screenpipe-api

name	screenpipe-api
description	Query the user's screen recordings, audio, UI elements, and usage analytics via the local Screenpipe REST API at localhost:3030. Use when the user asks about their screen activity, meetings, apps, productivity, media export, retranscription, or connected services.

Screenpipe API

Local REST API at http://localhost:3030. Full reference (60+ endpoints): https://docs.screenpi.pe/llms-full.txt

Authentication

ALL requests require authentication. Add the auth header to every curl call:

curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" \
  -H "X-Screenpipe-Client: api" \
  "http://localhost:3030/..."

The fixed X-Screenpipe-Client: api value attributes a successful, nonempty external retrieval to the API surface. Never put an agent name, customer name, project, prompt, or other dynamic value in this header.

The $SCREENPIPE_LOCAL_API_KEY env var is already set in your environment. Without it you get 403. The only exception is /health (no auth needed).

Context Window Protection

API responses can be large. Always write curl output to a file first (curl ... -o /tmp/sp_result.json), check size (wc -c /tmp/sp_result.json), and if over 5KB read only the first 50-100 lines. Extract what you need with jq. NEVER dump full large responses into context.

For the list endpoints (/search, /elements, /frames/{id}/elements) you can also cut tokens at the source: add &format=csv (or tsv) to get a columnar table that writes each column name once instead of repeating keys per row, and &fields=a,b,c to return only the columns you need (dotted paths like content.text). On a list of UI elements that is roughly a 70% token cut versus JSON. For the element endpoints specifically, &format=outline (alias tree) goes further still — a deduped, indented tree of just the text-bearing nodes (~91% fewer tokens, measured) — and is the best default for reading UI structure. Use &format=automation for automation planning: it retains interactive controls, state, bounds, allowed actions, short response-local refs, and best-effort stable keys. Text-heavy ocr/audio barely benefit from any reshaping (the text blob dominates), so reach for fields + max_content_length there. With no format/fields the response is unchanged JSON.

---

1. Search — GET /search

curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" \
  -H "X-Screenpipe-Client: api" \
  "http://localhost:3030/search?q=QUERY&content_type=all&limit=10&start_time=1h%20ago"

Parameters

Parameter	Type	Required	Description
q	string	No	Keywords. Do NOT use for audio searches — transcriptions are noisy, q filters too aggressively.
content_type	string	No	all (default), accessibility, audio, input, ocr, memory, parsed. Use parsed for compact app-specific messages, emails, tasks, documents, and code review. Parsed capture is experimental, may be empty when disabled/unsupported, and is not included in all. Screen text is primarily captured via the OS accessibility tree (accessibility); OCR is a fallback for apps without accessibility support.
limit	integer	No	Max 1-20. Default: 10
offset	integer	No	Pagination. Default: 0
start_time	ISO 8601, relative, or local calendar	Yes	Accepts 2024-01-15T10:00:00Z, 16h ago, today, yesterday, or YYYY-MM-DD
end_time	Same as start_time	No	Defaults to now
app_name	string	No	e.g. "Google Chrome", "Slack", "zoom.us"
window_name	string	No	Window title substring
frame_id	integer	No	With content_type=parsed, return parsed data attached to one frame.
actor_id	integer	No	With content_type=parsed, filter by a resolved actor identity.
speaker_name

Progressive Disclosure

Don't jump to heavy /search calls. Escalate:

Step	Endpoint	When
0	GET /memories?q=...	Always query first/in parallel — highest signal, lowest cost
1	GET /activity-summary?start_time=...&end_time=...	Broad questions ("what was I doing?", "which apps?")
2	GET /search?...	Need specific content
3	GET /elements?... or GET /frames/{id}/context	UI structure, buttons, links
4	GET /frames/{frame_id} (PNG)	Visual context needed

Decision tree:

• "What was I doing?" → Step 1 only
• "Summarize my meeting" → Step 2 with content_type=audio, NO q param. Add content_type=all for screen context.
• "How long on X?" → Step 1 (/activity-summary → total_active_minutes for the whole range, plus per-app/window minutes)
• "Which apps today?" → Step 1 (do NOT use frame counts or raw SQLite)
• "What button did I click?" → Step 3 (/elements with role=AXButton)
• "Show me what I saw" → Step 2 (find frame_id) → Step 4

Attached activity episodes

Chat messages can include [Context from activity episode: ...] with an exact Time range plus cited screen, audio, or meeting artifacts. Treat those values as retrieval anchors. The Activity title and Summary are generated labels, not captured content and not search terms.

