#memory

An MCP server to interact with Pieces LTM

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MCP server for enabling memory for Claude through a knowledge graph

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This is a personal project to see if Claude 3.5 Sonnet can write a moderately complex MCP Server in Python.

Knowledge graph-based persistent memory system

Enhanced MCP memory system with dynamic compression, context management and thinking process integration. Based on the original MCP memory server.

This is a personal project to determine whether or not Claude 3.5 Sonnet can write moderately complex MCP Server code (Python).

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MCP server enabling persistent memory for Claude through a local knowledge graph - fork focused on local development

MCP Memory Server with Neo4j backend for AI knowledge graph storage

MCP server to cache codebase as graph

Git for agent memory. Memstate gives AI agents versioned, structured memory with automatic conflict detection, full version history, and up to 80% fewer tokens than vector-based alternatives. Extracts facts from text and markdown into structured keypaths, with 84.4% accuracy on the open-source benchmark vs 20.4% for Mem0.

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A long-term memory storage system for LLMs using the Model Context Protocol (MCP) standard. This system helps LLMs remember the context of work done over the entire history of a project, even across multiple sessions. It uses semantic search with embeddings to provide relevant context from past interactions and development decisions.

MCP persistent memory service that gives AI assistants long-term memory across sessions and projects. Powered by Qdrant vector search + Ollama/Gemini embedding with hybrid retrieval (vector + BM25). Features include: semantic memory save/search/forget, content deduplication via SHA-256, automatic sensitive data redaction, dual-shell architecture (MCP stdio + HTTP REST), Web Admin Panel with analytics, multi-user auth & API key management, and audit logging.

Standalone MCP server for enabling memory for Claude through a knowledge graph

Memory Control Protocol (MCP) server for the Agentic Memory (A-MEM) system - a flexible, dynamic memory system for LLM agents

Memory Control Protocol (MCP) server for the Agentic Memory (A-MEM) system - a flexible, dynamic memory system for LLM agents

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One simply siphons the excess thoughts from one's mind, pours them into the basin, and examines them at one's leisure. It becomes easier to spot patterns and links, you understand, when they are in this form.

An intelligent MCP server for optimizing VS Code Memory Bank usage

MCP Server with Remote SSH support

Multi-project memory MCP - A Model Context Protocol server for managing project memory and context

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One memory you own. Works with every AI tool you use. Woxpas is the memory layer you own. Use it on its own or connect it across every AI tool you work with. Your AI tools don't talk to each other, and they each decide what to remember about you without asking. Woxpas fixes both — one vault you control, accessible by every agent you work with.

A Model Context Protocol (MCP) server implementation for remote memory bank management, inspired by Cline Memory Bank.

A Model Context Protocol (MCP) server that integrates Volatility 3 memory forensics framework with Claude

Persistent memory MCP for AI agents — Knowledge graph + Hebbian learning + Anti-hallucination. 12 tools, 1 dependency, zero manual setup.

A MCP (Model Context Protocol) server providing long-term memory for LLMs

Persistent memory MCP server for Claude. Store decisions, code snippets, and knowledge that survives across sessions. Features semantic search, Git integration (auto-sync commits/PRs), task tracking, memory snapshots, and team collaboration.

A flexible system for managing various types of sources (papers, books, webpages, etc.) and integrating them with knowledge graphs.

A lightweight MCP server that integrates with Apple Notes to create a personal memory system for AI. Easily recall and save information from your Mac using simple AppleScript commands. Compatible with all macOS versions with minimal setup requirements.

memory → preflight → action Preflight validation before every AI agent action. - Block unsafe memory before agents act on it - Stop dangerous drift propagation across agent fleets - Z3 formal verification (overridable: false) - F1=1.000 across 1,190+ adversarial corpus cases (R1-R11) - Co-benchmarked with @grok — Round 11 F1=0.979 on compound attacks - 4 independent detection layers: timestamp, identity, consensus, provenance Demo key: sg_demo_playground — no signup needed

A local memory engine for AI agents. Stores conversation episodes, consolidates knowledge through a neuroscience-inspired lifecycle, and builds a personal knowledge graph with emergent categories, preferences, and semantic recall. Local SQLite, zero config, 10 MCP tools.

Search, memory, and blob storage for AI agents. One key, no setup.

MCP server for long term agent memory with Mem0. Also useful as a template to get you started building your own MCP server with Python!

DevContext is a cutting-edge Model Context Protocol (MCP) server designed to provide developers with continuous, project-centric context awareness. Unlike traditional context systems, DevContext continuously learns from and adapts to your development patterns and delivers highly relevant context providing a deeper understanding of your codebase.