From e7b761dbae279bb60bc4244cf078b37795bbab04 Mon Sep 17 00:00:00 2001 From: Thomas Stockham Date: Mon, 23 Feb 2026 20:17:11 -0700 Subject: [PATCH 1/3] Add engram-sdk to Knowledge & Memory --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index fd0d62c9..9acc9ee3 100644 --- a/README.md +++ b/README.md @@ -1071,6 +1071,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to - [dodopayments/contextmcp](https://github.com/dodopayments/context-mcp) 📇 ☁️ 🏠 - Self-hosted MCP server that indexes documentation from various sources and serves it to AI Agents with semantic search. - [doobidoo/MCP-Context-Provider](https://github.com/doobidoo/MCP-Context-Provider) 📇 🏠 - Static server that provides persistent tool-specific context and rules for AI models - [doobidoo/mcp-memory-service](https://github.com/doobidoo/mcp-memory-service) 📇 🏠 - Universal memory service providing semantic search, persistent storage, and autonomous memory consolidation +- [engram-sdk](https://github.com/tstockham96/engram) 📇 🏠 🍎 🪟 🐧 - Universal memory layer for AI agents with vector search, automatic consolidation, bi-temporal recall, and MCP tools. Zero-dependency Node.js server, works with any LLM provider. - [entanglr/zettelkasten-mcp](https://github.com/entanglr/zettelkasten-mcp) 🐍 🏠 - A Model Context Protocol (MCP) server that implements the Zettelkasten knowledge management methodology, allowing you to create, link, and search atomic notes through Claude and other MCP-compatible clients. - [g1itchbot8888-del/agent-memory](https://github.com/g1itchbot8888-del/agent-memory) 🐍 🏠 - Three-layer memory system for agents (identity/active/archive) with semantic search, graph relationships, conflict detection, and LearningMachine. Built by an agent, for agents. No API keys required. - [ErebusEnigma/context-memory](https://github.com/ErebusEnigma/context-memory) 🐍 🏠 🍎 🪟 🐧 - Persistent, searchable context storage across Claude Code sessions using SQLite FTS5. Save sessions with AI-generated summaries, two-tier full-text search, checkpoint recovery, and a web dashboard. From 13950685fd74514ed1e27f1669728c528e6b168b Mon Sep 17 00:00:00 2001 From: tstockham96 <35585164+tstockham96@users.noreply.github.com> Date: Fri, 27 Feb 2026 14:44:24 -0700 Subject: [PATCH 2/3] Add Engram to Knowledge & Memory section --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9acc9ee3..e25ba54c 100644 --- a/README.md +++ b/README.md @@ -1071,7 +1071,6 @@ Persistent memory storage using knowledge graph structures. Enables AI models to - [dodopayments/contextmcp](https://github.com/dodopayments/context-mcp) 📇 ☁️ 🏠 - Self-hosted MCP server that indexes documentation from various sources and serves it to AI Agents with semantic search. - [doobidoo/MCP-Context-Provider](https://github.com/doobidoo/MCP-Context-Provider) 📇 🏠 - Static server that provides persistent tool-specific context and rules for AI models - [doobidoo/mcp-memory-service](https://github.com/doobidoo/mcp-memory-service) 📇 🏠 - Universal memory service providing semantic search, persistent storage, and autonomous memory consolidation -- [engram-sdk](https://github.com/tstockham96/engram) 📇 🏠 🍎 🪟 🐧 - Universal memory layer for AI agents with vector search, automatic consolidation, bi-temporal recall, and MCP tools. Zero-dependency Node.js server, works with any LLM provider. - [entanglr/zettelkasten-mcp](https://github.com/entanglr/zettelkasten-mcp) 🐍 🏠 - A Model Context Protocol (MCP) server that implements the Zettelkasten knowledge management methodology, allowing you to create, link, and search atomic notes through Claude and other MCP-compatible clients. - [g1itchbot8888-del/agent-memory](https://github.com/g1itchbot8888-del/agent-memory) 🐍 🏠 - Three-layer memory system for agents (identity/active/archive) with semantic search, graph relationships, conflict detection, and LearningMachine. Built by an agent, for agents. No API keys required. - [ErebusEnigma/context-memory](https://github.com/ErebusEnigma/context-memory) 🐍 🏠 🍎 🪟 🐧 - Persistent, searchable context storage across Claude Code sessions using SQLite FTS5. Save sessions with AI-generated summaries, two-tier