• For questions about the episode's details, takeaways, decisions, or cause, fetch the underlying content before answering. Start with the exact Time range and no q: inspect cited screens with /frames/{frame_id}/context, query cited audio with content_type=audio, use the cited meeting id for its transcript, or query content_type=all for a mixed-source interval.
• Never derive q from the Activity title or Summary. Use q only when the user explicitly asks to locate a literal word or phrase.
• Analyze the fetched content. Do not merely restate the generated Summary.

Tags — linking people, projects, topics

Tags are a shared label layer across screen, audio, and memories under one string namespace. Use namespaced tags: person:ada, project:atlas, topic:pricing. Two items sharing a tag are connected.

• Add to a frame/audio: POST /tags/vision/{frame_id} or POST /tags/audio/{chunk_id} body {"tags":["person:ada"]}.
• Add to a memory: include tags in POST /memories (or PUT /memories/{id}).
• Retrieve by tag: GET /search?tags=person:ada&start_time=30d%20ago (screen+audio), or add content_type=memory for memories. Multiple tags AND together; matching is exact, not substring.

Frames are pruned by retention, so for a durable link tag a memory (memories also carry created_at and a frame_id back to the moment — jump there with GET /frames/{frame_id}). To pull everything about a person across time: one call for captures (content_type=all&tags=person:ada) plus one for facts (content_type=memory&tags=person:ada).

Add include_related=true to a tag query to get the surrounding context in the same response — the tags that co-occur with yours, grouped by namespace (prefix pluralized: person:→people, project:→projects) and ranked by frequency. Replaces the 2-3 follow-up "who/what else" calls with one:

curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" \
  "http://localhost:3030/search?tags=person:ada&include_related=true&limit=5"
# data: [...], related: { "people": ["connor","drew"], "projects": ["atlas"], "workflows": ["planning"] }

Critical Rules

Calendar ranges are local: today, yesterday, and bare YYYY-MM-DD dates mean the user's LOCAL calendar days in their timezone, not UTC days or rolling 24-hour ranges. Pass calendar literals directly to the API (start_time=today&end_time=now, start_time=yesterday&end_time=today). Never calculate midnight with date -u or append T00:00:00Z.

1. ALWAYS include start_time — queries without time bounds WILL timeout
2. Start with 1-2 hour ranges — expand only if no results
3. Use app_name when user mentions a specific app
4. Keep limit low (5-10) initially
5. "recent" = 30 min
6. If timeout, narrow the time range

Response Format

{
  "data": [
    {"type": "OCR", "content": {"frame_id": 12345, "text": "...", "timestamp": "...", "app_name": "Chrome", "window_name": "..."}},
    {"type": "Audio", "content": {"chunk_id": 678, "transcription": "...", "timestamp": "...", "speaker": {"name": "John"}}},
    {"type": "UI", "content": {"id": 999, "text": "Clicked 'Submit'", "timestamp": "...", "app_name": "Safari"}},
    {"type": "Parsed", "content": {"frame_id": 12345, "text": "compact corrected app data", "items": [], "actors": []}}
  ],
  "pagination": {"limit": 10, "offset": 0, "total": 42}
}

Note: The "OCR" type label is used for all screen text results, including text captured via the accessibility tree. Most screen text comes from accessibility, not OCR.

