"Multi-Agent Systems with Shared Memory Storage" matching MCP servers:

Enables AI coding agents to store and retrieve durable project decisions, conventions, environment notes, and bug fixes in Notion, with optional Obsidian Vault dual-write.

Provides local-first persistent memory with a typed knowledge graph and bounded multi-hop retrieval via MCP, letting coding agents and local LLM systems store, search, and recall facts across sessions without hosted services or model dependencies.

Provides a shared context layer for AI agent teams to improve token efficiency through context deduplication and incremental state sharing. It enables multiple agents to coordinate tasks, share real-time discoveries, and manage dependencies while significantly reducing redundant data transmission.

An MCP server that exposes web lookup, save note, and list notes tools for a router-research-writer agent pipeline, enabling coordinated multi-agent workflows via the Model Context Protocol.

MCP server that exposes agent-memory-daemon to any MCP-compatible client — Kiro (CLI & IDE), Claude Desktop, Cursor, and others. The daemon does the thinking (consolidation + extraction); this server is a thin filesystem bridge so agents can read, append, and search memory through the Model Context Protocol.

An MCP server that gives multiple agents a shared versioned memory with explicit handoff packets, enabling structured context transfer and stale data detection across sessions.

A knowledge graph memory server using SQLite to provide persistent, isolated contexts for organizing information into searchable categories like work and personal projects. It features unique ID-based operations and a token-efficient serialization format designed to optimize interactions with LLMs.

Multi-tier memory server for AI agents with hybrid search across episodic, semantic, and procedural memory.

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.

An MCP server that orchestrates multiple AI agents in parallel to get diverse perspectives on a single topic, supporting debate, review, and quick modes.

MCP server allowing two agents to play chess or Connect Four against each other, with a live rendered board and emotion signaling.

Persistent memory for MCP-powered coding agents, allowing LLMs to remember preferences, project context, and decisions across sessions via Markdown files.

Orchestrates multiple AI agents (Product Manager, Software Architect, Engineer, QA, Reviewer) to collaboratively plan, design, implement, review, and improve software development projects via MCP tools.

Provides systems thinking models as composable analysis lenses for architecture, infrastructure, DevOps, incident analysis, and technical decision-making.

An MCP server for managing agent memory using provenance tracking, decay-weighted retrieval, and feedback loops to optimize information recall. It allows agents to store insights in a local SQLite database and rank them based on confidence, age, and usefulness.

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

Enables secure file and bucket management operations with Supabase Storage through enterprise-grade security features, batch uploads/downloads, and comprehensive file management capabilities. Supports both local file paths and base64 data with advanced transformations and auto-download functionality.

MCP server for agent-maintained memories, allowing creation, reading, updating, deletion, and listing of memories with global or repo scopes.

Enables AI agents to search and retrieve past coding-tool conversations across Claude Code, Claude Desktop, Codex, and Cursor through a local, read-only index.

A self-improving memory server that captures, organizes, and shares memories across workspaces, builds a personal knowledge graph, learns preferences, and recommends tools to limit token usage.