Event-Driven Capture — Architecture Spec

Status: Draft Date: 2026-02-20

1. Problem

Three independent capture systems run on their own clocks with zero synchronization:

• Vision: polls at 0.5–10 FPS, compares frames, runs OCR, encodes H.265 video
• Accessibility tree walker: walks every 3s or on app switch, stores text separately
• UI event recorder: real-time input capture, stored in its own table

When a user searches a keyword that exists in accessibility data, the nearest screenshot is from a different moment. The thumbnail is wrong. The user doesn't trust the results.

Meanwhile the vision pipeline burns CPU comparing and skipping identical frames on a static screen. The ActivityFeed already detects every click, keystroke, and app switch — but instead of triggering a capture, it nudges a polling rate. That's backwards.

2. Design

2.1 One Capture System

Kill the three-system split. One system: event happens → screenshot + text extraction → store together.

Event (click / app switch / typing pause / scroll stop / idle timer)
  → Screenshot (reuse capture_monitor_image)
  → Accessibility tree walk (reuse walk_focused_window)
  → If accessibility empty → OCR fallback (reuse process_ocr_task)
  → Write JPEG to disk
  → Insert frame + text into DB (single row, single timestamp)

Screenshot and text share the same timestamp because they come from the same capture. No desync possible.

2.2 Event Triggers

Hard constraints:

• Minimum interval: 200ms between captures per monitor. Non-negotiable — prevents storms.
• Maximum gap: 10s. If nothing triggers a capture for 10s, take one anyway. Identical consecutive idle frames are deduplicated via frame hash comparison (already exists in FrameComparer).

2.3 Text Extraction

Accessibility first. OCR as fallback. No "both" mode at capture time — keep it simple.

walk_focused_window() → result
  if result.text_content is non-empty → done (text_source = "accessibility")
  if result is empty/error → run OCR → done (text_source = "ocr")

Accessibility tree walk has a 200ms hard timeout. If the app has a massive AX tree (Electron apps with 10k+ nodes), we take whatever text we got in 200ms and move on. This keeps capture latency predictable.

OCR is the safety net for:

• Image-heavy apps (Figma, Photoshop)
• PDF viewers rendering as canvas
• Video players showing text
• Apps with broken/missing accessibility support

The user doesn't choose. The system picks the right method per-capture.

2.4 Snapshot Storage

No more H.265 video encoding. No more FFmpeg for frame extraction.

Each capture writes a JPEG directly to disk:

~/.screenpipe/data/
  2026-02-20/
    1708423935123_m0.jpg     # monitor 0 screenshot
    1708423937456_m0.jpg
    1708423939100_m1.jpg     # monitor 1 screenshot
    ...

Metadata (text, app name, trigger, etc.) lives in the DB, not sidecar files. The JPEG is just pixels.

Why kill video?

1. Event-driven capture has irregular timing. H.265 assumes consistent FPS.
2. FFmpeg extraction is the #1 timeline bottleneck (100-500ms per frame, 3-permit semaphore).
3. FFmpeg is a 100MB+ dependency we can stop depending on for the hot path.
4. JPEG files are directly servable. Zero processing to display.

Storage math (8 hours active use, 1080p, JPEG quality 80 ≈ 80KB/frame):

• Today (0.5-1 FPS continuous): 14,400–28,800 frames → 1.1–2.3 GB (H.265 compressed ≈ 100-200 MB/hr × 8hr)
• Event-driven (~10 captures/min active, 6/min idle, 50/50 split): ~3,840 frames → ~300 MB total

Fewer frames, each slightly larger, far less total storage.

Reading old data: Legacy video-chunk frames stay on disk forever. The frame retrieval endpoint checks snapshot_path on the frame row — if set, serve JPEG directly; if NULL, use the existing FFmpeg extraction path. Old data keeps working with zero migration effort.

2.5 Database Changes

One migration, additive only:

ALTER TABLE frames ADD COLUMN snapshot_path TEXT;
ALTER TABLE frames ADD COLUMN accessibility_text TEXT;
ALTER TABLE frames ADD COLUMN capture_trigger TEXT;  -- 'app_switch', 'click', 'typing_pause', 'scroll_stop', 'clipboard', 'idle', etc.
ALTER TABLE frames ADD COLUMN text_source TEXT DEFAULT 'ocr';  -- 'ocr' or 'accessibility'

CREATE INDEX idx_frames_ts_device ON frames(timestamp, device_name);

New frames: snapshot_path set, video_chunk_id may be NULL, accessibility_text populated. Old frames: snapshot_path NULL, existing video_chunk_id + offset_index used.

Both coexist in the same table. Timeline and search show both. No data loss.

2.6 Search

Keyword search queries both ocr_text (via existing ocr_text_fts) and the new accessibility_text on frames. Since accessibility_text is on the frame row, the matched thumbnail is always correct.

For the keyword search handler (/search/keyword):

-- Existing OCR path (unchanged)
SELECT ... FROM ocr_text_fts WHERE ocr_text_fts MATCH ?