---

2. Activity Summary — GET /activity-summary

curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" \
  -H "X-Screenpipe-Client: api" \
  "http://localhost:3030/activity-summary?start_time=1h%20ago&end_time=now"

Returns a rich overview with:

• total_active_minutes: authoritative total active screen time for the whole range (every app, idle gaps excluded). Use this as the grand total / denominator. Do NOT sum windows[].minutes (capped at 30) and do NOT open db.sqlite to recompute durations — this field already is the answer.
• apps: per-app minutes (active time), first/last seen
• windows: every distinct window/tab with title, browser_url, and minutes spent — the most valuable field for what the user worked on (top 30 by time)
• key_texts: one representative text snippet per window context (user input fields prioritized over static page text)
• audio_summary.top_transcriptions: actual transcription text with speaker and timestamp (not just counts)

This is usually enough to answer "what was I doing?" without further searches. Only drill into /search if you need verbatim quotes or specific content.

Building a pipe/automation? Same rule: call this endpoint for time math. The numbers are computed server-side from frame timestamps — never recompute durations from raw frames, and never ask an LLM to sum minutes (it will drift). Let the model label activities; let this endpoint own the durations.

---

3. Elements — GET /elements

Lightweight FTS search across UI elements (~100-500 bytes each vs 5-20KB from /search).

curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/elements?q=Submit&start_time=1h%20ago&limit=10"

Parameters: q, frame_id, source (accessibility|ocr), role, start_time, end_time, app_name, limit, offset, plus format (json/csv/tsv/outline/automation) and fields (dotted paths). Elements are uniform rows, so this is where compact formats pay off most.

format=outline (alias tree) is the cheapest read for "what's on screen?" — a deduped, indented text tree of just the text-bearing nodes (drops empty structural nodes + bounds, collapses repeats into ×N, #id is the ref, inlines (disabled)/(selected)/(focused)/(expanded)/(off-screen) state, body capped). Best on source=accessibility (the common UI case — structural noise, repeated rows, hierarchy, state): 85–99% fewer tokens than JSON (o200k_base). Flat OCR text blocks are the floor (~67%, nothing to dedup) — for pure OCR format=csv&fields=text is about as good.

# compact outline — best default for an LLM reading the UI
curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/elements?q=Submit&format=outline&limit=30"
#   frame 12345 · accessibility · 8 text elements
#     AXButton "Submit Order" #4012
#     AXButton "Cancel" #4013 (disabled)
#     AXCell "Shipped" #4020 ×6

# columnar table when you need specific columns (e.g. bounds) instead
curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/elements?frame_id=12345&format=csv&fields=role,text,bounds.left,bounds.top"

GET /frames/{id}/elements?format=outline gives the whole frame's tree the same way (and is capped, unlike the raw JSON dump).

format=automation is for automation structure, not memory. It emits a snapshot revision, short ref=eN handles, best-effort key=k_* identities, key quality, state, normalized bounds, and allowed actions. Re-fetch it before every action. A best-effort key is matching evidence, not authority to act: verify key + role + name + bounds, and stop on key_quality=ambiguous. format=preferred follows the desktop AI context setting; the default setting keeps the read/memory outline.

curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/frames/12345/elements?format=automation"

Frame Context — GET /frames/{id}/context

Returns accessibility text, parsed nodes, and extracted URLs for a frame.

curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/frames/6789/context"

Common Roles (platform-specific)

Roles are not normalized across platforms. Use the correct format for the user's OS:

Concept	macOS	Windows	Linux
Button	AXButton	Button	Button
Static text	AXStaticText	Text	Label
Link	AXLink	Hyperlink	Link
Text field	AXTextField	Edit	Entry
Text area	AXTextArea	Document	Text
Menu item	AXMenuItem	MenuItem	MenuItem
Checkbox	AXCheckBox	CheckBox	CheckBox
Group	AXGroup	Group	Group
Web area	AXWebArea	Pane	DocumentWeb
Heading	AXHeading	Header	Heading
Tab	AXTab	TabItem	Tab
List item	AXRow	ListItem	ListItem

OCR-only roles (fallback when accessibility unavailable): line, word, block, paragraph, page

---

4. Frames (Screenshots) — GET /frames/{frame_id}

curl -o /tmp/frame.png "http://localhost:3030/frames/12345"

Returns raw PNG. Never fetch more than 2-3 frames per query (~1000-2000 tokens each).

