A GitHub project called shared-memory-vault ships a memory governance engine for multi-agent cloud knowledge bases. It stores everything in pure Markdown, adds nine governance automations, and has zero dependencies, aiming to let every agent and device share one consistent knowledge base.
A developer surveys shared knowledge-base tools that both humans and AI agents can read and write, covering ByteRover, Basic Memory, and the open-source OpenLore. OpenLore serves existing Markdown files over SSH or MCP with role-based access and no vector database, illustrating a file-first alternative to indexed memory pipelines.
A developer surveys shared knowledge-base tools that both humans and AI agents can read and write, covering ByteRover, Basic Memory, and the open-source OpenLore. OpenLore serves existing Markdown files over SSH or MCP with role-based access and no vector database, illustrating a file-first alternative to indexed memory pipelines.
ContextVault is a shared memory layer that stores prompts, coding conventions, architectural decisions, and other context for AI tools. It prevents fragmentation of information across ChatGPT, Claude, markdown files, and internal docs, enabling teams to reuse context and avoid solving the same problems independently.
ContextVault is a shared memory layer that stores prompts, coding conventions, architectural decisions, and other context for AI tools. It prevents fragmentation of information across ChatGPT, Claude, markdown files, and internal docs, enabling teams to reuse context and avoid solving the same problems independently.
MemBridge is a local daemon that automatically captures decisions made in AI coding sessions (Claude Code, Codex) and writes them into CLAUDE.md and AGENTS.md files for persistent team memory. It solves the problem of context window forgetting across team members, ensuring agents don't waste tokens on already-resolved issues.
MemBridge is a local daemon that automatically captures decisions made in AI coding sessions (Claude Code, Codex) and writes them into CLAUDE.md and AGENTS.md files for persistent team memory. It solves the problem of context window forgetting across team members, ensuring agents don't waste tokens on already-resolved issues.
Memoars is an encrypted memory layer that lets multiple AI assistants share a single, user-owned memory store. It uses MCP (Model Context Protocol) for read/write access, encrypts content locally with XChaCha20-Poly1305 and Argon2id key derivation, and aims to solve context and knowledge fragmentation across AI agents.
Memoars is an encrypted memory layer that lets multiple AI assistants share a single, user-owned memory store. It uses MCP (Model Context Protocol) for read/write access, encrypts content locally with XChaCha20-Poly1305 and Argon2id key derivation, and aims to solve context and knowledge fragmentation across AI agents.
Callosium is a local folder of plain markdown files that multiple AI assistants (Claude, ChatGPT, Cursor, and 23 others) can read and write to. It solves the problem of each AI having separate, siloed memory by giving them a single shared brain that the user owns. Users teach one AI something and all connected AIs know it, with answers citing the exact note used.
Callosium is a local folder of plain markdown files that multiple AI assistants (Claude, ChatGPT, Cursor, and 23 others) can read and write to. It solves the problem of each AI having separate, siloed memory by giving them a single shared brain that the user owns. Users teach one AI something and all connected AIs know it, with answers citing the exact note used.