-- New accessibility path
UNION

SELECT ... FROM frames
WHERE accessibility_text LIKE '%' || ? || '%'
  OR frame_id IN (SELECT rowid FROM accessibility_text_fts WHERE accessibility_text_fts MATCH ?)

Results are merged, deduplicated by frame ID, sorted by timestamp. Thumbnails are always correct regardless of which text source matched.

2.7 Multi-Monitor

Events are monitor-specific where possible:

• Click/scroll → capture the monitor where the cursor is
• App switch → capture the monitor with the newly focused window
• Typing pause → capture the monitor with the focused window

Other monitors get idle fallback captures only (every 5s, deduplicated).

This avoids capturing all monitors on every click — important for 3+ monitor setups.

2.8 Concurrency Model

                    ┌─────────────────────┐
                    │  Event Listener      │  (reuse existing CGEventTap / UI Automation)
                    │  (real-time thread)  │
                    └─────────┬───────────┘
                              │ EventTrigger (type + monitor + timestamp)
                              ▼
                    ┌─────────────────────┐
                    │  Debounce + Dedup    │  (per-monitor, 200ms min interval)
                    │  (async task)        │
                    └─────────┬───────────┘
                              │ qualified trigger
                              ▼
              ┌───────────────────────────────┐
              │  Capture Worker (per monitor)  │
              │  1. capture_monitor_image()    │  ~5ms
              │  2. capture_windows()          │  ~10ms
              │  3. walk_focused_window()      │  ~10-200ms (200ms timeout)
              │  4. if empty → process_ocr()   │  ~100-500ms (rare)
              │  5. encode JPEG, write to disk  │  ~5-10ms
              │  6. insert frame + text to DB   │  ~5ms (batched)
              └───────────────────────────────┘

Total latency per capture: ~30-50ms typical (accessibility path), ~200-600ms worst case (OCR fallback). Well within the 200ms minimum interval.

One capture worker per monitor. Workers are independent — a slow OCR on monitor 1 doesn't block capture on monitor 2.

2.9 Settings

Remove:

• FPS slider (meaningless in event-driven)
• Adaptive FPS toggle (replaced entirely)
• Video quality presets (no video encoding)

Add:

• Capture sensitivity — Low / Medium / High
  • Low: 500ms debounce, 10s idle gap (laptop battery mode)
  • Medium: 200ms debounce, 5s idle gap (default)
  • High: 100ms debounce, 3s idle gap (maximum recall)
• JPEG quality — slider, 60-95%, default 80%

Keep:

• Monitor selection (which monitors to capture)
• Ignored windows list (skip sensitive apps)

3. What Gets Deleted

This is not additive. Old code gets removed.

What stays for backward compat (read path only):

• extract_frame_from_video() — for displaying old video-chunk frames
• video_chunks table — for old data
• offset_index / fps columns on frames — for old data

These remain but receive no new writes. They're read-only legacy support.

4. Implementation Order

Not phased. One PR per step, each shippable independently, but all ship in the same release.

Step 1: DB migration + snapshot write path

• Add new columns to frames
• SnapshotWriter: JPEG write to ~/.screenpipe/data/YYYY-MM-DD/
• insert_snapshot_frame() in DB
• Update get_frame_data() to serve snapshots directly

Step 2: Paired capture function

• paired_capture(): screenshot + accessibility walk + OCR fallback
• Returns PairedCaptureResult with image bytes + text + metadata
• Unit-testable in isolation

Step 3: Event trigger system

• Extend ActivityFeed with tokio::sync::Notify + event type
• EventDrivenCapture::wait_for_trigger() — debounce + dedup logic
• Idle fallback timer
• Wire into existing CGEventTap / UI Automation hooks

Step 4: New capture loop

• Replace VisionManager's capture task with event-driven loop
• One worker per monitor: wait_for_trigger → paired_capture → snapshot_write → db_insert
• Delete continuous_capture(), save_frames_as_video(), FrameWriteTracker
• Delete adaptive FPS, get_capture_params()

Step 5: Search + timeline integration

• Add accessibility_text to keyword search FTS
• Update timeline data fetching to handle snapshot frames
• Update settings UI (remove FPS, add sensitivity)

Step 6: Cleanup

• Remove dead code: video encoding pipeline, OCR queues, frame comparison gatekeeper
• Update TESTING.md with new test cases
• Remove adaptive-fps feature flag from Cargo.toml

5. Testing Checklist

Capture correctness

• App switch → capture within 500ms, correct app/window in metadata
• Click → capture within 400ms, screenshot reflects post-click state
• Typing 3 words, stop → capture within 1s of last keystroke
• Scroll through long page, stop → capture shows final scroll position
• Copy text → capture within 400ms
• Sit idle 10s → idle capture fires, identical consecutive frames deduplicated
• Rapid clicking (10 clicks in 1s) → at most 5 captures (200ms min interval)
• 3 monitors → events only capture affected monitor, others get idle captures

Text extraction

• Chrome: accessibility returns page text, window title, URL
• VS Code: accessibility returns visible code
• Finder: accessibility returns file names
• Figma/Photoshop: accessibility empty → OCR fallback activates
• Electron app with huge DOM: tree walk returns partial text within 200ms timeout

