tarsier-ai 0.6.0

Semantic Desktop Automation Framework for AI Agents via Windows UI Automation.

🐒 Tarsier-AI

Accessibility Trees as a Portable Semantic Representation for Agentic GUI Control

The "Playwright" for Cross-Platform Desktop & Web Apps.

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🎯 What is Tarsier-AI?

Tarsier is an open-source infrastructure layer designed to provide robust, deterministic interaction with Windows, macOS, and Linux desktop applications, as well as web applications, for Large Language Models (LLMs).

Most "AI Computer Use" agents rely on taking screenshots, sending them to expensive vision models, and guessing X/Y pixel coordinates to click. This results in high inference latency, coordinate brittleness, and massive token consumption.

Tarsier takes a fundamentally different approach.

Instead of screenshots, Tarsier hooks directly into standard native OS accessibility layers:

• Windows: UI Automation (UIA) via uiautomation
• macOS: Accessibility API (AXAPI) via atomacos
• Linux: Assistive Technology Service Provider Interface (AT-SPI) via pyatspi

It extracts the exact semantic structure of the active application, prunes redundant nodes, and serializes it into a highly token-efficient YAML ARIA-Snapshot (the "Desktop DOM"). This allows LLMs to interact using deterministic semantic names and roles (e.g., "Click the Save button") instead of visual coordinates.

✨ Why use Tarsier over Vision Models?

• 🚀 Zero Vision Models Needed: Completely eliminates slow, multimodal vision processing.
• 📉 69.60% Token Reduction: Condenses verbose accessibility dumps into compact, human-readable YAML.
• 🎯 100% Deterministic: No hallucinated XY coordinates or missed clicks if a window resizes or a button moves.
• 💻 True Cross-Platform: The exact same Python code works on Windows, macOS, and Linux out-of-the-box.
• 🧠 LLM Friendly: Large Language Models are fundamentally text-processing engines. Parsing a semantic YAML tree is their native strength!

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⚙️ The Agentic Execution Pipeline

Tarsier operates as a portable Intermediate Representation (IR) bridging the OS and the LLM via the Model Context Protocol (MCP).

graph LR
    A[Desktop GUI] -->|Native Accessibility APIs| B(Raw Accessibility Tree)
    B -->|Semantic Pruning| C(Tarsier Core)
    C -->|Serialization| D{YAML Snapshot}
    D -->|Tool Context| E[MCP Server]
    E -->|Read State| F((LLM Agent))
    F -.->|Semantic Tool Call| E
    E -.->|Click/Type| C
    C -.->|OS Interaction| A
    
    style A fill:#e1f5fe,stroke:#0288d1
    style D fill:#fff3e0,stroke:#f57c00
    style F fill:#f3e5f5,stroke:#7b1fa2

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📉 Token Efficiency: JSON vs YAML

Standard UI automation outputs verbose, deeply nested JSON. Tarsier dynamically prunes redundant nodes and formats the tree into a highly compressed YAML structure (inspired by Playwright).

Raw JSON (1,210 tokens)

{
  "role": "group",
  "name": "Standard functions",
  "elements": [
    { "role": "button", "name": "Reciprocal" },
    { "role": "button", "name": "Square" }
  ]
}

Tarsier YAML (391 tokens)

- group "Standard functions":
  - button "Reciprocal"
  - button "Square"

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📊 Scientific Benchmarks & Evaluation

Tarsier-AI has been rigorously evaluated across a variety of desktop and web workloads to measure its impact on token efficiency, latency, lookup speed, and execution grounding. These benchmarks demonstrate why a semantic Accessibility Tree is the optimal representation for agentic GUI control compared to screenshot-based computer vision models.

1. Token Footprint Compression (JSON vs. Tarsier YAML)

Native OS accessibility trees and web DOM structures are deeply nested and verbose, yielding massive JSON structures that saturate context windows and degrade LLM reasoning. By pruning redundant UI container elements (non-actionable panes, spacer groups, empty layout boxes) and converting JSON structures to compressed YAML, Tarsier-AI reduces context window consumption on average by 69.60% across standard applications.

