From 1fcebf10aef83950bd0f1f85f5eed2cb85498ff6 Mon Sep 17 00:00:00 2001 From: Saveliy Date: Mon, 11 May 2026 13:05:40 +0000 Subject: [PATCH 1/2] =?UTF-8?q?Add=20mnemostack=20MCP=20server=20?= =?UTF-8?q?=F0=9F=A4=96=F0=9F=A4=96=F0=9F=A4=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index a58a33b32..407362b5c 100644 --- a/README.md +++ b/README.md @@ -1650,6 +1650,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to - [markmhendrickson/neotoma](https://github.com/markmhendrickson/neotoma) [![Neotoma MCP server](https://glama.ai/mcp/servers/markmhendrickson/neotoma/badges/score.svg)](https://glama.ai/mcp/servers/markmhendrickson/neotoma) 📇 🏠 🍎 🪟 🐧 - Deterministic state layer for AI agents. Stores versioned entities (contacts, tasks, transactions, decisions) with immutable observations, full provenance, and schema-first extraction. Local-first SQLite, cross-client memory across Claude, Cursor, ChatGPT, and OpenClaw. [Website](https://neotoma.io) - [mattjoyce/mcp-persona-sessions](https://github.com/mattjoyce/mcp-persona-sessions) 🐍 🏠 - Enable AI assistants to conduct structured, persona-driven sessions including interview preparation, personal reflection, and coaching conversations. Built-in timer management and performance evaluation tools. - [mem0ai/mem0-mcp](https://github.com/mem0ai/mem0-mcp) 🐍 🏠 - A Model Context Protocol server for Mem0 that helps manage coding preferences and patterns, providing tools for storing, retrieving and semantically handling code implementations, best practices and technical documentation in IDEs like Cursor and Windsurf +- [udjin-labs/mnemostack](https://github.com/udjin-labs/mnemostack) 🐍 🏠 🍎 🪟 🐧 - Durable hybrid memory for AI agents. Combines vector search, BM25, temporal retrieval, and optional Memgraph knowledge graph via reciprocal rank fusion. 6 MCP tools: health, search, answer, feedback, graph_query, graph_add_triple. Self-hosted with Qdrant backend. 82.5% strict accuracy on LoCoMo benchmark. `pip install 'mnemostack[mcp]'` - [tverney/mcp-agent-memory](https://github.com/tverney/mcp-agent-memory) [![tverney/mcp-agent-memory MCP server](https://glama.ai/mcp/servers/tverney/mcp-agent-memory/badges/score.svg)](https://glama.ai/mcp/servers/tverney/mcp-agent-memory) 📇 🏠 🍎 - Persistent agent memory via filesystem-native consolidation daemon. Agents read/write/search memory through MCP; a background daemon handles consolidation and extraction. Works with Kiro, Claude Desktop, Cursor, and any MCP client. `npx mcp-agent-memory --setup` - [memstate-ai/memstate-mcp](https://github.com/memstate-ai/memstate-mcp) [![memstate-mcp MCP server](https://glama.ai/mcp/servers/memstate-ai/memstate-mcp/badges/score.svg)](https://glama.ai/mcp/servers/memstate-ai/memstate-mcp) 📇 ☁️ - Versioned, structured memory for AI agents. Stores facts as keypaths with full version history, automatic conflict detection, and O(1) token retrieval. - [michael-denyer/memory-mcp](https://github.com/michael-denyer/memory-mcp) 🐍 🏠 🍎 🪟 🐧 - Two-tier memory with hot cache (instant injection) and cold semantic search. Auto-promotes frequently-used patterns, extracts knowledge from Claude outputs, and organizes via knowledge graph relationships. From efdcc1123f0167914f494564fcb1e2306c4fa65e Mon Sep 17 00:00:00 2001 From: Saveliy Date: Mon, 11 May 2026 13:42:37 +0000 Subject: [PATCH 2/2] Add Glama score badge for mnemostack --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 407362b5c..81cb47303 