CatchMe

🦞 Makes Your Agents Truly Personal. CatchMe ships as an agent-compatible skill for CLI agents (OpenClaw, NanoBot, Claude, Cursor, etc.). Run CatchMe independently. Your agents query memories via CLI commands only.

🎯 Enrich Your Personal Digital Context

✨ Key Features

📹 Always-On Event Capture

• Event-Driven Recording: No timer or delays - catch mouse actions with crosshair annotation instantly.
• Comprehensive Context: Five recorders track windows, keyboard, clipboard, notifications, and files around mouse actions.

🌲 Intelligent Memory Hierarchy

• Auto-Organization: Raw streams structure into five tiers: Day → Session → App → Location → Action.
• Smart Summaries: LLM summaries at each level, transforming logs into searchable knowledge trees.

🔍 Tree-Based Retrieval

• No Vector Complexity: Skip embeddings and VDBs — our system uses tree-based reasoning for navigation.
• Top-Down Search: LLM reads summaries, selects relevant branches, and drills down to evidence.

🤖 Zero-Config Agent Integration

• One-File Setup: Drop a single skill file into any AI agent for instant integration.
• Immediate Access: CLI-based screen history queries with zero configuration required.

🪶 Ultralight & Privacy-First

• Minimal Footprint: ~0.2GB runtime RAM with efficient SQLite + FTS5 storage.
• Local & Offline: All data stays on your machine with full offline mode via Ollama/vLLM/LM Studio.

🖥️ Rich Web Interface

• Visual Exploration: Interactive timelines, memory tree navigation, and real-time system monitoring.
• Natural Conversation: Chat with your complete digital footprint using natural language.

💡 CatchMe Architecture

CatchMe transforms raw digital activity into structured, searchable memory through three concurrent stages:

🔄 Record → Organize → Reason: Turn digital chaos into queryable memory

Capture. Six background recorders silently track your activity. They monitor window focus, keystrokes, mouse movement, screenshots, clipboard, and notifications.

Index. Raw events auto-organize into a Hierarchical Activity Tree: Day → Session → App → Location → Action. Each node gets LLM-generated summaries. Fast, meaningful recall without vector embeddings.

Retrieve. You ask a question. The LLM traverses your memory tree top-down. It selects relevant nodes and inspects raw data like screenshots or keystrokes. Then synthesizes a precise answer.

🌲 Hierarchical Activity Tree

The Activity Tree is CatchMe's memory core. It provides structured, multi-level views of your digital life. Browse high-level summaries or dive into granular details.

🔍 Intelligent Tree Retrieval

CatchMe skips traditional vector search. Instead, the LLM directly navigates your Activity Tree. This enables complex, cross-day reasoning. Precise evidence gathering from raw activity history.

📖 Learn More: Detailed design insights and technical deep-dive available in our blog.

🧠 LLM Configuration

❗️ Data Privacy Notice

• 100% Local Storage: All raw data (screenshots, keystrokes, activity trees) stays in ~/data/ and never leaves your machine.

• Offline-First Options: Local LLMs (Ollama, vLLM, LM Studio) enable fully offline operation without any cloud dependency.

• ⚠️Cloud Provider Caution: If used, cloud APIs will be used to summarize your daily activities. Untrusted endpoints may expose private data — review data policies of your provider carefully.

📋 Requirements

• Multimodal support: Your model should be able to handle text + images.

• Context window: Make sure the context window of your model exceed max_tokens limits in config.json.

• Cost control: For forced cost control, set limits via llm.max_calls or increase filter.mouse_cluster_gap to reduce summarization frequency.

CatchMe requires an LLM for background summarization and intelligent retrieval. Use catchme init (in Get Started)for guided setup or follow the manual configuration steps below.

For cloud API services:

{
    "llm": {
        "provider": "openrouter",
        "api_key": "sk-or-...",
        "api_url": null,
        "model": "google/gemini-3-flash-preview"
    }
}

For local/offline operation:

{
    "llm": {
        "provider": "ollama",
        "api_key": null,
        "api_url": null,
        "model": "gemma3:4b"
    }
}