"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 a local markdown-based memory system for coding agents through MCP, enabling search, add, register, inventory, sync, and ingest operations across user and project memory. It gives any agent tool a durable, provider-agnostic shared memory stored in folders you own.

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

Local, searchable project memory for AI coding agents. Markdown source of truth, MCP interface, safe structured updates

An MCP server that allows users to run and visualize systems models using the lethain:systems library, including capabilities to run model specifications and load systems documentation into the context window.

Provides local-first, project-aware durable memory for coding agents, with a human-reviewed Markdown vault, MCP daemon, and dashboard for managing and retrieving shared knowledge.

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.

A lightweight MCP data broker that stores large binary payloads under a UUID, enabling MCP clients and agents to exchange massive results without flooding the LLM context window.

MCP-native, local-first memory for coding agents that turns real sessions into reusable decisions, gotchas, and domain knowledge.

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.

Enables AI coding assistants to persist, semantically search, and organize memories across sessions on Cloudflare's free tier, including conversation search, reminders, and automated reflection.

A lightweight MCP memory engine for AI coding agents that provides zero-token-waste memory recall, knowledge graph management, and Obsidian-compatible notes. It enables agents to persist and retrieve project decisions, inspect file structures efficiently, and manage a persistent knowledge graph without Python dependencies.

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.

Lets coding agents like Codex, Cursor, and Claude Code share and resume each other's chats, and attach to a common task with saved notes that can be searched later. This keeps task state and decisions in sync no matter which agent you switch to.

Enables AI agents to access user and message data through MCP resources, providing REST API integration for user management with paginated lists and thread tracking.

Local-first memory, pipelines, learning, feedback, and safe code tools for AI coding agents.

Orchestrates multiple LLM agents to perform parallel code review, then synthesizes and votes on suggestions to rank improvements by consensus.

Aggregates multiple shared MCP servers into a single HTTP gateway, providing a unified, observable, and reusable MCP access layer for multiple AI clients like Codex, OpenCode, and OpenClaw. It centralizes configuration management, logging, health checks, and circuit breaking to simplify multi-client MCP deployments.

MCP server that keeps language server sessions warm and routes multiple languages through one process. Agents get persistent cross-file awareness, speculative execution (simulate edits before writing to disk), and 20 skills that encode correct multi-step operations like safe rename, blast-radius analysis, and end-to-end refactoring. Single Go binary, no runtime dependencies.