Shared Memory Storage for Multi-Agent Systems

The Cursor10x MCP is a persistent multi-dimensional memory system for Cursor that enhances AI assistants with conversation context, project history, and code relationships across sessions.

AgentChat 是一个基于 LLM 的智能体交流平台，内置默认 Agent 并支持用户自定义 Agent。通过多轮对话和任务协作，Agent 可以理解并协助完成复杂任务。项目集成 LangChain、Function Call、MCP 协议、RAG、Milvus 和 ElasticSearch 等技术，实现高效的知识检索与工具调用，使用 FastAPI 构建高性能后端服务。

Simple standalone MCP server giving Claude the ability to remember your conversations and learn from them over time.

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.

Cursor10x is a comprehensive suite of tools that enhances the A.I. agent's capabilities within the Cursor IDE, providing persistent memory across sessions, standardized task management, and enforced best practices through cursor rules.

AI agent that controls computer with OS-level tools, MCP compatible, works with any model

Agent Memory Playground: AI Agent Memory Design & Optimization Techniques

LangGraph Typescript Agents Notebooks: email, human in the loop, memory

MCP server that execute applescript giving you full control of your Mac

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.

Excuse me sir? Did you order Special Sauce with that Agentic workflow?

This repository introduces the Letta framework, empowering developers to build LLM-based agents with long-term, persistent memory and advanced reasoning capabilities. It leverages concepts from MemGPT to optimize context usage and enable multi-agent collaboration for real-world applications like research, HR, and task management.

This project is a concept demonstration of a layered memory system for Large Language Models. It includes a CLI chatbot and an AI playing Zork I with 'FROTZ' as examples. The true value lies in the memory_handle.py module, designed for easy integration into any Python project requiring LLM memory management (AI agents, games, etc.).

Learn Agentic AI with notes, resources, and practice code covering Generative AI basics, OpenAI agents SDK, prompt engineering, memory, model settings, design patterns and many more.

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.

The llm_to_mcp_integration_engine is a communication layer designed to enhance the reliability of interactions between LLMs and tools (like MCP servers or functions).

🤖 CodeForge AI: An autonomous multi-agent coding system powered by LangGraph for agentic software development and automated workflows. SOTA custom agentic GraphRag, shared-state memory, auto-model routing for cost optimization, and a range of custom tooling.

AI-powered assistant that indexes Google Drive files to a vector store on upload and answers user queries based on the content.

Rails engine that bootstraps AI-ready docs and project memory with SPEC-driven workflows and guides for Rails teams.