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store: Cognitive Memory v3.0: Rich Edges + Hybrid Embeddings + MCP Server

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Cal Corum 2026-02-19 14:10:42 -06:00
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graph/solutions/cognitive-memory-v30-rich-edges-hybrid-embeddings-mcp-server-9ea720.md Normal file
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---
id: 9ea72015-0b85-4a42-9ae1-144866f8d86f
type: solution
title: "Cognitive Memory v3.0: Rich Edges + Hybrid Embeddings + MCP Server"
tags: [cognitive-memory, mcp, architecture, upgrade]
importance: 0.9
confidence: 0.8
created: "2026-02-19T20:10:42.128691+00:00"
updated: "2026-02-19T20:10:42.128691+00:00"
---
Major upgrade to cognitive-memory skill. Phase 1: Rich edges as first-class markdown files in graph/edges/ with bidirectional frontmatter refs (edge_id field). relate() returns edge_id string instead of bool. Cascade deletion on memory delete. CLI: edge-get, edge-search, edge-update, edge-delete. Phase 2: Hybrid embedding providers (Ollama local + OpenAI optional) with automatic fallback chain. _config.json stores provider settings (gitignored). Dimension mismatch safety triggers re-embedding on provider switch. Phase 3: MCP server (mcp_server.py) with 18 tools via JSON-RPC 2.0 stdio protocol. Registered in ~/.claude.json. Phase 4: Updated SKILL.md, SCHEMA.md, feature.json to v3.0.0. Index version bumped from 1 to 2 (adds edges section). All stdlib-only, no external dependencies.