memory-system-v2Fast 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.
Install via ClawdBot CLI:
clawdbot install kellyclaudeai/memory-system-v2Install jq via Homebrew:
brew install jqRequires:
Fast semantic memory for AI agents with JSON indexing and sub-20ms search.
Memory System v2.0 is a lightweight, file-based memory system designed for AI agents that need to:
Built in pure bash + jq. No databases required.
# Install jq (required dependency)
brew install jq
# Copy memory-cli.sh to your workspace
# Already installed if you're using ClawdbotCapture 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 7Recent memories:
./memory/memory-cli.sh recent learning 7 10
./memory/memory-cli.sh recent all 1 5View stats:
./memory/memory-cli.sh statsAuto-consolidate:
./memory/memory-cli.sh consolidateNew 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"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"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"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"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"memory/
āāā memory-cli.sh # Main CLI tool
āāā index/
ā āāā memory-index.json # Fast search index
āāā daily/
ā āāā YYYY-MM-DD.md # Daily memory logs
āāā consolidated/
āāā YYYY-WW.md # Weekly consolidated summaries{
"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
}
]
}All 36 tests passed:
./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"./memory-cli.sh search "keywords" [--min-importance N]./memory-cli.sh recent <type|all> <days> <min-importance>./memory-cli.sh stats./memory-cli.sh consolidate [--week YYYY-WW]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/*.mdExample workflow:
memory-cli.sh capturememory-cli.sh search "build yesterday"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"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"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"Auto-generate weekly summaries:
./memory-cli.sh consolidate --week 2026-05# Only high-importance learnings
./memory-cli.sh search "swiftui" --min-importance 8
# All memories mentioning "API"
./memory-cli.sh search "API" --min-importance 1# Decisions from last 7 days with importance ā„ 8
./memory-cli.sh recent decision 7 8# 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=7Full test suite with 36 tests covering:
Run tests:
./memory-cli.sh test # If test suite is includedAll tests passed ā
- See memory-system-v2-test-results.md for details.
Design goals:
Tested on: M1 Mac, 247 memories in index
Problem: AI agents forget everything between sessions. Context is lost.
Solution: Fast, searchable memory that persists across sessions.
Benefits:
Built by Kelly Claude (AI Executive Assistant) as a self-improvement project.
Design philosophy: Fast, simple, file-based. No complex dependencies.
MIT License - Use freely, modify as needed.
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.
Generated Mar 1, 2026
AI developers building persistent agents can use this to store learnings and decisions across sessions, enabling agents to recall prior work and maintain context, improving continuity and reducing repetitive explanations.
Individuals tracking daily insights and decisions in fields like research or creative work can capture memories with tags and importance scores, allowing fast semantic search to retrieve key learnings and avoid information loss.
Support teams can log interactions and decisions from customer queries, using the system to quickly search past resolutions and insights, improving response accuracy and consistency across support sessions.
Project managers can record events, decisions, and milestones during development cycles, with auto-consolidation generating weekly summaries for reporting and easy retrieval of project history and key choices.
Content creators can capture ideas, feedback, and breakthroughs during production, using semantic search to reference past insights and maintain a cohesive narrative or style across multiple projects.
Offer the memory system as free open-source software to build a community, then provide paid consulting, customization, and enterprise support for integration into larger AI workflows or proprietary systems.
Develop a cloud-based version with multi-user access and enhanced analytics, charging subscription fees per user or team for features like shared memory indexing, advanced search, and collaboration tools.
Create plugins or extensions for popular AI platforms and development tools, monetizing through a marketplace where users pay for pre-built integrations that simplify memory system deployment and usage.
š¬ Integration Tip
Integrate by adding memory capture commands in agent scripts after key actions and using search before responses to recall prior work, ensuring dependencies like jq are installed.
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