100644 --- a/README.md +++ b/README.md @@ -1650,7 +1650,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to - [markmhendrickson/neotoma](https://github.com/markmhendrickson/neotoma) [![Neotoma MCP server](https://glama.ai/mcp/servers/markmhendrickson/neotoma/badges/score.svg)](https://glama.ai/mcp/servers/markmhendrickson/neotoma) 📇 🏠 🍎 🪟 🐧 - Deterministic state layer for AI agents. Stores versioned entities (contacts, tasks, transactions, decisions) with immutable observations, full provenance, and schema-first extraction. Local-first SQLite, cross-client memory across Claude, Cursor, ChatGPT, and OpenClaw. [Website](https://neotoma.io) - [mattjoyce/mcp-persona-sessions](https://github.com/mattjoyce/mcp-persona-sessions) 🐍 🏠 - Enable AI assistants to conduct structured, persona-driven sessions including interview preparation, personal reflection, and coaching conversations. Built-in timer management and performance evaluation tools. - [mem0ai/mem0-mcp](https://github.com/mem0ai/mem0-mcp) 🐍 🏠 - A Model Context Protocol server for Mem0 that helps manage coding preferences and patterns, providing tools for storing, retrieving and semantically handling code implementations, best practices and technical documentation in IDEs like Cursor and Windsurf -- [udjin-labs/mnemostack](https://github.com/udjin-labs/mnemostack) 🐍 🏠 🍎 🪟 🐧 - Durable hybrid memory for AI agents. Combines vector search, BM25, temporal retrieval, and optional Memgraph knowledge graph via reciprocal rank fusion. 6 MCP tools: health, search, answer, feedback, graph_query, graph_add_triple. Self-hosted with Qdrant backend. 82.5% strict accuracy on LoCoMo benchmark. `pip install 'mnemostack[mcp]'` +- [udjin-labs/mnemostack](https://github.com/udjin-labs/mnemostack) [![udjin-labs/mnemostack MCP server](https://glama.ai/mcp/servers/udjin-labs/mnemostack/badges/score.svg)](https://glama.ai/mcp/servers/udjin-labs/mnemostack) 🐍 🏠 🍎 🪟 🐧 - Durable hybrid memory for AI agents. Combines vector search, BM25, temporal retrieval, and optional Memgraph knowledge graph via reciprocal rank fusion. 6 MCP tools: health, search, answer, feedback, graph_query, graph_add_triple. Self-hosted with Qdrant backend. 82.5% strict accuracy on LoCoMo benchmark. `pip install 'mnemostack[mcp]'` - [tverney/mcp-agent-memory](https://github.com/tverney/mcp-agent-memory) [![tverney/mcp-agent-memory MCP server](https://glama.ai/mcp/servers/tverney/mcp-agent-memory/badges/score.svg)](https://glama.ai/mcp/servers/tverney/mcp-agent-memory) 📇 🏠 🍎 - Persistent agent memory via filesystem-native consolidation daemon. Agents read/write/search memory through MCP; a background daemon handles consolidation and extraction. Works with Kiro, Claude Desktop, Cursor, and any MCP client. `npx mcp-agent-memory --setup` - [memstate-ai/memstate-mcp](https://github.com/memstate-ai/memstate-mcp) [![memstate-mcp MCP server](https://glama.ai/mcp/servers/memstate-ai/memstate-mcp/badges/score.svg)](https://glama.ai/mcp/servers/memstate-ai/memstate-mcp) 📇 ☁️ - Versioned, structured memory for AI agents. Stores facts as keypaths with full version history, automatic conflict detection, and O(1) token retrieval. - [michael-denyer/memory-mcp](https://github.com/michael-denyer/memory-mcp) 🐍 🏠 🍎 🪟 🐧 - Two-tier memory with hot cache (instant injection) and cold semantic search. Auto-promotes frequently-used patterns, extracts knowledge from Claude outputs, and organizes via knowledge graph relationships.