---

5. Media Export — POST /export

Renders a real-time MP4 (screen frames at their true timestamps + synced microphone audio). The clip's duration matches the wall-clock span you ask for — it is NOT a sped-up timelapse.

curl -X POST http://localhost:3030/export \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" \
  -d '{"start": "5m ago", "end": "now"}'

Fields: start + end (ISO 8601 or relative like "2h ago", "now"; end defaults to now), OR meeting_id to export a whole meeting. Optional output_path writes the MP4 to a specific absolute path (e.g. ~/Downloads/clip.mp4); otherwise it lands in the data dir's exports/ folder.

Returns {"output_path": "...", "frame_count": N, "audio_chunk_count": N, "duration_secs": N, "file_size_bytes": N}. Show output_path as an inline code block for playback. Long ranges can take a few minutes.

Audio & ffmpeg

Audio files from search results (file_path). Common operations:

ffmpeg -y -i /path/to/audio.mp4 -q:a 2 ~/.screenpipe/exports/output.mp3          # convert
ffmpeg -y -i input.mp4 -ss 00:01:00 -to 00:05:00 -q:a 2 clip.mp3                 # trim
ffmpeg -y -i input.mp4 -filter:v "setpts=0.5*PTS" -an fast.mp4                    # speed 2x
ffmpeg -y -i input.mp4 -t 10 -vf "fps=10,scale=640:-1" output.gif                 # GIF

Always use -y, save to ~/.screenpipe/exports/.

---

6. Retranscribe — POST /audio/retranscribe

curl -X POST http://localhost:3030/audio/retranscribe \
  -H "Content-Type: application/json" \
  -d '{"start": "1h ago", "end": "now"}'

Optional: engine (whisper-large-v3-turbo|whisper-large-v3|deepgram|qwen3-asr), vocabulary (array of {"word": "...", "replacement": "..."} for bias/replacement), prompt (topic context for Whisper).

Keep ranges short (1h max). Show old vs new transcription.

---

7. Raw SQL — POST /raw_sql

curl -X POST http://localhost:3030/raw_sql \
  -H "Content-Type: application/json" \
  -d '{"query": "SELECT ... LIMIT 100"}'

Rules: Every SELECT needs LIMIT. Always filter by time. Read-only. For time math, see the timestamp caveat below.

Timestamp caveat: DB timestamps are stored as RFC3339 strings — usually 2026-06-26T18:01:14.214586+00:00 (frames / audio_transcriptions / ui_events), though some tables (e.g. meetings.meeting_start, memories) use a Z suffix with milliseconds: 2026-06-26T18:01:14.214Z. Do not compare either form directly to SQLite datetime() strings like timestamp > datetime('now', '-10 seconds'): the T vs space makes it a lexical string comparison and can include stale same-day rows. Use datetime(timestamp) > datetime('now', '-10 seconds') (works for both forms), or for indexed string comparisons use an RFC3339-shaped cutoff: timestamp > strftime('%Y-%m-%dT%H:%M:%f+00:00', 'now', '-10 seconds').

WARNING: Do NOT use frame counts for time estimates — frames are event-driven, not fixed-interval. Use /activity-summary for screen time.

Schema

Table	Key Columns	Time Column
frames	app_name, window_name, browser_url, focused	timestamp
ocr_text	text, app_name, window_name	join via frame_id
elements	source, role, text, bounds_*	join via frame_id
audio_transcriptions	transcription, device, speaker_id, is_input_device	timestamp
audio_chunks	file_path	timestamp
speakers	name, metadata	—
ui_events	event_type, app_name, window_title, browser_url	timestamp
accessibility	app_name, window_name, text_content, browser_url	timestamp
meetings	meeting_app, title, attendees, detection_source	meeting_start
memories	content, source, tags, importance	created_at

Example Queries

-- Most used apps (last 24h)
SELECT app_name, COUNT(*) as frames FROM frames
WHERE timestamp > strftime('%Y-%m-%dT%H:%M:%f+00:00', 'now', '-24 hours') AND app_name IS NOT NULL
GROUP BY app_name ORDER BY frames DESC LIMIT 20

