Build a Persistent Memory System for AI Agents Using SQLite and JSON

My AI agents have persistent memory. 🧠 

Every agent reads what the last one did before it starts. 

Nothing is forgotten between sessions.

8 SQLite databases. 19 memory directories. 21 shared brain files. 

Save and paste this into your OpenClaw!! 👇

LAYER 1: THE DATABASES

knowledge.db is the core. Vector embeddings. Tables: chunks, vec_chunks, kb_entities, kb_sources. Agents search by meaning, not keyword. 

Drop a fact in, retrieve it months later with a semantic query that uses no matching words.

crm.db tracks every lead. Contacts, companies, interactions. CLOSER logs every DM draft. Nothing falls through.

social-analytics.db has a tweet_performance table. Every tweet tracked over time. SCRIBE reads this before writing the next one. The system learns what resonates.

cron-runs.db logs every scheduled job. llm-usage.db tracks every AI call model, tokens, exact cost. notification-queue.db queues Telegram alerts so nothing drops when agents finish simultaneously.

LAYER 2: 19 EMPLOYEE MEMORY DIRECTORIES

ATLAS, CLAWD, CLIP, CLOSER, CONTENT, GROWTH, JARVIS, NOVA, ORACLE, PIXEL, RETENTION, SAGE, SCRIBE, SENTINEL, TRENDY, VIBE, WRITER and more.

Each has daily .md log files. Agent finishes a task, it writes what it did. Next task, it reads the last two days first.

Agents that compound instead of reset.

LAYER 3: 21 SHARED BRAIN JSON FILES

agent-handoffs.json, intel-feed.json, scribe-banger-vault.json (590 tweets analyzed), closer-outreach-log.json, user-intel.json, conversion-intel.json.

Protocol: agent WRITES after every task. Agent READS before every task.

TRENDY scouts X every 2 hours, writes to intel-feed.json. SCRIBE reads it before writing a word. CLOSER logs every lead so it never contacts the same person twice.

No middleware. No central server. Just files passing context between agents.

LAYER 4: THE SPAWN SCRIPT

https://t.co/M5uchkzAN8 runs before any agent starts:

1. Agent identity file
2. Last 2 days of personal memory logs
3. Relevant shared brain JSON files
4. Today's running log

Agents never start cold.

Steal this prompt:

"Build me a persistent memory system for my AI agents. I need: 

1) A SQLite database with vector embeddings for semantic search agents drop knowledge in and retrieve by meaning. 
2) A shared-brain/ folder with JSON files agents write to after every task and read before starting. 
3) A memory directory per agent with daily .md log files. 
4) A startup script called https://t.co/M5uchkzAN8 that injects each agent's last 2 days of logs plus shared brain files before they run anything. 
5) Show me the folder structure and confirm all tables are created.