daybrain

DayBrain

Private local memory for any AI assistant.

Give Claude, Cursor, ChatGPT, OpenCode, and any MCP client persistent context about your workday — without sending your data anywhere.

The problem

AI assistants know everything about the internet. They know nothing about you. You spend 30 seconds re-telling Claude what you've been working on — every conversation, every day.

What DayBrain does

DayBrain runs on your machine as a background process. It watches what you work on, stores it in a local SQLite database, extracts useful insights, and exposes everything through the Model Context Protocol (MCP). Any AI tool that speaks MCP can query it.

No cloud. No accounts. No new chat app.

npx daybrain

Architecture

┌──────────────┐     ┌────────────────┐     ┌──────────────────┐
│  Activity    │────▶│                │────▶│  Any MCP client  │
│  sources     │     │   DayBrain     │     │  Claude, Cursor,  │
│              │     │                │     │  OpenCode, etc.   │
├──────────────┤     ├────────────────┤     └──────────────────┘
│ CGWindow API │     │  SQLite DB     │
│ Chrome ext.  │     │  Insight engine│     ┌──────────────────┐
│ ActivityWatch│     │  Scheduler     │────▶│  Telegram/Slack   │
│ Screenpipe   │     │  MCP server    │     │  (push insights)  │
└──────────────┘     └────────────────┘     └──────────────────┘

Features

• 11 MCP tools — AI can query activity, commitments, patterns, baselines, and project context
• Native window tracking — macOS CGWindow API, zero permissions required
• Chrome extension — records browsing activity, one-click copy context anywhere
• Commitment detection — 9 regex patterns catch promises from window titles ("I'll send the demo by Friday")
• Context switching analysis — how often you jump between apps, which apps interrupt your flow
• Baseline comparison — compares today vs 7-day average, flags anomalies
• Focus block detection — identifies periods of sustained work on a single app
• Project context detection — groups activity by project keywords found in window titles
• Insight scheduling — hourly insight loop with overlap guard, no duplicated runs
• Linear + GitHub integration — create tasks from detected commitments
• Cross-device sync — push/pull endpoints for sharing context across your machines
• 100% local — SQLite at ~/.daybrain/data/daybrain.db, nothing leaves your device

MCP tools

The full list of tools any AI client can call:

Tool	Description	Required params
get_activity_summary	Today's activity breakdown with top apps and total time	period (today/week/month)
get_raw_events	Recent window/browser events with timestamps	limit
get_insights	Detected commitments, patterns, and anomalies	limit, min_confidence
get_daily_summary	Aggregated stats for a specific date	date (YYYY-MM-DD)
get_baseline	Current vs historical averages, flags anomalies	—
get_context	Full workday context for injection into AI chat	period, include_raw
create_linear_task	Create a Linear issue from a detected commitment	title, description?
create_github_issue	Create a GitHub issue from a detected commitment	title, body?, repo
search_activity	Full-text search across window titles	query, limit
get_project_context	Activity grouped by detected project keywords	keywords
get_server_status	Health check + data directory info	—

Quick start

npx daybrain              # start server (MCP + HTTP on :19840)

Connect to any AI

{
  "mcpServers": {
    "daybrain": {
      "command": "npx",
      "args": ["-y", "daybrain"]
    }
  }
}

Then ask: "What did I work on today?"

Chrome extension

• Open chrome://extensions → enable Developer mode
• Load unpacked → select /extension folder
• Click the DayBrain icon → toggle recording → browse normally
• Click "Copy context" → paste into any AI chat

Activity sources

Source	Platform	What it captures	Setup
CGWindow API	macOS	Active window titles and app names	Zero — built in
Chrome extension	All	Browsing activity, URLs, page titles	Load unpacked
ActivityWatch	All	Apps, window titles, AFK detection	Install aw-watcher-afk + aw-watcher-window
Screenpipe (optional)	macOS/Linux	Screen OCR + audio transcription	Install Screenpipe, set SCREENPIPE_ENABLED=true

What it detects

Pattern	Example match	Confidence
Commitments	"I will send the report by Friday"	80-90%
Deadlines	"due by EOD", "needs to ship tomorrow"	75%
Follow-ups	"let me follow up on that", "I'll check"	70%
Promises	"I promise to fix", "I will deploy"	85%
Avoidance	Frequent brief visits to social/distraction apps	70%
Context switches	87 app switches in one hour	75%
Focus blocks	30+ consecutive minutes in one app	80%

Tests

npm test

21 passed, 0 failed

Covers: config loading, database CRUD, deduplication, insight engine (commitments, avoidance, app categorization, project detection, focus blocks, summarization, empty-input safety, large-input performance), scheduler overlap guard, security (config permissions 0600, path traversal blocked, immutable config clones), error handler registration.

Tech stack

• Runtime: Node.js (TypeScript, compiled to CommonJS)
• Database: SQLite via better-sqlite3 (synchronous, no network)
• Protocol: MCP (Model Context Protocol) via @modelcontextprotocol/sdk
• Window tracking: macOS CGWindow API via Python pyobjc
• Browser: Chrome Extension Manifest V3
• Testing: Custom assertion runner (tsx test.ts)

License

MIT