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Memory System V2

Fast semantic memory system with JSON indexing, auto-consolidation, and <20ms search. Capture learnings, decisions, insights, events. Use when you need persistent memory across sessions or want to recall prior work/decisions.

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: README.md, SKILL.md, docs/memory-system-v2-design.md, docs/memory-system-v2-test-results.md, memory-cli.sh

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: kellyclaudeai
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 34 sections Open source page

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

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

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"

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 ✅

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

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

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.

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

4 Docs1 Scripts

SKILL.md Primary doc
docs/memory-system-v2-design.md Docs
docs/memory-system-v2-test-results.md Docs
README.md Docs
memory-cli.sh Scripts