perceive 0.3.2

Compact, ref-stable, reachability-filtered perception for AI browser agents

perceive

AI browser agents click things that aren't actually clickable.

perceive is a Python library that filters them out. Closed drawers, modal-occluded buttons, inert subtrees, off-screen transforms: gone before the model sees the snapshot. What's left is a compact, ref-stable action space the model can plan against, plus state.diff() for confirming what changed after each action.

import perceive

with perceive.browser(url="https://example.com") as t:
    state = t.perceive()
    print(state.to_prompt())
    # @e1 link "More information..."

    t.act("click", state.find(name="More information").ref)

Benchmark results

Measured on a 19-page hand-labeled reachability conformance suite (bench/): 14 synthetic patterns plus 5 real-world component-library cases (Radix Dialog, MUI Modal, Ant Design Drawer, Headless UI Combobox, scrollable list with repeated actions). Same machine, same Chromium build, same 60 ground-truth labels (34 reachable, 26 unreachable):

Playwright MCP surfaces 18 elements an AI agent cannot actually interact with; perceive surfaces 0.

playwright_mcp     18 / 26 unreachable surfaced     195 tokens     3548 ms cold-call
perceive            0 / 26 unreachable surfaced      14 tokens     1657 ms cold-call

Adapter	Precision	F1	False-positive actions	Median observation tokens / page	Median cold-call latency
Raw a11y baseline (no reachability filtering)	0.567	0.723	26 / 26	26	1844 ms
Playwright MCP (@playwright/mcp)	0.654	0.791	18 / 26	195	3548 ms
perceive	1.000	1.000	0 / 26	14	1657 ms

Recall is 1.000 for all three adapters; the gap is precision, not coverage.

$ perceive-bench run --adapter playwright_mcp --suite reachability
  precision : 0.654    FP: 18 / 26    median tokens: 195    median cold-call latency: 3548 ms

$ perceive-bench run --adapter perceive --suite reachability
  precision : 1.000    FP:  0 / 26    median tokens:  14    median cold-call latency: 1657 ms

Tokens are the agent-facing snapshot only (state.to_prompt() for perceive, browser_snapshot for Playwright MCP); prompt context is excluded. Latency is per-call wall time including a fresh browser launch; a long-lived MCP server would close most of that gap. The false-positive and token numbers are unaffected.

The 18 false positives are patterns Chromium's a11y tree alone can't resolve: modal occlusion, sticky-header overlap, off-screen transforms, inert subtrees, aria-hidden cascades, including the Radix Dialog, MUI Modal, and Ant Design Drawer. perceive runs an explicit reachability pass over them. Determinism across 19 pages × 5 runs: 1.000 exact match.

Scope of claim. This is a reachability conformance benchmark, not a general claim about Playwright. Playwright remains the execution layer perceive's browser backend builds on; this measures the observation layer.

Bench adapters for Chrome DevTools MCP and Vercel agent-browser are still on the roadmap.

Install

pip install perceive
playwright install chromium    # ~100 MB Chromium binary

Three things perceive does that a raw accessibility tree does not

1. Filter unreachable elements

import perceive

# A closed drawer is still in the DOM, just translated off-screen.
# A raw a11y tree includes its buttons. perceive does not.
with perceive.browser(url="https://your-app.com") as t:
    state = t.perceive()
    print(len(state.elements))                                    # 4 visible buttons
    state_full = t.perceive(include_unreachable=True)
    print(len(state_full.elements))                                # 7 (visible + drawer contents)
    for el in state_full.elements:
        if not el.reachable:
            print(f"  filtered: {el.role} {el.name!r} ({el.unreachable_reason})")
    # filtered: button 'Close Drawer' (offscreen)
    # filtered: button 'Submit Form' (offscreen)

2. Filter modal-occluded elements

# Buttons behind an open modal are present in the DOM and the a11y tree,
# but a real user cannot click them. perceive returns only the modal's buttons.
with perceive.browser(url="https://your-app.com") as t:
    state = t.perceive()
    for el in state:
        print(el.ref, el.role, repr(el.name))
    # e1 button 'OK'        (in the modal)
    # e2 button 'Cancel'    (in the modal)
    # the two background buttons are filtered out

3. Stable refs across reflows, including for repeated elements

with perceive.browser(url="https://your-app.com/users") as t:
    state = t.perceive()

    # Repeated buttons with the same label get distinct refs, disambiguated
    # by surrounding context (parent landmark, siblings, stable attributes):
    edits = state.find_all(name="Edit")
    print([e.ref for e in edits])
    # ['e3', 'e5', 'e7']

    # An element's ref is preserved across re-perceives, including after
    # scrolling and other reflows that keep the element in the document:
    sign_in_before = state.find(name="Sign in").ref
    t.act("scroll", direction="down", amount=400)
    sign_in_after = t.perceive().find(name="Sign in").ref
    assert sign_in_before == sign_in_after

Why not just use Playwright locators?

Playwright locators are the right tool when you already know what to interact with. You write page.get_by_role("button", name="Sign in") because you, the human author, decided that button is what you want.

perceive is for the part of an agent loop where the model needs to decide what's available. The flow is observe → plan → act → verify, and step 1 is "give the model a compact, reachable, ref-stable action space." perceive does that step; it doesn't replace deterministic Playwright tests for code you've already written.

