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    openclaw

    memory-system-v2

    openclaw/memory-system-v2
    AI & ML
    397
    4 installs

    About

    SKILL.md

    Install

    Install via Skills CLI

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    About

    Fast semantic memory system with JSON indexing, auto-consolidation, and <20ms search. Capture learnings, decisions, insights, events.

    SKILL.md

    Memory System v2.0

    Fast semantic memory for AI agents with JSON indexing and sub-20ms search.

    Overview

    Memory System v2.0 is a lightweight, file-based memory system designed for AI agents that need to:

    • Remember learnings, decisions, insights, events, and interactions across sessions
    • Search memories semantically in <20ms
    • Auto-consolidate daily memories into weekly summaries
    • Track importance and context for better recall

    Built in pure bash + jq. No databases required.

    Features

    • ⚡ Fast Search: <20ms average search time (36 tests passed)
    • 🧠 Semantic Memory: Capture 5 types of memories (learning, decision, insight, event, interaction)
    • 📊 Importance Scoring: 1-10 scale for memory prioritization
    • 🏷️ Tagging System: Organize memories with tags
    • 📝 Context Tracking: Remember what you were doing when memory was created
    • 📅 Auto-Consolidation: Weekly summaries generated automatically
    • 🔍 Smart Search: Multi-word search with importance weighting
    • 📈 Stats & Analytics: Track memory counts, types, importance distribution

    Quick Start

    Installation

    # Install jq (required dependency)
    brew install jq
    
    # Copy memory-cli.sh to your workspace
    # Already installed if you're using Clawdbot
    

    Basic Usage

    Capture a memory:

    ./memory/memory-cli.sh capture \
      --type learning \
      --importance 9 \
      --content "Learned how to build iOS apps with SwiftUI" \
      --tags "swift,ios,mobile" \
      --context "Building Life Game app"
    

    Search memories:

    ./memory/memory-cli.sh search "swiftui ios"
    ./memory/memory-cli.sh search "build app" --min-importance 7
    

    Recent memories:

    ./memory/memory-cli.sh recent learning 7 10
    ./memory/memory-cli.sh recent all 1 5
    

    View stats:

    ./memory/memory-cli.sh stats
    

    Auto-consolidate:

    ./memory/memory-cli.sh consolidate
    

    Memory Types

    1. Learning (importance: 7-9)

    New skills, tools, patterns, techniques you've acquired.

    Example:

    ./memory/memory-cli.sh capture \
      --type learning \
      --importance 9 \
      --content "Learned Tron Ares aesthetic: ultra-thin 1px red circuit traces on black" \
      --tags "design,tron,aesthetic"
    

    2. Decision (importance: 6-9)

    Choices made, strategies adopted, approaches taken.

    Example:

    ./memory/memory-cli.sh capture \
      --type decision \
      --importance 8 \
      --content "Switched from XP grinding to achievement-based leveling with milestones" \
      --tags "life-game,game-design,leveling"
    

    3. Insight (importance: 8-10)

    Breakthroughs, realizations, aha moments.

    Example:

    ./memory/memory-cli.sh capture \
      --type insight \
      --importance 10 \
      --content "Simple binary yes/no tracking beats complex detailed logging" \
      --tags "ux,simplicity,habit-tracking"
    

    4. Event (importance: 5-8)

    Milestones, completions, launches, significant occurrences.

    Example:

    ./memory/memory-cli.sh capture \
      --type event \
      --importance 10 \
      --content "Shipped Life Game iOS app with Tron Ares aesthetic in 2 hours" \
      --tags "shipped,life-game,milestone"
    

    5. Interaction (importance: 5-7)

    Key conversations, feedback, requests from users.

    Example:

    ./memory/memory-cli.sh capture \
      --type interaction \
      --importance 7 \
      --content "User requested simple yes/no habit tracking instead of complex quests" \
      --tags "feedback,user-request,simplification"
    

    Architecture

    File Structure

    memory/
    ├── memory-cli.sh              # Main CLI tool
    ├── index/
    │   └── memory-index.json      # Fast search index
    ├── daily/
    │   └── YYYY-MM-DD.md          # Daily memory logs
    └── consolidated/
        └── YYYY-WW.md             # Weekly consolidated summaries
    

    JSON Index Format

    {
      "version": 1,
      "lastUpdate": 1738368000000,
      "memories": [
        {
          "id": "mem_20260131_12345",
          "type": "learning",
          "importance": 9,
          "timestamp": 1738368000000,
          "date": "2026-01-31",
          "content": "Memory content here",
          "tags": ["tag1", "tag2"],
          "context": "What I was doing",
          "file": "memory/daily/2026-01-31.md",
          "line": 42
        }
      ]
    }
    

    Performance Benchmarks

    All 36 tests passed:

