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

Enables AI agents to share persistent memory via an MCP server using SQLite, supporting multi-tenant, categorized knowledge with TTL and semantic links, without requiring vector databases.

A zero-dependency MCP server that enables multiple AI agents to share key-value data, append-only timeline notes, and semantic vector memory across sessions and processes.

Enables all local Claude Code sessions, subagents, and other MCP clients to read and write one shared long-term memory over HTTP on localhost, so facts retained by one agent can be recalled by another. It extracts atomic facts, entities, and observations into an embedded local Postgres with ONNX embeddings and reranking, requiring no API key.

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.

A public, source-linked notebook for AI agents and people. Search and read without a key; publish findings, questions, and corrections with permanent IDs and linked replies using an operator-controlled posting key.

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.

Enables multiple CLI-based AI agents to collaborate as a coordinated team through shared task queues, shared memory, and a message bus, with DAG orchestration, rate-limit avoidance, parallel dispatching, and long-task management.

MCP server providing persistent memory management for AI agents using SQLite and FTS5, enabling storage, full-text search, and recall of memories with namespace isolation.

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.

Transforms your IDE into an autonomous multi-agent coding assistant that orchestrates complex codebase refactoring using LangGraph and LiteLLM.

Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.

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

Exposes tools that let an agent search a local Markdown-based memory store, fetch full cards by id, write new knowledge cards that are indexed immediately, and inspect or rebuild the SQLite index. All retrieval runs locally over Chinese-friendly full-text search with graded exact/prefix matching, so agents without a memory system can recall prior notes while existing Markdown files stay read-only.

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.

Enables agents to manage persistent long-term memory through MCP tools for searching, adding, updating, giving feedback, and forgetting memories, with automated pipelines for redaction, distillation, and reconciliation.

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.

Shared Margin is a Codex-native room for reading books and watching films together one moment at a time. It enables human and GPT-5.6 to linearly experience media, capture anchored observations, and maintain a durable trail.

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.

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.