🔔 agentrem

Structured reminders for AI agents. Persistent, searchable, works across sessions.

Instant Start

npx agentrem add "Deploy to prod" --due tomorrow --priority 2
npx agentrem check
npx agentrem list

Related MCP server: Selti

For AI Agents

Copy this into your CLAUDE.md / AGENTS.md (or run agentrem setup to generate it):

## Reminders
You have access to `agentrem` CLI for persistent reminders across sessions.

### On every session start, run:
agentrem check --type time,session --budget 800

### When the user says "remind me", "don't forget", "follow up", or "next time":
agentrem add "<content>" --due "<when>" --priority <1-5> --tags "<tags>"

### Key commands:
- `agentrem add` — create a reminder
- `agentrem check` — see what's triggered/due
- `agentrem check --watch` — block until next reminder fires
- `agentrem list` — list all active reminders
- `agentrem search <query>` — full-text search
- `agentrem complete <id>` — mark done
- `agentrem snooze <id> --for 2h` — snooze
- `agentrem --help` — full reference

MCP Server

For Claude Desktop and any MCP client — add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "agentrem": {
      "command": "agentrem-mcp",
      "args": []
    }
  }
}

No global install? Use npx:

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

Run agentrem setup --mcp to print this config. MCP tools: add_reminder · check_reminders · list_reminders · search_reminders · complete_reminder · snooze_reminder · edit_reminder · delete_reminder · get_stats · get_history · undo_change · garbage_collect · export_reminders · import_reminders

All Commands

--json is available on check, list, search, stats, history, doctor — use it for structured output in your agent.

Trigger Types

Priority Levels

Natural Language Dates

--due, --until, and --decay all accept natural language:

--due "now"                   # Immediately
--due "today"                 # Today at 23:59
--due "tomorrow"              # Tomorrow at 09:00
--due "in 5 minutes"
--due "in 2 hours"
--due "in 3 days"
--due "in 1 week"
--due "+5m"                   # Short relative
--due "+2h"
--due "+3d"
--due "+1w"
--due "2026-04-01T09:00:00"   # ISO datetime
--due "2026-04-01"            # ISO date

check --watch: Blocking Mode

agentrem check --watch blocks until the next due reminder fires. Useful for scripting, pipelines, or pausing an agent until something needs attention.

# Wait indefinitely for next reminder
agentrem check --watch

# Exit 1 if nothing fires within 5 minutes
agentrem check --watch --timeout 300

# Get the full reminder as JSON when it fires
agentrem check --watch --json

# Filter by trigger type and agent
agentrem check --watch --type time,heartbeat --agent jarvis --timeout 60

Exit codes: 0 = reminder found (or SIGINT/SIGTERM), 1 = timeout elapsed with no reminder.

Note: --watch does not update fire counts. Use a regular agentrem check after to actually mark reminders as fired.

Poll-then-act pattern:

if agentrem check --watch --timeout 120 --json > /tmp/due.json; then
  echo "Reminder fired:"
  cat /tmp/due.json
  agentrem check   # mark as fired
fi

watch --on-fire: Hooks

⚠️ Security: The --on-fire command runs with your user's permissions. Only use trusted commands. Reminder data is passed via environment variables (never shell-interpolated) to prevent injection.

Execute a shell command whenever a reminder fires:

agentrem watch --on-fire "curl -X POST https://hooks.example.com/reminder"

Reminder data is passed as environment variables (no shell injection — data never interpolated into the command):

• Fire-and-forget — failures are logged to ~/.agentrem/logs/on-fire.log, never crash the watcher

• Sequential — multiple reminders process one at a time

• Timeout: 5 seconds default, configurable with --on-fire-timeout <ms>

Built-in presets — skip the shell command entirely:

agentrem watch --on-fire-preset openclaw   # auto-delivers to your OpenClaw agent

Or craft your own:

agentrem watch --on-fire 'curl -X POST https://hooks.example.com/reminder -d "text=$AGENTREM_CONTENT"'

Background Watcher

agentrem watch polls for due reminders and fires native OS notifications.

agentrem watch                           # Poll every 30s (foreground)
agentrem watch --interval 60             # Custom interval
agentrem watch --once                    # Single check and exit
agentrem watch --agent jarvis            # Watch for a specific agent
agentrem watch --verbose                 # Show poll log

# Install as OS service (auto-start on boot)
agentrem watch --install
agentrem watch --install --interval 60
agentrem watch --status
agentrem watch --uninstall

Service files: macOS → ~/Library/LaunchAgents/com.agentrem.watch.plist · Linux → ~/.config/systemd/user/agentrem-watch.service · Logs → ~/.agentrem/logs/watch.log

Native Notifications 🔔

On macOS, agentrem ships a bundled Swift app (Agentrem.app) that runs as a singleton process — notifications appear under "agentrem" with a bell icon.

Notification behavior:

• Click body → notification re-appears (won't dismiss until you act on it)

• Complete ✅ → marks reminder complete and dismisses (the only way to complete a fired reminder)

• Multiple reminders → single process handles all via IPC

• Fallback chain: Agentrem.app → terminal-notifier → osascript → console

To rebuild the Swift app: npm run build:notify

Programmatic API

Use agentrem directly from JavaScript/TypeScript — no CLI subprocess needed.

npm install agentrem

import { add, check, list, complete, snooze, search, stats } from 'agentrem';
import type { Reminder } from 'agentrem';

// Add a reminder
const rem = await add('Review PR #42', { due: 'tomorrow', priority: 2, tags: 'pr,review' });

// Check for triggered reminders (session start pattern)
const { included, totalTriggered } = await check({ type: 'time,session', budget: 800 });
for (const r of included) {
  console.log(`[P${r.priority}] ${r.content}`);
}

// List active reminders
const reminders = await list({ limit: 20 });

// Complete a reminder
const done = await complete(rem.id, 'Reviewed and merged');

// Snooze a reminder
const snoozed = await snooze(rem.id, { for: '2h' });

// Full-text search
const results = await search('deploy staging');

// Get statistics
const s = await stats();
console.log(`${s.totalActive} active, ${s.overdue} overdue`);

All API functions are async and return full Reminder objects. The database is auto-initialized on first call (no manual init needed).

See llms-full.txt for complete type signatures and all options.

Why agentrem?

# vs flat files / memory.md
agentrem check --json   # structured output your agent can parse; memory.md can't do that

• Persistent across sessions — SQLite-backed, survives restarts, not just in-context notes

• Priority-aware + token budgets — check --budget 800 fits within any context window without overflow

• Triggerable — time, keyword, condition, session, heartbeat triggers; not just static lists

• Blocking watch mode — check --watch lets agents pause until something needs attention

• Agent-native — --json everywhere, --agent namespacing, MCP server for chat clients

Install

npm install -g agentrem

The database auto-initializes on first use. Run agentrem setup to get your CLAUDE.md snippet, or agentrem setup --mcp for Claude Desktop.

MIT License