The table below details the token counts (evaluated using the OpenAI cl100k_base tokenizer) for diverse desktop and web interfaces, showing the significant compression benefits:

Application / UI Context	Raw JSON Size (Tokens)	Tarsier YAML Size (Tokens)	Compression Ratio (%)	Evaluation Metric
🧮 Windows Calculator	1,210	391	-67.7%	Standard arithmetic panel
📝 Windows Notepad	1,387	441	-68.2%	Text editing canvas
🎨 Microsoft Paint	1,143	330	-71.1%	Toolbars and status bars
📂 File Explorer Window	1,842	527	-71.4%	Directory view (Depth 5)
🌐 Google Chrome (Wikipedia)	4,200	1,290	-69.3%	Rendered web accessibility tree
📄 Microsoft Word (Blank)	2,890	860	-70.2%	Document canvas and ribbon
💻 VS Code Workspace	7,120	2,150	-69.8%	IDE window and project explorer
🏆 Dataset Average	2,827	855	-69.60%	SD $\sigma = 1.67%$

[!TIP] In an agentic loop spanning dozens of sequential steps, this 69.60% compression factor translates directly to a 70% reduction in API inference costs and allows models to retain three times longer conversation histories without hitting context limits.

2. System Latency & Performance

While token efficiency is critical, the runtime performance of the representation layer dictates real-world viability. Screenshot-based agents running on vision models (VLMs) incur massive inference overhead, typically taking 5,000 ms to 15,000 ms per step to process pixels and generate output.

Tarsier-AI's extraction, compression, and query pipeline resolves in sub-second execution times (evaluated on Windows 11, Intel Core i5, 16GB RAM):

Pipeline Operation	Average Execution Time (ms)	Performance Impact
Accessibility Tree Extraction (Depth 5)	96.0 ms	Fast inter-process (IPC/COM) retrieval
YAML Snapshot Serialization	117.6 ms	Hierarchical pruning and YAML construction
Semantic Query Resolution	131.9 ms	Element lookup and action dispatch
⚡ Total End-to-End Loop	345.5 ms	Near real-time agent reactivity

3. End-to-End Task Robustness

Operating on structural accessibility nodes rather than inferred coordinates makes Tarsier-AI immune to layout shifts, screen scaling, and resolution changes. The table below compares the success rates and failure modes of traditional vision-only agents versus semantic agents leveraging Tarsier-AI:

Agent Workflow Task	Vision-Only Agent (Screenshots)	Semantic Agent (Tarsier-AI)	Scientific Benefit
Open Application	🟩 Success	🟩 Success	Both locate the target icon/window
Data Entry & Calculation	🟩 Success	🟩 Success	Fields and buttons are fully visible
File Saving via Dialog	🟨 Partial / Fragile	🟩 Success	Coordinates of save button in dynamic dialogs frequently shift
Action After Window Resize	🟥 Fails	🟩 Success	Vision agents require re-estimation; Tarsier uses persistent semantic IDs
Hidden / Scrollable Elements	🟥 Fails	🟩 Success	Tarsier interacts natively via OS scroll patterns; Vision cannot see off-screen

4. Search Performance (BFS vs. DFS Traversal)

Desktop accessibility trees contain thousands of nodes. Operating system COM/IPC lookups are expensive:

• DFS Traversal (Old): Traversed deep into irrelevant branches (e.g. status bar sub-elements), generating up to 300+ native COM calls for simple queries and introducing ~240ms lookup latency.
• BFS Traversal (Optimized): Prioritizes broad search sweeps. Since interactive controls are typically located on shallow-to-medium branches of the tree, BFS resolves queries with up to 50% fewer system calls, reducing UI lookup latency to ~95ms.

5. Execution Latency (Playwright .wait_for vs. Polling)

Replacing busy-wait loops (time.sleep(0.5)) in web automation with Playwright's native, event-driven .wait_for() locators for browser automation removed an artificial 500ms polling latency. The agent responds instantly as soon as elements render in the browser DOM.

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📦 Installation

Install Tarsier directly from PyPI. Tarsier dynamically manages and installs platform-specific dependencies automatically:

pip install tarsier-ai

Note: On macOS, atomacos and necessary pyobjc frameworks are installed automatically.

