total-recallThe only memory skill that watches on its own. No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into priorit...
Install via ClawdBot CLI:
clawdbot install gavdalf/total-recallThe only memory skill that watches on its own.
No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into prioritised notes, consolidates when they grow, and recovers anything missed. Five layers of redundancy, zero maintenance. ~$0.10/month.
While other memory skills ask you to remember to remember, this one just pays attention.
Layer 1: Observer (cron, every 15-30 min)
â compresses recent messages â observations.md
Layer 2: Reflector (auto-triggered when observations > 8000 words)
â consolidates, removes superseded info â 40-60% reduction
Layer 3: Session Recovery (runs on every /new or /reset)
â catches any session the Observer missed
Layer 4: Reactive Watcher (inotify daemon, Linux only)
â triggers Observer after 40+ new JSONL writes, 5-min cooldown
Layer 5: Pre-compaction hook (memoryFlush)
â emergency capture before OpenClaw compacts context
observations.md with priority levels (đ´ high, đĄ medium, đĸ low)clawdhub install total-recall
Add to your .env or OpenClaw config:
OPENROUTER_API_KEY=sk-or-v1-xxxxx
bash skills/total-recall/scripts/setup.sh
This will:
memory/, logs/, backups)Add to your agent's workspace context (e.g., MEMORY.md or system prompt):
At session startup, read `memory/observations.md` for cross-session context.
Or use OpenClaw's memoryFlush.systemPrompt to inject a startup instruction.
| Platform | Observer + Reflector + Recovery | Reactive Watcher |
|----------|-------------------------------|-----------------|
| Linux (Debian/Ubuntu/etc.) | â Full support | â With inotify-tools |
| macOS | â Full support | â Not available (cron-only) |
All core scripts use portable bash â stat, date, and md5 commands are handled cross-platform via _compat.sh.
All scripts read from environment variables with sensible defaults:
| Variable | Default | Description |
|----------|---------|-------------|
| OPENROUTER_API_KEY | (required) | OpenRouter API key for LLM calls |
| MEMORY_DIR | $OPENCLAW_WORKSPACE/memory | Where observations.md lives |
| SESSIONS_DIR | ~/.openclaw/agents/main/sessions | OpenClaw session transcripts |
| OBSERVER_MODEL | google/gemini-2.5-flash | Primary model for compression |
| OBSERVER_FALLBACK_MODEL | google/gemini-2.0-flash-001 | Fallback if primary fails |
| OBSERVER_LOOKBACK_MIN | 15 | Minutes to look back (daytime) |
| OBSERVER_MORNING_LOOKBACK_MIN | 480 | Minutes to look back (before 8am) |
| OBSERVER_LINE_THRESHOLD | 40 | Lines before reactive trigger (Linux) |
| OBSERVER_COOLDOWN_SECS | 300 | Cooldown between reactive triggers (Linux) |
| REFLECTOR_WORD_THRESHOLD | 8000 | Words before reflector runs |
| OPENCLAW_WORKSPACE | ~/clawd | Workspace root |
memory/
observations.md # The main observation log (loaded at startup)
observation-backups/ # Reflector backups (last 10 kept)
.observer-last-run # Timestamp of last observer run
.observer-last-hash # Dedup hash of last processed messages
logs/
observer.log
reflector.log
session-recovery.log
observer-watcher.log
The setup script creates these OpenClaw cron jobs:
| Job | Schedule | Description |
|-----|----------|-------------|
| memory-observer | Every 15 min | Compress recent conversation |
| memory-reflector | Hourly | Consolidate if observations are large |
The reactive watcher uses inotifywait to detect session activity and trigger the observer faster than cron alone. It requires Linux with inotify-tools installed.
On macOS, the watcher is not available â the 15-minute cron provides full coverage.
# Install inotify-tools (Debian/Ubuntu)
sudo apt install inotify-tools
# Check watcher status
systemctl --user status total-recall-watcher
# View logs
journalctl --user -u total-recall-watcher -f
Using Gemini 2.5 Flash via OpenRouter:
observations.md/new or /resetinotifywait to monitor session directoryThe observer and reflector system prompts are in prompts/:
prompts/observer-system.txt â controls how conversations are compressedprompts/reflector-system.txt â controls how observations are consolidatedEdit these to match your agent's personality and priorities.
This system is inspired by how human memory works during sleep â the hippocampus (observer) captures experiences, and during sleep consolidation (reflector), important memories are strengthened while noise is discarded.
Read more: Your AI Has an Attention Problem
"Get your ass to Mars." â Well, get your agent's memory to work.
Generated Feb 25, 2026
An AI agent handles customer inquiries across multiple sessions, using Total Recall to retain key details like user preferences, past issues, and resolutions. This ensures consistent, personalized support without manual data entry, improving response accuracy and customer satisfaction.
An AI agent aids researchers by summarizing conversations, tracking hypotheses, and consolidating findings into prioritized notes. Total Recall autonomously compresses and organizes data, enabling efficient recall of critical insights across long-term projects without database management.
An AI agent manages project updates and team discussions, using Total Recall to capture task assignments, deadlines, and decisions. The autonomous memory system ensures no details are lost during context shifts, facilitating seamless handoffs and reducing administrative overhead.
An AI agent provides mental health support by remembering user history, emotional patterns, and progress over sessions. Total Recall compresses conversations into notes, allowing the agent to offer continuity and personalized care without violating privacy through external databases.
An AI agent engages with leads across multiple interactions, using Total Recall to track prospect interests, objections, and follow-ups. This enables tailored outreach and improves conversion rates by maintaining context without manual logging or vector storage.
Offer Total Recall as a premium add-on for AI agent platforms, charging a monthly fee based on usage tiers. This model leverages low operational costs (~$0.10/month) to generate recurring revenue from users needing persistent, autonomous memory without infrastructure management.
Provide a free basic version with limited observation frequency, then upsell to paid plans for advanced features like faster reactive triggers or higher priority notes. This attracts users with zero upfront cost while monetizing heavy usage and cross-session recall needs.
License Total Recall to other AI developers or companies for embedding into their products, such as chatbots or virtual assistants. This generates revenue through licensing fees while allowing clients to customize the memory system under their own branding.
đŦ Integration Tip
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