"BEM" matching MCP servers:

A structured, persistent reasoning workspace for AI. 20 tools for thought chains, branching, revision, search, tagging, and SQLite session persistence that survives context window resets and server restarts.

Reflexive Memory for AI Agents - MCP Server. Remembers, forgets, questions, and bites.

Enables persistent memory for AI agents, combining episodic and semantic memory with LLM reasoning, accessible via MCP.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Memory-palace graph MCP server for AI agent memory, exposing memory_add / get / like / record_window / walk / retrieve / neighbors / rooms / misshelved / rehome operations over a zero-dependency, append-only, provenance-tracking knowledge graph with decay, PMI-derived adjacency, and spatial coordinates.

An MCP server that provides AI agents with persistent, multi-layered memory inspired by the human brain, including consolidation, self-reflection, and generative replay.

MCP server that enables one-click BIM audits by chaining IFC statistics, validation, and quantity takeoff workflows through natural language queries.

Enables AI agents to control macOS by capturing screenshots and performing mouse clicks, movement, dragging, text typing, hotkeys, and scrolling, with support for Gemini's normalized coordinate protocol and Retina display scaling.

An MCP server that combines sequential thinking with persistent memory through a knowledge graph, enabling AI assistants to explore decision trees by recording thinking traces, branching at low-confidence points, and backtracking to explore alternative paths.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Provides a coherent memory layer for LLM agents that models beliefs as nodes in a constraint network to detect and surface semantic contradictions. It uses local embeddings and a hybrid geometric-logical energy model to identify conflicting information that requires review.