By Every new Claude Code session starts cold. Cursor doesn’t know what Codex built yesterday. If you spend the first five minutes of every AI session re-typing your tech stack, your preferences, and your project’s context, Memmy Agent is the fix. The open-source personal memory hub from MemTensor hit 5th on Product Hunt last week — 206 upvotes, 555 comments — because developers are tired of being strangers to their own tools.
One Memory, Every Tool
Memmy is a local-first memory hub that sits between you and your AI coding tools. Install it once, and Claude Code, Codex, Cursor, OpenClaw, and Hermes Agent all pull from the same memory store. Your preferences, architecture decisions, and current project context — shared, persistent, and fully under your control.
The key design choice: Memmy exposes an OpenAI-compatible local API. Any tool that can call an OpenAI endpoint can talk to Memmy. No custom integration work required. Point your tools at http://localhost:8765 and you’re done. The core is open-source, and the free tier ships with 2 million tokens.
How It Gets Smarter Over Time
Under the hood, Memmy runs on MemOS, MemTensor’s memory operating system. It uses a four-layer architecture that does something most memory tools don’t: it gets smarter, not just bigger.
- L1 — Traces: Raw logs of every conversation, command, and decision
- L2 — Policies: Behavioral patterns extracted from many traces — “this developer prefers functional components” becomes a rule
- L3 — World Model: Compressed project state: your stack, constraints, and architecture
- L4 — Skills: Proven patterns crystallize into reusable, callable capabilities
The community’s most-cited feature: L2 policies becoming Skills. Your working patterns get encoded, not just recorded. That’s a meaningful difference from tools that are really just fancy clipboard managers.
Memory Beats Context Stuffing — By a Lot
If you’re still pasting your full project context into every session, the numbers are brutal. Mem0’s 2026 agent memory benchmarks show persistent memory achieving 91.6% accuracy with under 7,000 tokens. Full-context stuffing delivers 72.9% accuracy at 26,000+ tokens — worse performance at 4x the cost and 91% higher latency.
Memory isn’t just a convenience feature. It’s the cheaper, faster, and more accurate approach. MemOS itself reports 35.24% token savings via hybrid retrieval. You’re burning money on context you don’t need.
Getting Started
Setup is straightforward:
git clone https://github.com/MemTensor/memmy-agent
memmy serve --port 8765Set MEMMY_API_BASE=http://localhost:8765 in your environment and configure your tools to use the local endpoint. Memmy also ships as a Claude Code skill — add it to your skills directory for direct integration. The Product Hunt listing links to official binary releases if you prefer not to build from source.
What It Won’t Do (Yet)
Be honest about the limitations before you commit. Memmy is a solo developer tool — local-first means memory isn’t shared across teammates. No sync layer for teams exists yet. If you’re working in a compliance environment where local storage raises audit concerns, this isn’t your answer today.
The cross-agent memory experience is still maturing. The architecture is solid, but some edge cases in cross-tool context handoff are rougher than others. For team or production agent memory, Mem0, