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Merge pull request #7369 from alan5543/add-beever-atlas

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Add Beever-AI/beever-atlas to Knowledge & Memory 🤖🤖🤖
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Frank Fiegel
2026-06-14 18:09:47 -07:00
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@@ -1826,6 +1826,7 @@ Persistent memory storage using knowledge graph structures. Enables AI models to
- [ayushagrawal288/memex](https://github.com/ayushagrawal288/memex) [![ayushagrawal288/memex MCP server](https://glama.ai/mcp/servers/ayushagrawal288/memex/badges/score.svg)](https://glama.ai/mcp/servers/ayushagrawal288/memex) 🐍 🏠 - Production-grade persistent memory service for AI agents. Semantic search with recency decay (configurable α blend), local ONNX embeddings (zero API cost), background memory summarisation, and Prometheus/Grafana observability. `docker compose up` — no cloud dependencies. - [ayushagrawal288/memex](https://github.com/ayushagrawal288/memex) [![ayushagrawal288/memex MCP server](https://glama.ai/mcp/servers/ayushagrawal288/memex/badges/score.svg)](https://glama.ai/mcp/servers/ayushagrawal288/memex) 🐍 🏠 - Production-grade persistent memory service for AI agents. Semantic search with recency decay (configurable α blend), local ONNX embeddings (zero API cost), background memory summarisation, and Prometheus/Grafana observability. `docker compose up` — no cloud dependencies.
- [basicmachines-co/basic-memory](https://github.com/basicmachines-co/basic-memory) [![basic-memory MCP server](https://glama.ai/mcp/servers/@basicmachines-co/basic-memory/badges/score.svg)](https://glama.ai/mcp/servers/@basicmachines-co/basic-memory) 🐍 🏠 ☁️ 🍎 🪟 🐧 - Persistent, local-first AI memory: a semantic knowledge graph of plain Markdown files that humans and LLMs both read and write. Works with any MCP client, with optional cloud sync and team workspaces. - [basicmachines-co/basic-memory](https://github.com/basicmachines-co/basic-memory) [![basic-memory MCP server](https://glama.ai/mcp/servers/@basicmachines-co/basic-memory/badges/score.svg)](https://glama.ai/mcp/servers/@basicmachines-co/basic-memory) 🐍 🏠 ☁️ 🍎 🪟 🐧 - Persistent, local-first AI memory: a semantic knowledge graph of plain Markdown files that humans and LLMs both read and write. Works with any MCP client, with optional cloud sync and team workspaces.
- [Battam1111/Myco](https://github.com/Battam1111/Myco) [![Battam1111/Myco MCP server](https://glama.ai/mcp/servers/Battam1111/Myco/badges/score.svg)](https://glama.ai/mcp/servers/Battam1111/Myco) 🐍 🏠 🍎 🪟 🐧 - Agent-first cognitive substrate with 18 manifest-driven verbs (germinate / eat / assimilate / sporulate / traverse / immune / molt / …) and 25 lint dimensions enforcing contract invariants mechanically (R1–R7). Cross-session / cross-project memory via a self-validating filesystem graph — AST + markdown-link derived, not embedding-based. Provider-agnostic by design: MP1/MP2 dims forbid LLM-SDK imports in the kernel and plugin tree. Editable-default install. Works with Claude Code, Cursor, Windsurf, Zed, VS Code, and any MCP client. - [Battam1111/Myco](https://github.com/Battam1111/Myco) [![Battam1111/Myco MCP server](https://glama.ai/mcp/servers/Battam1111/Myco/badges/score.svg)](https://glama.ai/mcp/servers/Battam1111/Myco) 🐍 🏠 🍎 🪟 🐧 - Agent-first cognitive substrate with 18 manifest-driven verbs (germinate / eat / assimilate / sporulate / traverse / immune / molt / …) and 25 lint dimensions enforcing contract invariants mechanically (R1–R7). Cross-session / cross-project memory via a self-validating filesystem graph — AST + markdown-link derived, not embedding-based. Provider-agnostic by design: MP1/MP2 dims forbid LLM-SDK imports in the kernel and plugin tree. Editable-default install. Works with Claude Code, Cursor, Windsurf, Zed, VS Code, and any MCP client.
- [Beever-AI/beever-atlas](https://github.com/Beever-AI/beever-atlas) [![Beever-AI/beever-atlas MCP server](https://glama.ai/mcp/servers/Beever-AI/beever-atlas/badges/score.svg)](https://glama.ai/mcp/servers/Beever-AI/beever-atlas) 🐍 🏠 🍎 🪟 🐧 - Open-source LLM knowledge base for teams. 28-tool native MCP server turns Slack/Discord/Teams/Mattermost chat into a typed knowledge graph + auto-generated wiki with cited answers, semantic search, expert finding, and decision tracing. BYO LLM via LiteLLM, Apache 2.0, on-prem via Docker.
- [bitatlas-group/bitatlas](https://github.com/bitatlas-group/bitatlas) [![bitatlas MCP server](https://glama.ai/mcp/servers/bitatlas-group/bitatlas/badges/score.svg)](https://glama.ai/mcp/servers/bitatlas-group/bitatlas) 📇 ☁️ - Zero-Knowledge Cloud Drive for Humans and Agents. Client-side AES-256-GCM encryption with 7 MCP tools for encrypted file vault management — upload, download, search, and organize files that the server never sees in plaintext. - [bitatlas-group/bitatlas](https://github.com/bitatlas-group/bitatlas) [![bitatlas MCP server](https://glama.ai/mcp/servers/bitatlas-group/bitatlas/badges/score.svg)](https://glama.ai/mcp/servers/bitatlas-group/bitatlas) 📇 ☁️ - Zero-Knowledge Cloud Drive for Humans and Agents. Client-side AES-256-GCM encryption with 7 MCP tools for encrypted file vault management — upload, download, search, and organize files that the server never sees in plaintext.
- [besslframework-stack/project-tessera](https://github.com/besslframework-stack/project-tessera) [![project-tessera MCP server](https://glama.ai/mcp/servers/@besslframework-stack/project-tessera/badges/score.svg)](https://glama.ai/mcp/servers/@besslframework-stack/project-tessera) 🐍 🏠 🍎 🪟 🐧 - Local workspace memory for Claude Desktop. Indexes your documents (Markdown, CSV, session logs) into a vector store with hybrid search, cross-session memory, auto-learn, and knowledge graph visualization. Zero external dependencies — fastembed + LanceDB, no Ollama or Docker required. 15 MCP tools. - [besslframework-stack/project-tessera](https://github.com/besslframework-stack/project-tessera) [![project-tessera MCP server](https://glama.ai/mcp/servers/@besslframework-stack/project-tessera/badges/score.svg)](https://glama.ai/mcp/servers/@besslframework-stack/project-tessera) 🐍 🏠 🍎 🪟 🐧 - Local workspace memory for Claude Desktop. Indexes your documents (Markdown, CSV, session logs) into a vector store with hybrid search, cross-session memory, auto-learn, and knowledge graph visualization. Zero external dependencies — fastembed + LanceDB, no Ollama or Docker required. 15 MCP tools.
- [bh-rat/context-awesome](https://github.com/bh-rat/context-awesome) 📇 ☁️ 🏠 - MCP server for querying 8,500+ curated awesome lists (1M+ items) and fetching the best resources for your agent. - [bh-rat/context-awesome](https://github.com/bh-rat/context-awesome) 📇 ☁️ 🏠 - MCP server for querying 8,500+ curated awesome lists (1M+ items) and fetching the best resources for your agent.