-- Most visited domains
SELECT CASE WHEN INSTR(SUBSTR(browser_url, INSTR(browser_url, '://') + 3), '/') > 0
  THEN SUBSTR(SUBSTR(browser_url, INSTR(browser_url, '://') + 3), 1, INSTR(SUBSTR(browser_url, INSTR(browser_url, '://') + 3), '/') - 1)
  ELSE SUBSTR(browser_url, INSTR(browser_url, '://') + 3) END as domain,
COUNT(*) as visits FROM frames
WHERE timestamp > strftime('%Y-%m-%dT%H:%M:%f+00:00', 'now', '-24 hours') AND browser_url IS NOT NULL
GROUP BY domain ORDER BY visits DESC LIMIT 20

-- Speaker stats
SELECT COALESCE(NULLIF(s.name, ''), 'Unknown') as speaker, COUNT(*) as segments
FROM audio_transcriptions at LEFT JOIN speakers s ON at.speaker_id = s.id
WHERE at.timestamp > strftime('%Y-%m-%dT%H:%M:%f+00:00', 'now', '-24 hours')
GROUP BY at.speaker_id ORDER BY segments DESC LIMIT 20

-- Context switches per hour
SELECT strftime('%H:00', timestamp) as hour, COUNT(*) as switches
FROM ui_events WHERE event_type = 'app_switch' AND timestamp > strftime('%Y-%m-%dT%H:%M:%f+00:00', 'now', '-24 hours')
GROUP BY hour ORDER BY hour LIMIT 24

Common patterns: GROUP BY date(timestamp) (daily), GROUP BY strftime('%H:00', timestamp) (hourly), HAVING frames > 5 (filter noise).

---

8. Connections — GET /connections

# List all integrations (Telegram, Slack, Discord, Email, Todoist, Teams, 40+)
curl http://localhost:3030/connections

# Get connection status and non-secret settings
curl http://localhost:3030/connections/telegram

Connection reads return status and declared non-secret settings only. Stored secrets never appear in API responses. Use dedicated local endpoints or proxies:

• Telegram: POST /connections/telegram/send with {"text":"..."}
• n8n / Zapier / Make: POST /connections/<id>/proxy with arbitrary JSON
• Discord: POST /connections/discord/proxy with {"content":"..."}
• Teams webhook: POST /connections/teams/proxy with {"text":"..."}

API proxy integrations — credentials are stored server-side. Call the local wildcard proxy; it injects auth and forwards to the upstream API:

# GitHub — create an issue (repo owner/name from pipe settings)
curl -X POST http://localhost:3030/connections/github/proxy/repos/OWNER/REPO/issues \
  -H "Content-Type: application/json" \
  -d '{"title":"Found a bug","body":"Steps to reproduce..."}'

# GitHub — comment on an issue
curl -X POST http://localhost:3030/connections/github/proxy/repos/OWNER/REPO/issues/42/comments \
  -H "Content-Type: application/json" \
  -d '{"body":"Thanks for the report!"}'

# Generic OAuth proxy pattern (Zoom, Vercel, Google Docs, Microsoft 365, etc.)
curl -X POST http://localhost:3030/connections/<id>/proxy/<upstream-api-path> \
  -H "Content-Type: application/json" \
  -d '{...}'

Do not call https://api.github.com/... directly from a pipe — use /connections/github/proxy/... instead. There is no /connections/<id>/token endpoint.

If not connected, tell the user to set it up from the Connections page in the desktop app.

---

9. Meetings — GET /meetings, PUT /meetings/:id

curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/meetings?start_time=1d%20ago&end_time=now&limit=10&offset=0"
curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/meetings?q=alice%40acme.com"
curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/meetings/42"

# Update mutable fields. This is a partial update body: omitted fields stay as-is.
curl -X PUT http://localhost:3030/meetings/42 \
  -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"title":"Q3 planning", "note":"<existing note>\n\n## Summary\n<summary>"}'

Returns detected meetings (from calendar, app detection, window titles, UI elements, multi-speaker audio). q is a case-insensitive substring filter against title, attendees, and notes.

