------+---------+ |
| | |
| +------------------------+------------------------+ |
| | | | | | |
| v v v v v |
| +---------+ +---------+ +---------+ +---------+ +--------+ |
| | capture | | ml | | evals | | viewer | |optional| |
| +---------+ +---------+ +---------+ +---------+ +--------+ |
| | | | | | |
| v v v v v |
| +---------------------------------------------------------------+ |
| | Shared Interfaces | |
| | - Trajectory format (JSON/Parquet) | |
| | - Action space specification | |
| | - Observation schema | |
| | - Benchmark protocols | |
| +---------------------------------------------------------------+ |
| | |
| v |
| +---------------------------------------------------------------+ |
| | Model Layer | |
| | +----------+ +----------+ +----------+ +----------+ | |
| | | Claude | | GPT-4V | | Gemini | | Qwen-VL | | |
| | +----------+ +----------+ +----------+ +----------+ | |
| +---------------------------------------------------------------+ |
| |
+=========================================================================+
Full System Architecture (Mermaid)
flowchart TB
subgraph User["User Layer"]
CLI[openadapt CLI]
UI[Desktop/Web GUI]
end
subgraph Phase1["DEMONSTRATE"]
direction TB
REC[Recorder<br/>openadapt-capture]
SCRUB[Privacy Scrubber<br/>openadapt-privacy]
STORE[(Demo Library)]
REC --> SCRUB
SCRUB --> STORE
end
subgraph Phase2["LEARN"]
direction TB
subgraph Retrieval["Retrieval Path"]
EMB[Embedder]
IDX[Vector Index]
SEARCH[Similarity Search]
end
subgraph Training["Training Path"]
LOADER[Data Loader]
TRAINER[Model Trainer]
CKPT[(Checkpoints)]
end
subgraph Mining["Process Mining"]
ABSTRACT[Abstractor]
PATTERNS[Pattern Library]
end
end
subgraph Phase3["EXECUTE"]
direction TB
OBS[1. OBSERVE]
GROUND[2. GROUND<br/>openadapt-grounding]
PLAN[3. PLAN<br/>Demo-Conditioned]
ACT[4. ACT]
EVAL[5. EVALUATE<br/>openadapt-evals]
OBS --> GROUND
GROUND --> PLAN
PLAN --> ACT
ACT --> EVAL
EVAL -->|retry| OBS
end
subgraph Models["Model Layer"]
direction LR
CLAUDE[Claude]
GPT[GPT-4o]
GEMINI[Gemini]
QWEN[Qwen-VL]
end
subgraph Viewer["Cross-Cutting: Viewer"]
VIZ[Visualization]
REPLAY[Replay]
DASH[Dashboard]
end
%% User interactions
CLI --> REC
UI --> REC
CLI --> TRAINER
CLI --> EVAL
%% Demo flow
STORE --> EMB
STORE --> LOADER
STORE --> ABSTRACT
EMB --> IDX
IDX --> SEARCH
LOADER --> TRAINER
TRAINER --> CKPT
ABSTRACT --> PATTERNS
%% Execution flow (demo-conditioning)
SEARCH -->|demo context| PLAN
CKPT -->|policy| PLAN
PATTERNS -.->|templates| PLAN
%% Model connections
PLAN --> Models
GROUND --> Models
%% Viewer connections
STORE -.-> VIZ
CKPT -.-> DASH
EVAL -.-> DASH
%% Feedback loops
EVAL -->|success trace| STORE
EVAL -->|failure| User
%% Styling
classDef userLayer fill:#E74C3C,stroke:#A93226,color:#fff
classDef phase1 fill:#3498DB,stroke:#1A5276,color:#fff
classDef phase2 fill:#27AE60,stroke:#1E8449,color:#fff
classDef phase3 fill:#9B59B6,stroke:#6C3483,color:#fff
classDef models fill:#F39C12,stroke:#B7950B,color:#fff
classDef viewer fill:#1ABC9C,stroke:#148F77,color:#fff
classDef future fill:#95A5A6,stroke:#707B7C,color:#fff,stroke-dasharray: 5 5
class CLI,UI userLayer
class REC,SCRUB,STORE phase1
class EMB,IDX,SEARCH,LOADER,TRAINER,CKPT phase2
class ABSTRACT,PATTERNS future
class OBS,GROUND,PLAN,ACT,EVAL phase3
class CLAUDE,GPT,GEMINI,QWEN models
class VIZ,REPLAY,DASH viewer
Execution Loop Evolution
ALPHA: Strategy-Based MODERN: Policy/Grounding
================================ ================================
+------------------+ +------------------+
| BaseReplay | | OBSERVE |
| Strategy | | (Screenshot + |
| | | A11y tree) |
| while True: | +--------+---------+
| screenshot = | |
| take() | v
| action = | +------------------+
| get_next() | ------> | GROUND |
| play(action) | | (Element detect |
| | | + SoM annotate)|
+------------------+ +--------+---------+
|
v
+------------------+
| PLAN |
| (VLM reasoning |
| + demo context)|
+--------+---------+
|
v
+------------------+
| ACT |
| (Input synth + |
| safety check) |
+--------+---------+
|
v
+------------------+
| EVALUATE |
| (Success check |
| + feedback) |
+------------------+
Package Responsibility Diagram
flowchart LR
subgraph demonstrate["DEMONSTRATE Phase"]
CAP[openadapt-capture]
PRV[openadapt-privacy]
end
subgraph learn["LEARN Phase"]
RET[openadapt-retrieval]
ML[openadapt-ml]
end
