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💭 Reasoning: The self-test passed! All three core functions work: 1. extract_and_store() - Successfully extracted entities and stored them 2. graph_retrieval() - Found seed nodes via vector similarity 3. summarize_context() - Generated structured markdown context Now let me update the memory with this new information about the graph memory module. Self-test successful! The module is running stably. 🏰 Hermes Graph Memory — Deployment completed Architecture implemented ┌─────────────────────────────────────────────┐ │ HERMES GRAPH MEMORY SYSTEM │ ├─────────────────────────────────────────────┤ │ │ │ LAYER 1: Conversation (ephemeral) │ │ └→ In-memory buffer, auto-clear per turn │ │ │ │ LAYER 2: Session Graph (7-day TTL) │ │ └→ Entity triplets + vector embeddings │ │ SQLite edges + Qdrant vectors │ │ │ │ LAYER 3: User Memory (persistent) │ │ └→ Promoted high-confidence facts │ │ Cross-session retrieval │ │ │ ├─────────────────────────────────────────────┤ │ STORAGE: │ │ • Qdrant: Vector similarity search │ │ • SQLite: Graph edges (S-P-O triplets) │ │ • LocalEmbedder: Hash-based projection │ │ (384-dim, L2-normalized) │ ├─────────────────────────────────────────────┤ │ RETRIEVAL: │ │ • Vector cosine similarity → seed nodes │ │ • N-hop BFS traversal → expanded context │ │ • Confidence-weighted ranking │ └─────────────────────────────────────────────┘ 3 Core functions implemented | Function | Purpose | Test Result | |---|---|---| | extract_and_store(text) | Entity extraction + triplets → push to graph | ✓ 8 entities, 1 edge | | graph_retrieval(query) | Vector search + N-hop traversal | ✓ 5 seed nodes found | | summarize_context() | Compress context into structured Markdown | ✓ 682 chars generated | Module components (hermes_graph_memory.py) * EntityNode — Graph node with label, properties, and confidence score * GraphEdge — Directed relationship (subject→predicate→object) * MemoryRecord — Layered record (conversation/session/user) * LocalEmbedder — 384-dimensional embedding, no external API required * QdrantStore — Vector store with in-memory fallback * GraphStore — SQLite persistence for graph edges * LocalExtractor — Entity extraction (LLM or rule-based) * HermesGraphMemory — Main manager with 3 core functions Self-test results ✅ Extracted: PERSON(5), PROJECT(1), TECHNOLOGY(1), CONCEPT(1) ✅ Stored: 4 session memories with entity links ✅ Retrieved: 5 seed nodes via vector similarity ✅ Summarized: Structured markdown context (682 chars) The module is ready to be integrated into the core system. The file is located at ~/.hermes/core/hermes_graph_memory.py.