"Memory systems for AI agents" matching MCP servers:

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

An MCP server that provides persistent memory capabilities for AI agents using Mem0, enabling storage, search, and management of contextual information across conversations with support for multiple backends and LLM providers.

Provides persistent session memory for AI assistants, enabling them to store, search, and retrieve conversation summaries across sessions via the Model Context Protocol.

Enables AI agents to persist memory across sessions through MCP, supporting semantic retrieval, source-backed context, knowledge graph relationships, supersession lineage, and topic digests.

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.

Provides AI agents and teams with self-hosted adaptive memory, combining hybrid search and a self-improving neural memory graph to enable precise recall and serendipitous discovery across MCP-compatible clients.

Persistent memory for any AI assistant. Zero token cost until recall. Stores memories in local SQLite, ranks by 6-factor scoring, returns results 79% smaller than JSON. Works with Claude, ChatGPT, Grok, Cursor, Windsurf, and any MCP client.

MCP server for zero-config, traceable long-term memory using SQLite, enabling agents to store, search, trace, and monitor memory with tools like memory_store, memory_search, and memory_health.

A persistent, trust-scored project memory for AI coding agents, backed by PostgreSQL + pgvector, providing durable memory of architecture decisions, bug patterns, and coding conventions.

A memory, docs, and repo context layer for engineering agents, providing typed persistent memory, RAG document retrieval, and repository-aware tools to recall decisions, runbooks, and operational knowledge across sessions.

Give long term memory to your agents without building the RAG pipelines.

Enables AI agents to store, retrieve, and manage contextual knowledge across sessions using semantic search with PostgreSQL and vector embeddings. Supports memory relationships, clustering, multi-agent isolation, and intelligent caching for persistent conversational context.

Provides persistent, globally-distributed memory for AI agents using CockroachDB's MCP Server and AWS S3 backups, with session isolation, audit trails, and full-text search.

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.

Enables real-time user intervention for MCP agents through a web UI, allowing users to review context and provide feedback when AI agents drift from intent, keeping them on track.

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

Ratary is an AI Brain Platform — infrastructure that gives AI: Persistent memory — durable, owner-scoped, versioned Structured knowledge — metadata, relations, graph traversal Intelligent retrieval — hybrid search + bounded context assembly Protocol access — Ratary MCP, REST, optional gRPC It sits between AI clients and storage. One brain, many surfaces — Cursor, Claude Code, custom agents, enter

Self-hosted personal knowledge graph for Claude that persists across sessions, devices, and tools. Built on Neo4j with local semantic embeddings; OAuth 2.1 lets Claude Code, Claude Desktop, and claude.ai web all hit the same graph.

Multi-agent swarm orchestrator for AI workflows. Exposes blackboard read/write, agent dispatch, permission gating (AuthGuardian), token budget enforcement (FederatedBudget), and audit log tools over MCP. Supports 14 AI framework adapters including LangChain, AutoGen, CrewAI, Codex, and A2A — mix any frameworks in one swarm with race-condition-safe shared state.(Traffic light for agents)

Enables AI agents to search and browse a curated catalog of 330+ third-party APIs and MCP servers using tools like search_apis, get_api, get_reviews, and list_categories, with no API key required.