subgraph execute["EXECUTE Phase"]
GRD[openadapt-grounding]
EVL[openadapt-evals]
end
subgraph crosscut["Cross-Cutting"]
VWR[openadapt-viewer]
end
subgraph meta["Meta-Package"]
OA[openadapt CLI]
end
%% CLI orchestrates all
OA --> CAP
OA --> ML
OA --> EVL
OA --> VWR
%% Phase 1 flow
CAP -->|raw data| PRV
PRV -->|scrubbed data| ML
PRV -->|scrubbed data| RET
%% Phase 2 flow
RET -->|embeddings| EVL
ML -->|checkpoints| EVL
%% Phase 3 flow
GRD -->|coordinates| EVL
GRD -->|coordinates| ML
%% Viewer integration
CAP -.->|demos| VWR
ML -.->|training| VWR
EVL -.->|results| VWR
%% Styling
classDef phase1 fill:#3498DB,stroke:#1A5276,color:#fff
classDef phase2 fill:#27AE60,stroke:#1E8449,color:#fff
classDef phase3 fill:#9B59B6,stroke:#6C3483,color:#fff
classDef cross fill:#1ABC9C,stroke:#148F77,color:#fff
classDef meta fill:#2C3E50,stroke:#1A252F,color:#fff
class CAP,PRV phase1
class RET,ML phase2
class GRD,EVL phase3
class VWR cross
class OA meta
Feedback Loop Diagram
flowchart TB
subgraph Loop1["Loop 1: Demo Library Growth"]
D1[Human Demo] --> L1[Learn]
L1 --> E1[Execute]
E1 --> |success| S1[Store as Demo]
E1 --> |failure| F1[Failure Analysis]
F1 --> |new demo| D1
S1 --> L1
end
subgraph Loop2["Loop 2: Self-Improvement"]
E2[Execute] --> T2[Trace]
T2 --> |success trace| FT2[Fine-tune]
FT2 --> E2
end
subgraph Loop3["Loop 3: Benchmark-Driven"]
B3[Benchmark] --> M3[Metrics]
M3 --> A3[Architecture Improvement]
A3 --> B3
end
Loop1 -.->|traces| Loop2
Loop2 -.->|models| Loop1
Loop1 -.->|metrics| Loop3
Loop3 -.->|improvements| Loop1
%% Styling
classDef loop1 fill:#3498DB,stroke:#1A5276,color:#fff
classDef loop2 fill:#27AE60,stroke:#1E8449,color:#fff,stroke-dasharray: 5 5
classDef loop3 fill:#9B59B6,stroke:#6C3483,color:#fff
class D1,L1,E1,S1,F1 loop1
class E2,T2,FT2 loop2
class B3,M3,A3 loop3
10. Future Directions
Near-Term (Q1 2026)
| Priority | Goal | Package | Status |
|---|---|---|---|
| P0 | PyPI releases for all packages | all | In progress |
| P0 | WAA baseline metrics established | evals | Pending |
| P1 | Fine-tuning pipeline validated | ml | In progress |
| P1 | Demo conditioning in evals | evals + retrieval | Pending |
| P2 | docs.openadapt.ai launched | docs | Pending |
Medium-Term (2026)
| Goal | Description |
|---|---|
| Process Mining | Automatic extraction of semantic actions from demos |
| Self-Improvement | Training on successful execution traces |
| Multi-Benchmark | WebArena + OSWorld integration |
| Enterprise Deployment | Production deployment guides |
Long-Term (2026+)
| Goal | Description |
|---|---|
| Cross-App Transfer | Demos from Excel help with Google Sheets |
| Multi-Agent | Coordinated agents for complex workflows |
| Active Learning | Agents request human help strategically |
| Mobile Platforms | iOS and Android capture/replay |
Research Questions
- Abstraction Discovery: Can we automatically extract semantic actions from literal event sequences?
- Transfer Learning: How much does demo conditioning help across different applications?
- Explanation: How do we make agent decisions interpretable to users?
- Safety: What guardrails prevent harmful autonomous actions?
Appendix A: Glossary
| Term | Definition |
|---|---|
| A11y Tree | Accessibility tree - structured UI element representation |
| Demo | Recorded human demonstration (trajectory) |
| Grounding | Mapping text/intent to specific UI coordinates |
| LoRA | Low-Rank Adaptation - efficient fine-tuning method |
| Policy | Decision function mapping observations to actions |
| SoM | Set-of-Mark - visual grounding via numbered labels |
| Trajectory | Sequence of (observation, action) pairs |
| VLM | Vision-Language Model |
| WAA | Windows Agent Arena benchmark |
Appendix B: Related Documents
- Architecture Overview - Package structure and data flow
- Roadmap Priorities - Current development priorities
- Package Documentation - Individual package guides
- Legacy Freeze - Migration from monolith
Appendix C: Version History
| Version | Date | Changes |
|---|---|---|
| 3.0 | Jan 2026 | Alpha vision synthesis, evolution diagrams, SOTA alignment |
| 2.0 | Jan 2026 | Comprehensive redesign, modular architecture |
| 1.0 | Dec 2025 | Initial modular architecture |
| 0.x | 2023-2024 | Legacy monolithic design |
This document is maintained as part of the OpenAdapt project. For updates, see the GitHub repository.