Screenpipe vs Throughline

A side-by-side look at the Screenpipe and Throughline MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.

Screenpipe logoScreenpipeKnowledge & Memory · Local subprocess (stdio)Local-first workflow memory for AI agents. screenpipe lets MCP clients search selected screen, audio, app, and meeting context and turn real work into cited notes, SOPs, workflow reports, and automation candidates.72GreatAuth: no auth required
T
ThroughlineKnowledge & Memory
A persistent AI self you own — memory, persona, judgment — portable across all your AI hosts.37FairAuth: not declared

Verdict: choose Screenpipe for knowledge & memory work as a local process; choose Throughline for knowledge & memory. You can also run both side by side in one mcpServers block.

Which MCP server should you choose?

Screenpipe logoChoose Screenpipe when

  • You need dedicated capabilities in the Knowledge & Memory domain.
  • You prefer a local stdio subprocess transport.
  • Its auth model fits you: No auth required.
  • You want tools like search-content, synced-devices, search-synced-content.
Explore Screenpipe

T
Choose Throughline when

  • You need dedicated capabilities in the Knowledge & Memory domain.
Explore Throughline

Feature & specification comparison

Rows where the two servers differ are marked.

SpecScreenpipe logoScreenpipe
T
Throughline
CategoryKnowledge & MemoryKnowledge & Memory
Quality signal72/100 · Great37/100 · Fair
Install commandConfirmedNot confirmed yet
TransportLocal subprocess (stdio)—
AuthNo auth requiredNot declared
PricingFreemiumNot declared
Required env varsNone listedNone listed
Verified ownerCommunity listingCommunity listing
GitHub stars22,033—
Directory activity 3 views 0 installs 0 upvotes 0 views 0 installs 0 upvotes
Tools30Not listed

Tools & capabilities

Screenpipe tools 30

  • search-contentSearch screen text, audio transcriptions, input events, memories, and parsed app data. Returns timestamped results with app context. USE WHEN: you need the actual text/content of a moment — quotes, screen text, transcript lines, or compact parsed messages, emails, tasks, documents, and code review — or want to filter by speaker/window. DO NOT USE for: broad questions like 'what was I doing?' (use activity-summary, it pre-summarizes apps + windows + transcripts). Also DO NOT USE for: targeted UI controls (use search-elements). Start with limit=5, increase only if needed. Per-result text is auto-truncated to 1000 chars; pass max_content_length=0 to opt out, or a custom integer to override.
  • synced-devicesList this signed-in user's Screenpipe devices that have uploaded Data Sync records, including each device name and last sync time. USE WHEN: the user asks what devices are available, names another device, or asks a cross-device question and you need the exact device_name filter. This never accepts an account or bucket identifier; the local app forwards the signed-in user's identity.
  • search-synced-contentSearch Data Sync records from this signed-in user's devices. Results include device name, device ID, and timestamp for attribution. USE WHEN: the user asks about another/named device, asks across devices, or local search does not cover the requested machine. For the current machine only, use search-content. Start with a narrow time range and limit=10.
  • list-meetingsList detected meetings (Zoom, Teams, Meet, etc.) with id, duration, app, attendees, and note status. Pass `q` to substring-match title, attendee names/emails, and notes — `q` searches ALL meeting history, so when looking for a meeting with a person or on a topic ('when did I last talk to Noah?'), pass `q` and OMIT start_time. Only constrain the time range when the question itself is time-bound. Results are newest-first; without `q`, old meetings only surface via time range or offset pagination. Follow up with get-meeting (id from results) for the full note and transcript.
  • activity-summaryRich activity overview: authoritative active minutes, app/window time, edited document paths, key text, and audio transcriptions, with optional parsed task context when available. USE WHEN: any broad question about what the user did — 'what was I doing?', 'how long on X?', 'which apps?', 'recap my morning'. This is almost always the right first call for time-range questions — usually sufficient without follow-up searches. Use parsed/path evidence to identify tasks, but only active-minute fields for duration; frame and row counts are never time. DO NOT USE for: finding a specific keyword (use keyword-search) or a specific UI control (use search-elements).
  • search-elementsSearch UI elements (buttons, links, text fields) from the accessibility tree, filterable by role. USE WHEN: you want a specific UI control or page-structure question — 'find every Submit button I saw', 'list the links in that page'. DO NOT USE for: general text/content (use search-content) or fast keyword lookup (use keyword-search).
  • frame-contextGet full accessibility text, parsed tree nodes, and URLs for a specific frame ID. Use after search-content to get detailed context for a specific moment.
  • export-videoExport an MP4 of screen recordings for a time range, with synced microphone audio. Frames are placed at their real timestamps, so the clip's duration matches the wall-clock span you requested (not a sped-up timelapse). Returns the file path. Can take a few minutes for long ranges.
  • update-memoryCreate, update, or delete a persistent memory (facts, preferences, decisions the user wants to remember). To retrieve memories, use search-content with content_type='memory'. To create: provide content + tags. To update: provide id + fields to change. To delete: provide id + delete=true.
  • get-feedbackSearch local user ratings and written comments attached to AI-produced notifications, chats, memories, blocks, artifacts, and other targets. Use before generating related work so you preserve what earned up ratings and correct what earned down ratings.
  • send-notificationSend a notification to the screenpipe desktop UI. Use high priority only for time-sensitive failures or decisions needing human attention; routine findings and completed tasks should be normal or low.
  • health-checkCheck if screenpipe is running and healthy. Returns recording status, frame/audio stats, timestamps.
  • +18 more on the listing page

Throughline tools

No tool names declared yet. Check the project README from the listing page.

Ready-to-paste client configs

Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.

Screenpipe

mcpServers (Claude Desktop / Cursor)
{
  "mcpServers": {
    "screenpipe": {
      "command": "npx",
      "args": [
        "-y",
        "screenpipe-mcp@latest"
      ]
    }
  }
}

Throughline

No confirmed install command for Throughline yet — follow the setup steps linked from its listing page rather than a guessed package name.

Frequently asked questions

Screenpipe is categorized under Knowledge & Memory and uses a local stdio subprocess. Throughline belongs to Knowledge & Memory and uses an install path we have not confirmed yet. Pick Screenpipe for knowledge & memory work and Throughline for knowledge & memory.

Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client. Throughline has no confirmed install command yet — follow its own setup instructions.

Screenpipe is listed as freemium — a free tier with paid plans. Auth: no auth required. Throughline has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.

Yes. MCP clients support multiple servers: add screenpipe and throughline as separate keys in the same mcpServers object, and your assistant will call tools from either one.

On AllMCPs, Screenpipe has a quality signal of 72/100 (Great) with 3 views, 0 installs and 22,033 GitHub stars. Throughline has 37/100 (Fair) with 0 views, 0 installs and 0 GitHub stars.