Add Metis project to README

README.md

@@ -1939,6 +1939,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to
 - [STiFLeR7/memex](https://github.com/STiFLeR7/memex) [![STiFLeR7/memex MCP server](https://glama.ai/mcp/servers/STiFLeR7/memex/badges/score.svg)](https://glama.ai/mcp/servers/STiFLeR7/memex) 🐍 🏠 🍎 🪟 🐧 - Developer context continuity system. Watches your git repos and builds a temporal knowledge graph of modules, symbols, decisions, and open problems via Graphiti + Neo4j, then serves it to any AI coding agent over MCP. Every edge carries a validity window and a confidence score that decays over time. 12 tools across read and write. Install via `npx -y stifler-memex-mcp`. MIT licensed.
 - [xChuCx/agent-memory](https://github.com/xChuCx/agent-memory) [![agent-memory MCP server](https://glama.ai/mcp/servers/xChuCx/agent-memory/badges/score.svg)](https://glama.ai/mcp/servers/xChuCx/agent-memory) 🏎️ 🏠 🍎 🪟 🐧 - Git-native project memory for coding agents: Markdown source of truth committed to your repo, reviewable staged updates (`review --diff` → `apply`), secret/PII-safe, branch-aware — no cloud, no vector DB.
 - [zzallirog/weighted-compact](https://github.com/zzallirog/weighted-compact) [![zzallirog/weighted-compact MCP server](https://glama.ai/mcp/servers/zzallirog/weighted-compact/badges/score.svg)](https://glama.ai/mcp/servers/zzallirog/weighted-compact) 🐍 🏠 - Inspectable memory substrate for Claude Code. Three read-only MCP tools (search_pairs, compact_session, substrate_info) over a local-first, signal-scored parse of `~/.claude/projects/`. Per-pair scores are numpy columns on disk, not opaque vectors in a service. Zero outbound calls (CI-enforced).
+- [SVerITG/Metis](https://github.com/SVerITG/Metis) [![Metis MCP server](https://glama.ai/mcp/servers/SVerITG/Metis/badges/score.svg)](https://glama.ai/mcp/servers/SVerITG/Metis) 🐍 🏠 🍎 🪟  🐧 - A private, local research "second brain" for Claude: project-aware memory, cited answers from your own library (won't invent what it can't find), linked notes/meetings/ideas via a domain-specific knowledge layer, daily briefs (news + new papers in your field), a live meeting assistant, cross-pollination across your work, and 34 routed agents. A governed layer between you and the AI, with guardrails like data protection. A Research Cortex.
 
 ### ⚖️ <a name="legal"></a>Legal
 Access to legal information, legislation, and legal databases. Enables AI models to search and analyze legal documents and regulatory information.