openadapt-types

[!IMPORTANT] Status: Experimental. Interoperability schemas, not the product. This package publishes shared schemas for computer-use agents as an optional component, with no production support promise.

The OpenAdapt product is the demonstration compiler, openadapt-flow, installed via the OpenAdapt launcher (pip install openadapt): it compiles a demonstrated GUI workflow into a deterministic, locally executable program. Healthy runs make no model calls, and it halts instead of guessing when verification fails. Lifecycle labels for every repository are in the repository lifecycle registry.

Canonical Pydantic schemas for computer-use agents.

pip install openadapt-types

These are the shared Action and UI-state types used across the OpenAdapt stack: recorders emit them, the compiler stores and replays them, the grounding package resolves their targets, and the privacy package scrubs them. Defining the schema once keeps every substrate (web, Windows, macOS, Linux, RDP, Citrix/VDI) on the same contract.

The OpenAdapt stack

OpenAdapt is a governed demonstration compiler: record a workflow once, compile the recording into a deterministic program, and replay that program with zero model calls on the healthy path. When the live screen does not match what was demonstrated it halts instead of guessing, using identity gates and independent effect verification. Every substrate is first-class: web and desktop recording are validated, RDP and Windows replay are early, and Citrix is exploratory.

Documentation for the whole stack lives at docs.openadapt.ai.

What's in the box

Quick start

from openadapt_types import (
    Action, ActionTarget, ActionType,
    ComputerState, UINode, BoundingBox,
)

# Describe what's on screen
state = ComputerState(
    viewport=(1920, 1080),
    nodes=[
        UINode(node_id="n0", role="window", name="My App", children_ids=["n1"]),
        UINode(node_id="n1", role="button", name="Submit", parent_id="n0",
               bbox=BoundingBox(x=500, y=400, width=100, height=40)),
    ],
)

# Agent decides what to do
action = Action(
    type=ActionType.CLICK,
    target=ActionTarget(node_id="n1"),
    reasoning="Click Submit to proceed",
)

# Render element tree for LLM prompts
print(state.to_text_tree())
# [n0] window: My App
#   [n1] button: Submit

Action targeting

ActionTarget supports three grounding strategies (in priority order):

# 1. Element-based (preferred, most robust)
ActionTarget(node_id="n1")

# 2. Description-based (resolved by grounding module)
ActionTarget(description="the blue submit button")

# 3. Coordinate-based (fallback)
ActionTarget(x=550, y=420)
ActionTarget(x=0.29, y=0.39, is_normalized=True)

Agents SHOULD produce node_id or description. The runtime resolves to coordinates.

Compatibility with existing schemas

Converters for three existing OpenAdapt schema formats:

from openadapt_types._compat import (
    from_benchmark_observation,   # openadapt-evals BenchmarkObservation
    from_benchmark_action,        # openadapt-evals BenchmarkAction
    from_ml_observation,          # openadapt-ml Observation
    from_ml_action,               # openadapt-ml Action
    from_omnimcp_screen_state,    # omnimcp ScreenState
    from_omnimcp_action_decision, # omnimcp ActionDecision
)

# Convert existing data
state = from_benchmark_observation(obs.__dict__)
action = from_benchmark_action(act.__dict__)

JSON Schema

Export for language-agnostic tooling:

import json
from openadapt_types import ComputerState, Action, Episode

# Get JSON Schema
schema = ComputerState.model_json_schema()
print(json.dumps(schema, indent=2))

The same API exports the versioned cross-surface overlay contracts:

from openadapt_types import ControlOverlayFrameV1, ControlOverlayTimelineV1

frame_schema = ControlOverlayFrameV1.model_json_schema()
timeline_schema = ControlOverlayTimelineV1.model_json_schema()

The same schemas ship as openadapt_types/schemas/control-overlay-frame-v1.json and control-overlay-timeline-v1.json for TypeScript, Rust, and other consumers.

Overlay schemas reject unknown fields and contain only closed presentation labels and canonical statuses. Screenshots, action targets, typed values, identities, URLs, logs, report bodies, and user-authored workflow names remain outside this public presentation contract.

Design principles

• Pydantic v2: runtime validation, JSON Schema export, fast serialization
• Pixels and structure: always capture both visual and semantic UI state
• Node graph: full element tree, not just the focused element
• Platform-agnostic: same schema for web, Windows, macOS, Linux, RDP, Citrix/VDI
• Extension-friendly: raw, attributes, metadata fields everywhere
• Backward compatible: _compat converters for gradual migration

Dependencies

Just pydantic>=2.0. No ML libraries, no heavy deps.

License

MIT