Mnemosyne OS — Production use case: AI agent memory with Apache AGE knowledge graph

## Mnemosyne OS — Production use case with Apache AGE knowledge graph

Hi AGE team! We're building a cognitive memory OS for AI agents, and Apache AGE is our knowledge graph backbone. Wanted to share as a real-world use case.

### How we use AGE
**Mnemosyne OS v5.2** uses AGE's Cypher engine for entity relationship reasoning:
- Auto-extracts entities from conversation memories
- Builds Cypher graphs (`mnemosyne_graph`) with MERGE/CREATE patterns
- Multi-hop graph search via `POST /api/v1/graph/search`
- 3 ag_labels: `_ag_label_vertex`, `_ag_label_edge`, `Entity`

### Architecture
- PostgreSQL 16 + pgvector (1024d) + Apache AGE 1.6.0
- 5-level Time Memory Tree with LLM auto-distillation
- 3-Hall knowledge pipeline (Research→Engineering→Archive)
- Runs 7x24 on Tencent Cloud · FastAPI + asyncpg

### Links
- GitHub: https://github.com/gymaira1990-jpg/Mnemosyne-OS
- Whitepaper: https://doi.org/10.5281/zenodo.20837834
- Website: https://my.g-cat.cn

Happy to contribute AGE usage patterns or feedback back to the project!

This issue describes Mnemosyne OS using Apache AGE, PostgreSQL, pgvector, FastAPI, and asyncpg, but names no AGE repository file, test, or entry point. Start by clarifying with the issue author or maintainers whether a documented use case, usage-pattern contribution, or feedback report is wanted. Done requires a defined contribution scope and acceptance criteria.

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