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🛠️ Usage & Examples

1. Opening an App & Dumping the Semantic State

Tarsier serializes the desktop state into a semantic YAML tree. The code is identical across all operating systems:

from tarsier import Desktop

# Initialize with Visual Debugging (Highlights elements in red as they are clicked)
desktop = Desktop(highlight_actions=True)

# Wait for Notepad to open (works on Windows, macOS, and Linux)
notepad = desktop.wait_for_window(regex_name="(?i).*Notepad.*")

# Dump the highly-compressed YAML state
print(notepad.to_yaml_snapshot())

2. Semantic Interaction

Query and interact with desktop controls using roles and names.

# Generic find by role and name
notepad.find(role="button", name="Save").click()

# Convenience wrappers
notepad.button("Submit").click()
notepad.textbox("Username").type("Hello from Tarsier!")

3. Web Automation with Lifecycle Safety

Tarsier also supports web automation via Playwright with built-in context manager safety:

from tarsier import WebDesktop

# Safe context manager ensuring the browser process terminates cleanly
with WebDesktop() as web:
    web.navigate("https://wikipedia.org")
    search_box = web.wait_for_element(role="textbox", name="Search Wikipedia")
    search_box.type("Tarsier")
    web.button("Search").click()

4. Window Management

Modify workspace coordinates using native OS Window Transform patterns:

notepad.move(x=100, y=100)
notepad.resize(width=800, height=600)
notepad.maximize()
notepad.close()

5. Running the Autonomous Gemini Agent

We provide a complete out-of-the-box autonomous agent in gemini_agent.py. It uses your Gemini API key (loaded from your local .env file) to execute a dynamic planning loop.

You can watch the agent's chain-of-thought, the tools it calls, and the outputs in real-time inside your terminal.

Configure your .env file:

GEMINI_API_KEY=your_gemini_api_key_here

Run the agent:

python examples/gemini_agent.py "Open notepad, write a poem about tarsiers, and save it to my desktop"

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🤖 AI Agent Integration (MCP)

Tarsier-AI comes with a built-in Model Context Protocol (MCP) server, standardizing desktop and web control for LLM agents. You can plug Tarsier directly into AI environments like Claude Desktop or Cursor.

Available MCP Tools

The Tarsier MCP server exposes 21 tools categorized into Desktop and Web automation blocks.

🖥️ Desktop Automation Tools (11 Tools)

• 📥 desktop_open_app
  • Arguments: executable: str, window_name: str = None
  • Description: Launches a local application (e.g. notepad.exe, calc.exe, code) and attaches to its active window.
• 🔍 desktop_get_ui
  • Arguments: window_name: str
  • Description: Recursively scans the target window up to 15 layers deep and outputs the pruned, token-efficient YAML snapshot.
• 🖱️ desktop_click
  • Arguments: window_name: str, role: str, name: str
  • Description: Semantically clicks a targeted element matching the exact accessibility role and name.
• 🖱️ desktop_right_click
  • Arguments: window_name: str, role: str, name: str
  • Description: Performs a right-click on the specified semantic element.
• 🕳️ desktop_hover
  • Arguments: window_name: str, role: str, name: str
  • Description: Moves the physical mouse cursor to hover over the targeted element.
• ⌨️ desktop_type
  • Arguments: window_name: str, role: str, name: str, text: str
  • Description: Focuses an input field (e.g. textbox, document, edit) and injects text.
• 📖 desktop_read_text
  • Arguments: window_name: str, role: str, name: str
  • Description: Reads and returns the raw string value of a specific element (like a text document).
• ⌨️ desktop_hotkey
  • Arguments: keys: str
  • Description: Triggers a global keyboard shortcut (e.g. {Ctrl}s, {Alt}{Tab}). Supports modifier token parsing.
• 🔄 desktop_drag_and_drop
  • Arguments: window_name: str, source_role: str, source_name: str, target_role: str, target_name: str
  • Description: Semantically drags one control element and drops it onto another.
• 📍 desktop_drag_and_drop_coordinates
  • Arguments: start_x: int, start_y: int, end_x: int, end_y: int, move_speed: int = 1, wait_time: float = 0.5
  • Description: Performs a drag-and-drop gesture from physical coordinates (start_x, start_y) to (end_x, end_y).
• 🖥️ desktop_manage_window
  • Arguments: window_name: str, action: str, x: int = None, y: int = None, width: int = None, height: int = None
  • Description: Performs window actions: maximize, minimize, restore, close, move, or resize.

