coolmanns

coolmanns / openclaw-memory-architecture

Public

12-layer memory architecture for OpenClaw agents — knowledge graph (3K+ facts), semantic search (multilingual, 7ms GPU), continuity + stability + graph-memory plugins, activation/decay system, domain RAG. Agents reconstruct themselves from files on every boot.

28
3
100% credibility
Found Feb 18, 2026 at 12 stars 2x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A personal memory system for AI agents that stores structured facts, relationships, and knowledge with tools for adding, searching, visualizing, and maintaining the data.

How It Works

1
🔍 Discover the memory helper

You hear about a simple tool that helps your AI assistant remember important details about your life, family, projects, and decisions like a personal notebook.

2
📦 Set up your memory space

You prepare a safe, private spot on your computer where all your personal memories will be stored securely.

3
✨ Add your personal facts

You easily enter details like birthdays, project names, family relationships, and key decisions, making it feel like filling out a trusted diary.

4
🔗 Connect the dots

You link people to each other, projects to tools they use, and facts together so everything relates naturally.

5
🔎 Ask and explore

You start asking questions like 'Who is Mama?' or 'What projects do I own?' and get quick, smart answers with sources.

6
🧹 Keep it tidy

You occasionally clean up old notes, keeping only the most important memories fresh and relevant.

🎉 AI knows you perfectly

Now your AI assistant remembers every detail about your life, family, and work, making conversations feel personal and helpful.

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AI-Generated Review

What is openclaw-memory-architecture?

This Python project delivers a multi-layered memory architecture for OpenClaw agents, blending SQLite with FTS5 for structured facts, a knowledge graph for relationships, and semantic search. It solves agent amnesia by storing entities, facts, and triples persistently, enabling instant lookups and graph traversals via CLI tools like query-facts for FTS searches or graph-search for natural language queries. Users get a benchmarked system that jumps from 46.7% to 100% accuracy on 60 queries, like an orthopedic multi-layered zoned memory foam mattress cradling recall without sinking into vague vectors.

Why is it gaining traction?

The killer hook is the 60-query benchmark proving 100% recall gains over baselines, using FTS5 for exact matches and graph layers for context—like turnberry multi-layered memory foam that adapts without complexity. Lightweight Python/SQLite means zero deps, instant setup, and CLI-first workflows for seeding facts or pruning old memory. Developers dig the eco orthopedic multi-layered memory foam mattress vibe: efficient, zoned support for agents without heavy vector DB overhead.

Who should use this?

AI agent hackers building OpenClaw setups needing persistent knowledge for facts, relationships, or decisions. Perfect for solo devs prototyping multi-agent systems where structured search beats embeddings, or teams tracking project stacks, user prefs, and family ties in a single DB. Skip if you're all-in on Pinecone or Weaviate.

Verdict

Promising prototype for OpenClaw memory with killer benchmark results, but 10 stars and 1.0% credibility signal early days—docs are README-only, no tests visible. Worth forking for agent experiments; production needs more polish.

(198 words)

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