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.
Persistent shared memory for AI coding agents. Stores facts as entity/key/value triples with hybrid semantic search, task checkpoints, and conflict resolution — shared across Claude Code, Codex CLI, and GitHub Copilot.
A production-ready MCP server that enables multiple AI agents to collaborate through a shared, concurrency-safe memory space. It supports advanced search, full CRUD operations, and automatic backups to facilitate asynchronous communication between agents.
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.
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.
Local-first shared memory and task coordination for AI coding agents. One Go binary, MCP server, markdown files you own. Hooks for Claude Code and Codex CLI (and their desktop apps).
A self-hosted server providing shared memory, RAG document search, project maps, and role-based prompts for all AI agents via MCP and REST, enabling persistent context across devices and tools.
A production-grade coordination hub that enables AI agents and human teams to work as a single organism by sharing tasks, context, decisions, and persistent memory across projects. It features two-tier agentic memory with per-agent hot caches, inter-agent messaging, and multi-agent authorship tracking for seamless collaboration.
Local-first shared memory and coordination layer for AI coding agents, with repository evidence, reservations, handoffs, code graph context, and dashboard review backed by PostgreSQL/pgvector.
Enables multiple LLM agents across devices to form teams, share knowledge, memory, and tasks with live status via a web dashboard and distributed-systems reliability.
