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Add roampal-ai/roampal-core to Knowledge & Memory

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@@ -1310,6 +1310,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to
- [ragieai/mcp-server](https://github.com/ragieai/ragie-mcp-server) 📇 ☁️ - Retrieve context from your [Ragie](https://www.ragie.ai) (RAG) knowledge base connected to integrations like Google Drive, Notion, JIRA and more. - [ragieai/mcp-server](https://github.com/ragieai/ragie-mcp-server) 📇 ☁️ - Retrieve context from your [Ragie](https://www.ragie.ai) (RAG) knowledge base connected to integrations like Google Drive, Notion, JIRA and more.
- [remembra-ai/remembra](https://github.com/remembra-ai/remembra) [![remembra MCP server](https://glama.ai/mcp/servers/remembra-ai/remembra/badges/score.svg)](https://glama.ai/mcp/servers/remembra-ai/remembra) 🐍 📇 🏠 ☁️ 🍎 🪟 🐧 - Persistent memory layer for AI agents with entity resolution, PII detection, AES-256-GCM encryption at rest, and hybrid search. 100% on LoCoMo benchmark. Self-hosted. - [remembra-ai/remembra](https://github.com/remembra-ai/remembra) [![remembra MCP server](https://glama.ai/mcp/servers/remembra-ai/remembra/badges/score.svg)](https://glama.ai/mcp/servers/remembra-ai/remembra) 🐍 📇 🏠 ☁️ 🍎 🪟 🐧 - Persistent memory layer for AI agents with entity resolution, PII detection, AES-256-GCM encryption at rest, and hybrid search. 100% on LoCoMo benchmark. Self-hosted.
- [redleaves/context-keeper](https://github.com/redleaves/context-keeper) 🏎️ 🏠 ☁️ 🍎 🪟 🐧 - LLM-driven context and memory management with wide-recall + precise-reranking RAG architecture. Features multi-dimensional retrieval (vector/timeline/knowledge graph), short/long-term memory, and complete MCP support (HTTP/WebSocket/SSE). - [redleaves/context-keeper](https://github.com/redleaves/context-keeper) 🏎️ 🏠 ☁️ 🍎 🪟 🐧 - LLM-driven context and memory management with wide-recall + precise-reranking RAG architecture. Features multi-dimensional retrieval (vector/timeline/knowledge graph), short/long-term memory, and complete MCP support (HTTP/WebSocket/SSE).
- [roampal-ai/roampal-core](https://github.com/roampal-ai/roampal-core) [![roampal-core MCP server](https://glama.ai/mcp/servers/roampal-ai/roampal-core/badges/score.svg)](https://glama.ai/mcp/servers/roampal-ai/roampal-core) 🐍 🏠 - Outcome-based persistent memory for AI coding tools. Memories that help get promoted, memories that mislead get demoted. Works with Claude Code and OpenCode via hooks + MCP.
- [roomi-fields/notebooklm-mcp](https://github.com/roomi-fields/notebooklm-mcp) [![notebooklm-mcp MCP server](https://glama.ai/mcp/servers/@roomi-fields/notebooklm-mcp/badges/score.svg)](https://glama.ai/mcp/servers/@roomi-fields/notebooklm-mcp) 📇 🏠 🍎 🪟 🐧 - Full automation of Google NotebookLM — Q&A with citations, audio podcasts, video, content generation, source management, and notebook library. MCP + HTTP REST API. - [roomi-fields/notebooklm-mcp](https://github.com/roomi-fields/notebooklm-mcp) [![notebooklm-mcp MCP server](https://glama.ai/mcp/servers/@roomi-fields/notebooklm-mcp/badges/score.svg)](https://glama.ai/mcp/servers/@roomi-fields/notebooklm-mcp) 📇 🏠 🍎 🪟 🐧 - Full automation of Google NotebookLM — Q&A with citations, audio podcasts, video, content generation, source management, and notebook library. MCP + HTTP REST API.
- [rushikeshmore/CodeCortex](https://github.com/rushikeshmore/CodeCortex) [![codecortex MCP server](https://glama.ai/mcp/servers/@rushikeshmore/codecortex/badges/score.svg)](https://glama.ai/mcp/servers/@rushikeshmore/codecortex) 📇 🏠 🍎 🪟 🐧 - Persistent codebase knowledge layer for AI coding agents. Pre-digests codebases into structured knowledge (symbols, dependency graphs, co-change patterns, architectural decisions) via tree-sitter native parsing (28 languages) and serves via MCP. 14 tools, ~85% token reduction. Works with Claude Code, Cursor, Codex, and any MCP client. - [rushikeshmore/CodeCortex](https://github.com/rushikeshmore/CodeCortex) [![codecortex MCP server](https://glama.ai/mcp/servers/@rushikeshmore/codecortex/badges/score.svg)](https://glama.ai/mcp/servers/@rushikeshmore/codecortex) 📇 🏠 🍎 🪟 🐧 - Persistent codebase knowledge layer for AI coding agents. Pre-digests codebases into structured knowledge (symbols, dependency graphs, co-change patterns, architectural decisions) via tree-sitter native parsing (28 languages) and serves via MCP. 14 tools, ~85% token reduction. Works with Claude Code, Cursor, Codex, and any MCP client.
- [SecurityRonin/alaya](https://github.com/SecurityRonin/alaya) [![SecurityRonin/alaya MCP server](https://glama.ai/mcp/servers/SecurityRonin/alaya/badges/score.svg)](https://glama.ai/mcp/servers/SecurityRonin/alaya) 🦀 🏠 🍎 🪟 🐧 - Neuroscience-inspired memory engine for AI agents. Stores episodes, consolidates knowledge through a Bjork-strength lifecycle (strengthening, transformation, forgetting), and builds a personal knowledge graph with emergent categories, preferences, and semantic recall. Local SQLite, zero config, 10 MCP tools. Install via `npx alaya-mcp`. - [SecurityRonin/alaya](https://github.com/SecurityRonin/alaya) [![SecurityRonin/alaya MCP server](https://glama.ai/mcp/servers/SecurityRonin/alaya/badges/score.svg)](https://glama.ai/mcp/servers/SecurityRonin/alaya) 🦀 🏠 🍎 🪟 🐧 - Neuroscience-inspired memory engine for AI agents. Stores episodes, consolidates knowledge through a Bjork-strength lifecycle (strengthening, transformation, forgetting), and builds a personal knowledge graph with emergent categories, preferences, and semantic recall. Local SQLite, zero config, 10 MCP tools. Install via `npx alaya-mcp`.