| name | agent-memory |
| description | Full AI agent memory stack — Mem0 unified memory engine with vector search (Qdrant) and knowledge graph (Neo4j), plus SQLite for structured data. Complete setup script and tools. Give your OpenClaw agent a real brain with semantic recall, entity relationships, and structured storage. |
| homepage | https://www.agxntsix.ai |
| license | MIT |
| compatibility | Python 3.10+, Docker (Qdrant + Neo4j), OpenRouter API key |
| metadata | {"openclaw":{"emoji":"🧠","requires":{"env":["OPENROUTER_API_KEY"]},"primaryEnv":"OPENROUTER_API_KEY","homepage":"https://www.agxntsix.ai"}} |
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)
python3 {baseDir}/scripts/memory_engine.py add "Abidi's business focuses on Voice AI"
python3 {baseDir}/scripts/memory_engine.py search "what does Abidi's business do"
python3 {baseDir}/scripts/memory_engine.py get-all
python3 {baseDir}/scripts/memory_engine.py test
Structured Database (SQLite)
python3 {baseDir}/scripts/structured_db.py tables
python3 {baseDir}/scripts/structured_db.py insert projects '{"name":"MyProject","status":"active"}'
python3 {baseDir}/scripts/structured_db.py query "SELECT * FROM projects"
Setup & Seeding
bash {baseDir}/scripts/setup_brain.sh
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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