"Maintaining Consistent Behavior in System Prompts" matching MCP servers:

MCP server for managing AI prompts with CRUD operations, categorization, and tagging. Enables users to store, organize, and retrieve their favorite prompts efficiently.

Persistent memory and pattern learning for AI coding assistants via MCP.

Local-first knowledge system for reasoning agents, exposing facts, evidence, documents, retrieval, and audit history through a thin stdio MCP server.

Enables persistent storage and retrieval of decisions, settings, and operational rules across chat sessions, maintaining context continuity and decision consistency for long-term development projects through structured memory management.

Automatically generates and manages a prompts system for software projects, enabling persistent context for AI coding assistants through project scanning, requirement clarification, and module tracking.

About Your task manager is the best agent memory you're not using. An agent-native context layer over your existing task app.

Cross-project memory for Claude Code, enabling local semantic recall and secure, git-versioned markdown storage of reusable knowledge across repositories.

Enables real-time auditing of LLM long-term memory to detect contradictions, user-introduced falsehoods, staleness, and unsourced fabrications, while providing a safe memory store with tools like recall, review, and restore.

Provides AI clients with a persistent, readable and writable long-term memory layer over MCP, enabling creation, retrieval, tagging, updating, and deletion of memories across sessions.

Enables AI assistants to maintain persistent conversations and context between sessions through automated saving and global installation across projects. Provides zero-configuration memory persistence with automatic conversation history preservation.

Enables MCP-capable AI clients to read and write structured, project-scoped memory through a Cloudflare Worker backed by D1 and R2.

This MCP server provides an AI-driven learning and knowledge management platform that automatically summarizes conversations, manages knowledge graphs, tracks project progress, explores new technologies, and generates study plans through specialized agents.

Enables agents to optimize context-window attention by reordering retrieved documents so critical evidence lands at prompt boundaries, mitigating lost-in-the-middle degradation. It combines BM25 lexical scoring, dense vector similarity, reciprocal rank fusion, knowledge-graph expansion, and recency-decay memory caching, exposed as a zero-dependency MCP stdio server.

Enables agents to run hybrid retrieval, reciprocal rank fusion, knowledge graph expansion, semantic caching, and context reordering so critical evidence is placed at prompt boundaries to reduce lost-in-the-middle degradation.

An AI desire-driven system that maintains nine core drive dimensions, generates thoughts and behavior suggestions, and provides tools to inspect and trigger events via MCP.

An MCP server that provides searchable local storage for Claude conversation history, featuring automatic topic extraction and weekly insight summaries. It enables Claude to retrieve context from past sessions through full-text search and organized file storage.

A flexible memory system for AI applications that supports multiple LLM providers and can be used either as an MCP server or as a direct library integration, enabling autonomous memory management without explicit commands.

A local-first, open-source harness for organizing your files with an AI agent.

A Python-based system that provides AI-powered code reviews through simulated expert personas like Martin Fowler and Robert C. Martin, using the Model Context Protocol (MCP).

Enables persistent memory and semantic search for development workflows with hierarchical compression. Store and retrieve development knowledge across IDE sessions using natural language queries, circumventing context window limitations.