Integration: feeding perceive output to an LLM

import perceive

with perceive.browser(url="https://app.example.com/login") as target:
    state = target.perceive()

    prompt = f"""You are operating a browser. Available actions:
- click(ref)
- type(ref, text)
- scroll(direction)

Current UI:
{state.to_prompt()}

Task: sign in as [e-mail] with password hunter2.
Respond with one action per line."""

    # Send `prompt` to any LLM (Claude, GPT, Gemini, local model).
    # Parse the response into actions, then call:
    target.act("type", "e2", "[e-mail]")
    target.act("type", "e3", "hunter2")

    # Use observe_change to see the result of the click in compact form.
    with target.observe_change() as obs:
        target.act("click", state.find(name="Sign in").ref)
    print(obs.diff.to_prompt())
    # +@e7 dialog "Welcome back, Alice"
    # -@e3 textbox "Password"
    # … 5 unchanged

API

target = perceive.browser(url=None, *, headless=True, viewport=(1280, 800))

# Navigation and lifecycle
target.goto(url)
target.close()                                  # or use as a context manager

# Perception
state = target.perceive(
    region=None,                # CSS selector or (x, y, w, h) bbox to scope
    role=None,                  # filter to a single role (e.g. "button")
    include_text=False,         # reserved; not yet implemented
    include_unreachable=False,  # default: filter unreachable
)

# State
state.elements                  # list[Element]
state.find(ref=..., role=..., name=..., reachable=...)
state.find_all(role=..., name=..., reachable=...)
state.to_prompt(only_reachable=True)
state.diff(previous)            # DiffResult

# Action (shares ref space with the most recent perceive())
target.act("click", ref)
target.act("type", ref, text)
target.act("set_value", ref, text)            # programmatic, for tricky inputs
target.act("scroll", direction="down", amount=400)
target.act("press", key)                       # e.g. "Enter", "Tab"
target.act("goto", url)
target.act("wait", seconds)

# Self-verifying loop
with target.observe_change(settle_ms=200) as obs:
    target.act("click", "e1")
obs.before, obs.after, obs.diff

Limitations

This is a deliberately narrow early release. Things perceive does not do yet:

• Browser only. A macOS backend (perceive.macos()) is on the roadmap but not yet implemented.
• Chromium only. Playwright supports Firefox and WebKit but neither is tested against the benchmark suite.
• No vision fallback. Canvas-heavy UIs, custom widgets without ARIA, and image-only elements will return as fewer (or zero) elements. A small-VLM fallback is on the roadmap.
• Cross-origin iframes cannot be introspected (browser security; same-origin iframes work).
• Closed Shadow DOM cannot be traversed ({ mode: 'closed' } is opaque by design). Open shadow roots work.
• Ref stability is exact-fingerprint based. A button whose accessible name changes mid-session ("Save" → "Saving…") will get a new ref. Scored-similarity matching is on the roadmap.
• Benchmark is 19 pages. Patterns covered: CSS hiding, positioning, occlusion, ancestor attributes, traversal (Shadow DOM + iframe), non-interactive controls, and the real DOM emitted by Radix Dialog, MUI Modal, Ant Design Drawer, Headless UI Combobox, and a long scrollable list. Patterns not yet covered: virtualized lists with off-DOM rows, portals, nested modals, cookie banners, animated layout shift. Expanding before any "production-ready" claim.
• Bench adapters for Chrome DevTools MCP and Vercel agent-browser are not yet implemented. The Playwright MCP adapter ships in bench/adapters/.

Reproducing the benchmarks

The repo includes a bench package. To run it yourself:

git clone https://github.com/gauthierpiarrette/perceive.git
cd perceive
pip install -e ".[bench,dev]"
playwright install chromium

perceive-bench list pages
perceive-bench list adapters

# Run the head-to-head against Playwright MCP yourself.
# Requires Node.js + npx; the first invocation downloads @playwright/mcp.
perceive-bench run --adapter playwright_mcp --suite reachability
perceive-bench run --adapter playwright_mcp --suite tokens

# Same against perceive.
perceive-bench run --adapter perceive --suite reachability
perceive-bench run --adapter perceive --suite tokens
perceive-bench run --adapter perceive --suite determinism --runs 5

All results are written to results/ as JSON.

Roadmap

Ordered by priority; version assignments are deliberately unpinned because the v0.1 → v0.3 sequence already taught us that pinning features to specific versions is a promise the codebase will break.

• Next. Bench adapters for Chrome DevTools MCP and Vercel agent-browser; expanded conformance corpus (virtualized lists with off-DOM rows, portals, nested modals, cookie banners, animated layout shift).
• Then. include_text=True body capture; scored-similarity ref matching so elements whose accessible name changes mid-session keep their refs; an MCP server adapter so non-Python agents can consume perceive directly.
• Later. Experimental desktop perception: macOS (AXUIElement), Windows (UIA), Linux (AT-SPI), all behind the same State / Element shape. Read-only first; desktop act() ships separately.
• Beyond. Vision fallback as a plugin API (target.set_vision_backend(...)), with a first small-VLM backend for canvas-heavy and non-accessible regions.

License

Apache-2.0. See LICENSE and NOTICE.