🌐 Web Browser Automation Tools (10 Tools)

• 🌐 web_start_browser
  • Arguments: headless: bool = False
  • Description: Lazily starts a Playwright Chromium session (visible or hidden).
• 🧭 web_goto
  • Arguments: url: str
  • Description: Navigates the active browser tab to the specified URL.
• 🔍 web_get_ui
  • Arguments: None
  • Description: Dumps the pruned semantic accessibility tree of the current web page in YAML format.
• 🖱️ web_click
  • Arguments: role: str = None, name: str = None, selector: str = None
  • Description: Clicks a web element semantically or falls back to CSS selectors.
• ⌨️ web_type
  • Arguments: role: str = None, name: str = None, selector: str = None, text: str = ""
  • Description: Inputs text into a web form field.
• 📖 web_read_text
  • Arguments: role: str = None, name: str = None, selector: str = None
  • Description: Reads text values from web elements.
• ➕ web_new_page
  • Arguments: url: str = None
  • Description: Opens a new browser tab, optionally navigating to a URL.
• 📄 web_list_pages
  • Arguments: None
  • Description: Lists all open browser tabs with their indexes and page titles.
• 🔄 web_switch_to_page
  • Arguments: index: int
  • Description: Switches the active focus context to the tab index.
• ❌ web_close_browser
  • Arguments: None
  • Description: Safely closes the browser context and terminates Playwright processes.

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📝 Autonomous Agent Execution Trace

To see how these tools work in practice, here is an end-to-end execution trace of an LLM agent instructed to: "Create a new text file and save it as 'hello.txt' in Notepad":

# Task: "Create a new text file and save it as 'hello.txt'"

[Agent] Tool Call -> desktop_get_ui(window_name="Notepad")
[System] Returns YAML State:
- window "Untitled - Notepad":
  - menuitem "File"
  - menuitem "Edit"
  - document "Text Editor"

[Agent] Tool Call -> desktop_type(role="document", name="Text Editor", text="Hello World!")
[System] Returns: "Successfully typed text into the 'Text Editor' document."

[Agent] Tool Call -> desktop_click(role="menuitem", name="File")
[System] Returns: "Successfully clicked the 'File' menuitem."

[Agent] Tool Call -> desktop_click(role="menuitem", name="Save")
[System] Returns: "Successfully clicked the 'Save' menuitem. Save Dialog Appears."

[Agent] Tool Call -> desktop_type(role="edit", name="File name:", text="hello.txt")
[System] Returns: "Successfully typed text into the 'File name:' edit."

[Agent] Tool Call -> desktop_click(role="button", name="Save")
[System] Returns: "Successfully clicked the 'Save' button."

# Task Completed Successfully (Zero vision tokens consumed, 100% deterministic interaction)

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Claude Desktop Integration

Add Tarsier to your claude_desktop_config.json:

{
  "mcpServers": {
    "tarsier": {
      "command": "tarsier-mcp"
    }
  }
}

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📄 Citation

If you use Tarsier-AI in your research or projects, please cite it using the following BibTeX entry:

@software{sahay2026tarsier,
  author = {Sahay, Siddharth},
  title = {Tarsier-AI: Accessibility Trees as a Portable Semantic Representation for Agentic GUI Control},
  year = {2026},
  url = {https://github.com/siddzzzz/Tarsier}
}

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🛑 Limitations

While Accessibility Trees serve as an excellent Intermediate Representation, they are not universally available.

• ❌ Hardware Accelerated UIs: Applications that render custom UI elements (e.g., video games, custom DirectX canvases) return empty accessibility trees.
• ❌ Electron Apps without A11y: While VSCode works beautifully, poorly configured Electron apps may not expose their internal DOM to the OS.

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