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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