Meeting updates use PUT /meetings/:id, not PATCH. Before appending an AI-generated summary, read the current meeting first and include the existing note text in the new note body so user-written notes are preserved.

Field	Type	Description
id	integer	Meeting ID
meeting_start	ISO 8601	Start time
meeting_end	ISO 8601?	End time (null if ongoing)
meeting_app	string	App (zoom, teams, meet, etc.)
title	string?	Meeting title
attendees	string?	Attendees
note	string?	User notes / appended AI summaries
detection_source	string	How detected (app, calendar, ui, etc.)

Also available via raw SQL: SELECT * FROM meetings WHERE meeting_start > strftime('%Y-%m-%dT%H:%M:%f+00:00', 'now', '-24 hours') LIMIT 20

---

10. Speakers — Management & Reassignment

# Search speakers by name
curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/speakers/search?name=John"

# Get unnamed speakers (for labeling)
curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/speakers/unnamed?limit=20&offset=0"

# Get speakers similar to a given speaker (by voice embedding)
curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/speakers/similar?speaker_id=29&limit=5"

# Update speaker name/metadata
curl -X POST http://localhost:3030/speakers/update \
  -H "Content-Type: application/json" \
  -d '{"id": 29, "name": "Jordan"}'

# Reassign speaker for an audio chunk (propagates to similar chunks by default)
curl -X POST http://localhost:3030/speakers/reassign \
  -H "Content-Type: application/json" \
  -d '{"audio_chunk_id": 456, "new_speaker_name": "Jordan", "propagate_similar": true}'
# Returns: new_speaker_id, transcriptions_updated, old_assignments (for undo)

# Undo a speaker reassignment
curl -X POST http://localhost:3030/speakers/undo-reassign \
  -H "Content-Type: application/json" \
  -d '{"old_assignments": [{"transcription_id": 1, "old_speaker_id": 29}]}'

# Merge two speakers (keeps one, merges the other into it)
curl -X POST http://localhost:3030/speakers/merge \
  -H "Content-Type: application/json" \
  -d '{"speaker_to_keep_id": 5, "speaker_to_merge_id": 29}'

# Mark speaker as hallucination (false detection)
curl -X POST http://localhost:3030/speakers/hallucination \
  -H "Content-Type: application/json" \
  -d '{"speaker_id": 29}'

# Delete a speaker (also removes associated audio chunk files)
curl -X POST http://localhost:3030/speakers/delete \
  -H "Content-Type: application/json" \
  -d '{"id": 29}'

Speaker Reassignment Workflow

When the user says "that was actually Jordan, not Karishma":

1. Search audio results to find the chunk_id for the misidentified audio
2. Call POST /speakers/reassign with audio_chunk_id and new_speaker_name
3. With propagate_similar: true (default), it also fixes similar-sounding chunks

---

11. Parsed app data and actors

Parsed data uses the same search surface as every other readable content type:

curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" \
  "http://localhost:3030/search?content_type=parsed&start_time=2h%20ago&limit=10"

Results contain compact corrected text, typed items, parser provenance, and a separate actors array for correctable identities. Filter one frame with frame_id or one resolved identity with actor_id. Actor edits remain explicit: GET /semantic/actors/search, then POST /semantic/actors/create, update, merge, reassign, or aliases/reassign. Never merge actors by display name alone; the parser label is observed evidence, while the actor record is mutable.

---

12. Memories — High-Signal Persistent Knowledge

Memories are the highest-signal data source in screenpipe. They contain curated facts, user preferences, decisions, and project context — distilled from hours of screen/audio data. Always check memories when answering questions or building context.

When to Query Memories

Query memories FIRST (before or alongside /search) when:

• The user asks about preferences, decisions, or past context
• You need background on a project, person, or workflow
• You're generating a summary, recommendation, or action plan
• You're unsure about user preferences or past decisions
• Any task where historical context would improve the output

Rule: If you're calling /search, also call /memories in parallel. Memories provide the "why" behind the raw screen data. Search gives you what happened; memories tell you what matters.

