"Techniques and Strategies to Improve Long-Term Memory" matching MCP servers:

Provides curated long-term memory for Codex CLI via MCP, with Markdown-based storage, deduplication, and decay-based forgetting.

A local MCP server that gives AI assistants persistent long-term memory using a biological neural architecture with cortex, synapses, and hippocampus.

Provides Claude Code with cross-session long-term memory via Letta, allowing it to read and store project context, learnings, and decisions.

Enables AI agents to store and recall persistent long-term memories across sessions using LanceDB, with semantic search, automatic linking, conflict detection, and maintenance tools.

Provides OpenCode with adaptive long-term memory by storing preferences, lessons, and usage history locally in SQLite, enabling AI to remember and adapt to users through context-based learning.

Enables AI agents to store, search, and retrieve long-term memories with BM25 full-text search, auto-tagging, and importance scoring.

Local long-term memory service for AI agents via MCP, using SQLite with tools for memory CRUD, semantic search, time-based facts, and graph capabilities.

Enables agents to maintain persistent memory through three-tiered architecture: short-term session context with TTL, long-term user profiles and preferences, and searchable episodic event history with sentiment analysis. Provides comprehensive memory management for personalized AI interactions.

Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.

Enables AI agents to maintain and sync long-term memory and reusable skills across devices using local folder sync, with support for multiple agents and optional semantic recall.

Local-first MCP server for long-term memory of dev sessions. It indexes session metadata with embeddings in Postgres/pgvector, enables semantic search over past sessions, and resolves only relevant session files for context.

Enables AI agents to search, filter, and monitor curated AI model API free credits and low-cost token promotions through eight tools, four resources, and three prompts — including finding deals expiring soon, checking eligibility and blocked conditions, and producing daily briefs.

Provides long-term memory for AI coding agents, enabling them to remember, search, and organize information across sessions and platforms like Claude Code, ChatGPT, and Cursor.

MCP server enabling AI agents to interact with OpenProject API v3 for project management, including creating and managing work packages, projects, comments, time entries, boards, and user dashboards.

Provides a local long-term memory layer for AI coding tools like Cursor and Claude Code, enabling cross-session, cross-tool sharing of project facts, user preferences, decisions, and workflows.

MCP server for context-aware memory and retrieval with hybrid search, dual-phase memory, and boundary-first security.

Enables agents to persist, search, and manage short- and long-term memories using local SQLite storage and offline embeddings, with tools for recall, update, confirmation, and consolidation without external services.

A persistent, project-scoped memory layer for AI coding agents, providing 39 tools for capturing, searching, deduplicating, relating, and maintaining memory records across long-running coding sessions.

Provides persistent long-term memory for LLMs via local SQLite storage and semantic search, enabling recall across sessions without external APIs.

A local-first long-term memory system for AI coding agents, exposed as an MCP server.