Storage

• Snapshots written as valid JPEG, correct resolution, readable by Preview/Photos
• Directory ~/.screenpipe/data/YYYY-MM-DD/ created automatically
• Frame retrieval: snapshot frames served in <5ms (no FFmpeg)
• Frame retrieval: old video-chunk frames still served correctly via FFmpeg
• Disk cleanup deletes oldest snapshots when retention limit hit

Search

• Keyword in accessibility text → correct thumbnail (no desync)
• Keyword in OCR text (fallback frames) → correct thumbnail
• Keyword in old OCR data (pre-migration) → still works
• Mixed results (old video frames + new snapshots) display correctly

Regression (from TESTING.md)

• Section 3: Monitor plug/unplug — capture resumes on new/remaining monitors
• Section 5: Static screen < 0.5% CPU. Active use < 5% CPU.
• Section 6: Permissions — accessibility prompt on first launch
• Section 8: Sleep/wake — capture resumes within 5s
• Section 9: DB concurrent access — no "database is locked" errors
• Section 12: Timeline navigation, search results, frame deep links all work

6. E2E Robot Testing

Principle: Use Accessibility to Test Accessibility

The same APIs we use for capture can drive automated E2E tests. On macOS, osascript opens apps, clicks buttons, types text, switches windows. On Windows, PowerShell + UI Automation does the same. Tests perform real user actions, wait for captures to appear in the DB, and assert correctness.

Test Layers

Layer 1: Unit tests (fast, CI, no UI)

• Debounce: rapid events → correct trigger count
• Frame dedup: identical images → skip, different → capture
• SnapshotWriter: valid JPEG, correct path format
• DB: insert_snapshot_frame → query returns correct data
• Search: accessibility_text FTS matches

Layer 2: Integration tests (CI, headless)

• Paired capture: screenshot + accessibility return together
• OCR fallback: accessibility empty → OCR runs
• Legacy compat: video-chunk frames still serve via FFmpeg

Layer 3: E2E robot tests (real machines, real UI, nightly CI)

# macOS: osascript drives real apps
# Windows: PowerShell + [System.Windows.Automation] drives real apps

test_app_switch_capture:
  1. open TextEdit, type "test document alpha"
  2. open Safari, navigate to example.com
  3. sleep 1s
  4. query DB: frames WHERE capture_trigger = 'app_switch' AND timestamp > test_start
  5. assert: >= 2 frames
  6. assert: frame 1 accessibility_text contains "test document alpha"
  7. assert: frame 2 app_name = "Safari"
  8. assert: both snapshot_path files are valid JPEGs

test_typing_pause_capture:
  1. focus TextEdit, type "meeting notes for project X"
  2. sleep 1s
  3. assert: frame with capture_trigger = 'typing_pause'
  4. assert: accessibility_text contains "meeting notes for project X"

test_scroll_capture:
  1. open Safari, navigate to long page
  2. scroll down 5 times
  3. sleep 1s
  4. assert: frame with capture_trigger = 'scroll_stop'
  5. assert: content differs from pre-scroll frame

test_click_capture:
  1. open System Settings, click "General"
  2. sleep 500ms
  3. assert: frame with capture_trigger = 'click'

test_idle_fallback:
  1. do nothing for 12s
  2. assert: >= 1 frame with capture_trigger = 'idle'

test_rapid_events_debounce:
  1. click 20 times in 1s
  2. sleep 1s
  3. assert: <= 5 frames (200ms min interval)

test_search_thumbnail_correctness:
  1. open TextEdit, type "unique_term_xyz"
  2. switch to Safari
  3. sleep 2s
  4. GET /search?q=unique_term_xyz
  5. assert: result thumbnail shows TextEdit, not Safari

Layer 4: Soak test (8 hours real use, before every release)

• Run event-driven capture during a full workday
• End-of-day assertions:
  • No crashes, no DB corruption, no orphaned files
  • Frame count: 3,000–5,000 (reasonable for 8hr active day)
  • Disk: ~300 MB total
  • Search responds in <2s on full-day DB
  • CPU never exceeded 10% sustained

CI Integration

• Unit + integration tests: every PR (fast, headless)
• E2E robot tests: nightly on macOS + Windows runners (too slow for PRs)
• Soak test: manual before each release, automated weekly on dev machines

7. Windows Compatibility

~90% of new code is platform-agnostic (debounce, paired capture, snapshot writer, DB, search). Platform-specific code already exists and is abstracted:

One platform-specific tuning: UIA tree walk is slower on Windows (200-500ms vs 10-50ms on macOS). The accessibility timeout constant needs #[cfg(target_os)]:

#[cfg(target_os = "macos")]
const AX_WALK_TIMEOUT_MS: u64 = 200;
#[cfg(target_os = "windows")]
const AX_WALK_TIMEOUT_MS: u64 = 350;

E2E robot tests use platform-native automation:

• macOS: osascript (AppleScript)
• Windows: PowerShell + System.Windows.Automation

8. Success Criteria