full-text search, checkpoint recovery, and a web dashboard. @@ -1116,6 +1115,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to - [topoteretes/cognee](https://github.com/topoteretes/cognee/tree/dev/cognee-mcp) 📇 🏠 - Memory manager for AI apps and Agents using various graph and vector stores and allowing ingestion from 30+ data sources - [timowhite88/farnsworth-syntek](https://github.com/timowhite88/farnsworth-syntek) 📇 ☁️ - 7-layer recursive agent memory with context branching, holographic recall, dream consolidation, and on-chain persistence. MCP-native with 10 tools for persistent agent cognition. - [topskychen/tilde](https://github.com/topskychen/tilde) 🐍 🏠 - Your AI agents' home directory — privacy-first MCP server for portable AI identity. Configure once, use everywhere. It supports profile management, skills, resume import, and team sync. +- [tstockham96/engram](https://github.com/tstockham96/engram) 📇 🏠 🍎 🪟 🐧 - Intelligent agent memory with semantic recall, automatic consolidation, contradiction detection, and bi-temporal knowledge graph. 80% on LOCOMO benchmark using 96% fewer tokens than full-context approaches. - [TeamSafeAI/LIFE](https://github.com/TeamSafeAI/LIFE) 🐍 🏠 - Persistent identity architecture for AI agents. 16 MCP servers covering drives, emotional relationships, semantic memory with decay, working threads, learned patterns, journal, genesis (identity discovery), creative collision engine, forecasting, and voice. Zero dependencies beyond Python 3.8. Built across 938 conversations. - [unibaseio/membase-mcp](https://github.com/unibaseio/membase-mcp) 📇 ☁️ - Save and query your agent memory in distributed way by Membase - [upstash/context7](https://github.com/upstash/context7) 📇 ☁️ - Up-to-date code documentation for LLMs and AI code editors. From 748c8f2c108f743c35a0bd6e6d208d71b890562d Mon Sep 17 00:00:00 2001 From: Thomas Stockham Date: Sun, 1 Mar 2026 14:50:20 -0700 Subject: [PATCH 3/3] Add Glama listing link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e25ba54c..8aa8262c 100644 --- a/README.md +++ b/README.md @@ -1115,7 +1115,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to - [topoteretes/cognee](https://github.com/topoteretes/cognee/tree/dev/cognee-mcp) 📇 🏠 - Memory manager for AI apps and Agents using various graph and vector stores and allowing ingestion from 30+ data sources - [timowhite88/farnsworth-syntek](https://github.com/timowhite88/farnsworth-syntek) 📇 ☁️ - 7-layer recursive agent memory with context branching, holographic recall, dream consolidation, and on-chain persistence. MCP-native with 10 tools for persistent agent cognition. - [topskychen/tilde](https://github.com/topskychen/tilde) 🐍 🏠 - Your AI agents' home directory — privacy-first MCP server for portable AI identity. Configure once, use everywhere. It supports profile management, skills, resume import, and team sync. -- [tstockham96/engram](https://github.com/tstockham96/engram) 📇 🏠 🍎 🪟 🐧 - Intelligent agent memory with semantic recall, automatic consolidation, contradiction detection, and bi-temporal knowledge graph. 80% on LOCOMO benchmark using 96% fewer tokens than full-context approaches. +- [tstockham96/engram](https://github.com/tstockham96/engram) [glama](https://glama.ai/mcp/servers/@tstockham96/engram) 📇 🏠 🍎 🪟 🐧 - Intelligent agent memory with semantic recall, automatic consolidation, contradiction detection, and bi-temporal knowledge graph. 80% on LOCOMO benchmark using 96% fewer tokens than full-context approaches. - [TeamSafeAI/LIFE](https://github.com/TeamSafeAI/LIFE) 🐍 🏠 - Persistent identity architecture for AI agents. 16 MCP servers covering drives, emotional relationships, semantic memory with decay, working threads, learned patterns, journal, genesis (identity discovery), creative collision engine, forecasting, and voice. Zero dependencies beyond Python 3.8. Built across 938 conversations. - [unibaseio/membase-mcp](https://github.com/unibaseio/membase-mcp) 📇 ☁️ - Save and query your agent memory in distributed way by Membase - [upstash/context7](https://github.com/upstash/context7) 📇 ☁️ - Up-to-date code documentation for LLMs and AI code editors.