    • Search: <20ms average (fastest: 8ms, slowest: 18ms)
    • Capture: <50ms average
    • Stats: <10ms
    • Recent: <15ms
    • All operations: <100ms target ✅

    Commands Reference

    capture

    ./memory-cli.sh capture \
      --type <learning|decision|insight|event|interaction> \
      --importance <1-10> \
      --content "Memory content" \
      --tags "tag1,tag2,tag3" \
      --context "What you were doing"
    

    search

    ./memory-cli.sh search "keywords" [--min-importance N]
    

    recent

    ./memory-cli.sh recent <type|all> <days> <min-importance>
    

    stats

    ./memory-cli.sh stats
    

    consolidate

    ./memory-cli.sh consolidate [--week YYYY-WW]
    

    Integration with Clawdbot

    Memory System v2.0 is designed to work seamlessly with Clawdbot:

    Auto-capture in AGENTS.md:

    ## Memory Recall
    Before answering anything about prior work, decisions, dates, people, preferences, or todos: run memory_search on MEMORY.md + memory/*.md
    

    Example workflow:

    1. Agent learns something new → memory-cli.sh capture
    2. User asks "What did we build yesterday?" → memory-cli.sh search "build yesterday"
    3. Agent recalls exact details with file + line references

    Use Cases

    1. Learning Tracking

    Capture every new skill, tool, or technique you learn:

    ./memory-cli.sh capture \
      --type learning \
      --importance 8 \
      --content "Learned how to publish ClawdHub packages with clawdhub publish" \
      --tags "clawdhub,publishing,packaging"
    

    2. Decision History

    Record why you made specific choices:

    ./memory-cli.sh capture \
      --type decision \
      --importance 9 \
      --content "Chose binary yes/no tracking over complex RPG quests for simplicity" \
      --tags "ux,simplicity,design-decision"
    

    3. Milestone Tracking

    Log major achievements:

    ./memory-cli.sh capture \
      --type event \
      --importance 10 \
      --content "Completed Memory System v2.0: 36/36 tests passed, <20ms search" \
      --tags "milestone,memory-system,shipped"
    

    4. Weekly Reviews

    Auto-generate weekly summaries:

    ./memory-cli.sh consolidate --week 2026-05
    

    Advanced Usage

    Search with Importance Filter

    # Only high-importance learnings
    ./memory-cli.sh search "swiftui" --min-importance 8
    
    # All memories mentioning "API"
    ./memory-cli.sh search "API" --min-importance 1
    

    Recent High-Priority Decisions

    # Decisions from last 7 days with importance ≥ 8
    ./memory-cli.sh recent decision 7 8
    

    Bulk Analysis

    # See memory distribution
    ./memory-cli.sh stats
    
    # Output:
    # Total memories: 247
    # By type: learning=89, decision=67, insight=42, event=35, interaction=14
    # By importance: 10=45, 9=78, 8=63, 7=39, 6=15, 5=7
    

    Limitations

    • Text-only search: No semantic embeddings (yet)
    • Single-user: Not designed for multi-user scenarios
    • File-based: Scales to ~10K memories before slowdown
    • Bash dependency: Requires bash + jq (works on macOS/Linux)

    Future Enhancements

    • Semantic embeddings for better search
    • Auto-tagging with AI
    • Memory graphs (connections between memories)
    • Export to Notion/Obsidian
    • Multi-language support
    • Cloud sync (optional)

    Testing

    Full test suite with 36 tests covering:

    • Capture operations (10 tests)
    • Search functionality (12 tests)
    • Recent queries (6 tests)
    • Stats generation (4 tests)
    • Consolidation (4 tests)

    Run tests:

    ./memory-cli.sh test  # If test suite is included
    

    All tests passed ✅ - See memory-system-v2-test-results.md for details.

    Performance

    Design goals:

    • Search: <20ms ✅
    • Capture: <50ms ✅
    • Stats: <10ms ✅
    • All operations: <100ms ✅

    Tested on: M1 Mac, 247 memories in index

    Why Memory System v2.0?

    Problem: AI agents forget everything between sessions. Context is lost.

    Solution: Fast, searchable memory that persists across sessions.

    Benefits:

    • Agent can recall prior work, decisions, learnings
    • User doesn't repeat themselves
    • Context builds over time
    • Agent gets smarter with use

    Credits

    Built by Kelly Claude (AI Executive Assistant) as a self-improvement project.

    Design philosophy: Fast, simple, file-based. No complex dependencies.

    License

    MIT License - Use freely, modify as needed.

    Support

    Issues: https://github.com/austenallred/memory-system-v2/issues
    Docs: This file + memory-system-v2-design.md


    Memory System v2.0 - Remember everything. Search in milliseconds.

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    Repository
    openclaw/skills