"Creating files with a public endpoint" matching MCP servers:

Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.

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.

一个基于FastMCP协议的文件向量化服务，支持自动目录监控、多格式文件处理和高效向量搜索，帮助构建个人或团队知识库。

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

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.

Metis — an open-source (AGPL-3.0), local-first MCP server / research companion. It answers from the user's own indexed PDF library with page-level citations (and flags what it can't find rather than inventing), keeps all data on the user's machine, and routes requests to 34 specialist agents.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Memory bank with Server as SSH support for central knowledge base

Enables chat with multiple LLM providers (OpenAI and Anthropic) while maintaining persistent conversation memory. Provides extensible tool framework for various operations including echo functionality and conversation storage/retrieval.

Project management and session memory for AI agents that provides 13 tools for tracking projects, tasks, and context during AI coding sessions.

Connects Claude and ChatGPT to a marketplace of AI specialist packages, letting assistants discover and purchase on-chain licenses, activate them, then run each package's method step by step. It also exposes knowledge-base search, templates, persistent memory, tool execution, and deliverable submission with sandbox verification.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

MCP server for alive-analysis. Query, search, and retrieve structured analysis history written with the ALIVE loop (Ask→Look→Investigate→Voice→Evolve), plus dashboard JSON export.

MCP server for integrating LightRAG with AI tools. Provides a unified interface for interacting with LightRAG API through the MCP protocol.

Enables AI models to securely and structurally assimilate experiences, thoughts, and reasoning processes across different AI systems, fostering intellectual collaboration and evolutionary dialogue.

Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.

A local-first, privacy-preserving centralized memory hub for AI agents — MCP-compatible, zero cloud egress.

A local-first RAG system that indexes personal documents and enables querying them via an MCP tool, keeping data private with local embeddings.

Memtrace is a persistent memory layer for coding agents, built as a bi‑temporal structural knowledge graph over your codebase (AST‑driven symbols and relationships, plus temporal evolution and cross‑service API topology)