Agent Memory 🧠

Full intelligence layer: vector memory + knowledge graph + structured database.

When to Use

• Storing and recalling facts semantically ("remember that Abidi prefers...")
• Managing structured data: projects, contacts, tasks, bookmarks
• Setting up the brain stack after container rebuild
• Batch seeding memory with key facts

Usage

Memory Engine (Mem0 — vectors + graph)

# Store a fact
python3 {baseDir}/scripts/memory_engine.py add "Abidi's business focuses on Voice AI"

# Semantic recall
python3 {baseDir}/scripts/memory_engine.py search "what does Abidi's business do"

# List all memories
python3 {baseDir}/scripts/memory_engine.py get-all

# Test connections (Qdrant, Neo4j, Langfuse)
python3 {baseDir}/scripts/memory_engine.py test

Structured Database (SQLite)

# List tables
python3 {baseDir}/scripts/structured_db.py tables

# Insert data
python3 {baseDir}/scripts/structured_db.py insert projects '{"name":"MyProject","status":"active"}'

# Query
python3 {baseDir}/scripts/structured_db.py query "SELECT * FROM projects"

Setup & Seeding

# Install Python deps after container rebuild
bash {baseDir}/scripts/setup_brain.sh

# Batch seed with key facts
python3 {baseDir}/scripts/seed_mem0.py

Architecture

• Mem0 — Unified AI memory (auto fact extraction, dedup, multi-level recall)
• Qdrant — Vector database for semantic search
• Neo4j — Knowledge graph for entities & relationships
• SQLite — Structured data (projects, contacts, tasks, bookmarks)
• Langfuse — Observability tracing on all operations

Credits

Built by M. Abidi | agxntsix.ai YouTube | GitHub Part of the AgxntSix Skill Suite for OpenClaw agents.

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