API

# Search memories (FTS) — do this often!
curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/memories?q=preference&limit=20"

# List recent memories (high importance first)
curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/memories?min_importance=0.5&limit=20"

# Filter by source or tags
curl -H "Authorization: Bearer $SCREENPIPE_LOCAL_API_KEY" "http://localhost:3030/memories?source=user&tags=project&limit=20"

# Create a memory
curl -X POST http://localhost:3030/memories \
  -H "Content-Type: application/json" \
  -d '{"content": "User prefers dark mode", "source": "user", "tags": ["preference", "ui"], "importance": 0.7}'

# Update a memory
curl -X PUT http://localhost:3030/memories/1 \
  -H "Content-Type: application/json" \
  -d '{"content": "User prefers dark mode in all apps", "importance": 0.8}'

# Delete a memory
curl -X DELETE http://localhost:3030/memories/1

Parameters for GET /memories: q (FTS search), source, tags, min_importance, start_time, end_time, limit, offset.

Memories also appear in /search?content_type=memory.

Creating Memories

When you learn something important about the user (preferences, decisions, project context), store it as a memory. Use importance 0.0-1.0 to rank signal. Only store genuinely useful long-lived facts, not transient observations.

---

12. Notifications — POST http://localhost:11435/notify

Send a notification to the screenpipe desktop UI. This uses the Tauri sidecar server (port 11435), not the main API (port 3030).

The notification body supports markdown: **bold**, `inline code`, and [link text](url). Links can be web URLs, file paths, or screenpipe deeplinks.

Set priority to high, normal (default), or low. Every priority appears in the top-right notification panel. Reserve high for a time-sensitive failure or a decision that genuinely needs the human now; it also appears in the focused Priority inbox. normal stays in All, while low is toast-only by default. Completion logs and routine syncs should never be high.

# Simple notification
curl -X POST http://localhost:11435/notify \
  -H "Content-Type: application/json" \
  -d '{"title": "3 new voice memos", "body": "found recordings from today"}'

# Markdown body with links
curl -X POST http://localhost:11435/notify \
  -H "Content-Type: application/json" \
  -d '{"title": "Meeting summary", "body": "**Q3 Planning** notes saved\n\nopen [meeting notes](~/Documents/notes/q3.md) or view [recording](screenpipe://timeline)"}'

# Link to a local file (absolute path or ~ path)
curl -X POST http://localhost:11435/notify \
  -H "Content-Type: application/json" \
  -d '{"title": "Export complete", "body": "saved to [report.csv](~/Downloads/report.csv)"}'

# With action buttons
# Use `type: "link"` for external URLs and `type: "deeplink"` for
# screenpipe:// in-app routes. `type: "dismiss"` closes the notification.
curl -X POST http://localhost:11435/notify \
  -H "Content-Type: application/json" \
  -d '{"title": "Meeting summary", "body": "**Q3 Planning**\n- Budget approved", "actions": [{"id": "view", "label": "view", "type": "deeplink", "url": "screenpipe://timeline"}, {"id": "skip", "label": "skip", "type": "dismiss"}]}'

# External URL action (opens in browser)
curl -X POST http://localhost:11435/notify \
  -H "Content-Type: application/json" \
  -d '{"title": "PR ready for review", "body": "nice work", "actions": [{"id": "open", "label": "open pr", "type": "link", "url": "https://github.com/screenpipe/screenpipe/pull/1234"}]}'

