Memmy Gives Every AI Coding Agent One Shared Memory: Here's Why That Matters

TL;DR

I've been switching between Claude Code, Cursor, and Codex daily for months. The single biggest friction point isn't model quality or speed: it's that none of these tools remember what I did in the others. Memmy, a new MIT-licensed local memory hub from MemTensor, tries to fix this by giving every AI coding agent one shared, persistent memory. It's free, runs locally on your machine, and already supports Claude Code, Cursor, Codex, OpenClaw, and Hermes Agent. The bigger story: cross-agent memory is turning into its own product category, and claude-mem has already racked up 84,000 GitHub stars by solving a similar problem from a different angle.

What Memmy Does

Memmy sits on your machine as a local memory service on port 18960. It reads conversation history from each AI tool you authorize, structures it into searchable memory, and serves relevant context to whichever tool you're using next.

The architecture has three pieces. A memory core handles session management, search, and writing. A local backend built on Fastify and SQLite manages accounts, integrations, and source scanning. Agent adapters pull history from each supported tool and install context injection hooks so the memory gets surfaced at the right time.

Installation is straightforward: clone the repo, copy the env file, run the dev-start script. It ships with a pre-filled cloud address and works out of the box.

The Context Problem Is Real

If you use more than one AI coding tool, you know the friction. You spend 30 minutes with Claude Code debugging a complex refactor, then switch to Cursor for quick edits. Cursor has no idea what you just figured out. You re-explain yourself. You paste context. You waste time.

This isn't a small annoyance. When you're deep in a problem, switching between tools without shared memory feels like changing keyboards mid-sentence. The tools are individually powerful but collectively amnesiac. Every switch is a reset.

Two Approaches, One Problem

Memmy takes a local-first, open-source path. Everything runs on your machine. No cloud dependency, no API keys, no data leaving your laptop. It's MIT-licensed, so you can fork it, modify it, or embed it in your own tools.

Claude-Mem (thedotmack/claude-mem, 84k stars) tackles the same problem differently. It's a plugin that captures tool usage during sessions, compresses observations into semantic summaries, and makes them available to future sessions. It supports even more agents: Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, and OpenCode. But the memory lives within each agent's session rather than in a centralized hub.

Neither approach is wrong, just different philosophies. Memmy builds a shared brain that all agents consult. Claude-mem gives each agent its own persistent notebook. The right choice depends on whether you want your agents to share context or carry their own.

What This Signals

Two independent projects attacking the same problem, one with 84,000 stars, tell you where AI coding workflows are heading. We're past the single-tool era. Developers assemble toolbelts now: Claude Code for architecture work, Cursor for rapid editing, Codex for terminal automation, OpenClaw for browser tasks.

Each tool excels at different things. The missing piece has always been a memory layer that spans them all. Memmy and claude-mem are early attempts to build that layer, and they're gaining traction because the need is undeniable.

The real question isn't whether cross-agent memory becomes standard. It's which architecture wins: centralized hub or per-agent persistence, open source or managed service, local-only or cloud-backed.

What to Do Now

Try one. If you use multiple AI coding tools daily, the friction you've learned to live with is fixable. Start with Memmy if you want a local-first hub you fully control. Go with claude-mem for something more battle-tested with a larger community.

Either way, don't wait for tool vendors to solve this. Claude Code, Cursor, and Codex have no incentive to share context with competitors. The memory layer will come from the community. It's already here.