# Ask permission, then run a pipe on approval — the opt-in / agent-gated flow.
# `type: "pipe"` runs the TARGET pipe when clicked (POST /pipes/<pipe>/run); the
# `context` is injected into that pipe's prompt as the notification action
# context. Set `pipe` EXPLICITLY — if omitted it falls back to the sending pipe,
# which usually does nothing. Add `"open_in_chat": true` to run it in the chat UI
# so the user sees the output live instead of in the background.
# Actions persist into the notification bell, so the user can still approve from
# the bell after the ~20s toast fades.
curl -X POST http://localhost:11435/notify \
  -H "Content-Type: application/json" \
  -d '{"title": "share meeting notes with the team?", "body": "approve to send the adriaan call notes", "priority": "high", "actions": [{"id": "approve", "label": "approve", "type": "pipe", "primary": true, "pipe": "share-data", "context": {"meeting_id": 274}}, {"id": "decline", "label": "decline", "type": "dismiss"}]}'

# Run an inline prompt in a fresh chat session on click (`type: "chat"`).
# No pre-installed pipe needed — write the whole task in `prompt`, attach data
# in `context`. Add `"auto_send": false` to pre-fill chat for the user to
# review/edit before sending. This is the lightweight counterpart to a `pipe`
# action for one-off "approve → do this specific thing" flows.
curl -X POST http://localhost:11435/notify \
  -H "Content-Type: application/json" \
  -d '{"title": "summarize this call into a CRM note?", "body": "approve to draft it", "priority": "high", "actions": [{"id": "go", "label": "draft it", "type": "chat", "primary": true, "prompt": "summarize meeting 274 into a short CRM follow-up note and save it to output/", "context": {"meeting_id": 274}}, {"id": "no", "label": "no", "type": "dismiss"}]}'

# Call a local API endpoint on click (`type: "api"`)
curl -X POST http://localhost:11435/notify \
  -H "Content-Type: application/json" \
  -d '{"title": "stop recording?", "body": "tap to stop", "actions": [{"id": "stop", "label": "stop", "type": "api", "url": "/recording/stop", "method": "POST"}]}'

# Custom auto-dismiss (5 seconds)
curl -X POST http://localhost:11435/notify \
  -H "Content-Type: application/json" \
  -d '{"title": "Saved", "body": "Note saved", "timeout": 5000}'

Field	Type	Required	Description
title	string	Yes	Notification title
body	string	Yes	Markdown body (**bold**, `code`, [text](url))
type	string	No	Category (default "pipe")
priority	high | normal | low	No	Default normal; all priorities appear top-right, while only high enters the focused Priority view
timeout	integer	No	Auto-dismiss in ms (default 20000)
autoDismissMs	integer	No	Alias for timeout
actions	array	No	Action buttons (up to 5; each needs id, label, type)

Action button types:

• link — open a web URL in the browser (url)
• deeplink — navigate within screenpipe (url = screenpipe://...)
• pipe — run an installed pipe on click (pipe = target pipe name, optional context injected into its prompt, optional open_in_chat). The opt-in / agent-gated-sharing primitive.
• chat — run an inline prompt in a fresh chat session (no installed pipe needed; optional context as background data, optional auto_send default true). Lightweight counterpart to pipe for one-off approve-and-do flows.
• api — POST a local endpoint (url, optional method, optional body)
• dismiss — close the notification, no side effect
• primary: true renders the button filled (the recommended action). Actions persist into the notification bell, so a missed toast can still be acted on.

Supported link types in body markdown:

• Web URLs: [docs](https://docs.screenpi.pe) — opens in browser
• File paths: [notes](~/notes/file.md) or [log](/var/log/app.log) — opens in default app
• Deeplinks: [timeline](screenpipe://timeline) — navigates within screenpipe

Returns {"success": true, "message": "Notification sent successfully"}.

---

13. Other Endpoints

curl http://localhost:3030/health              # Health check
curl http://localhost:3030/audio/list           # Audio devices
curl http://localhost:3030/vision/list          # Monitors

---

Deep Links

Reference specific moments with clickable links:

[10:30 AM — Chrome](screenpipe://frame/12345)           # screen text results (use frame_id)
[meeting at 3pm](screenpipe://timeline?timestamp=ISO8601) # Audio results (use timestamp)

Only use IDs/timestamps from actual search results. Never fabricate.

Showing Videos

Show file_path from search results as inline code for playable video:

`/Users/name/.screenpipe/data/monitor_1_2024-01-15_10-30-00.mp4`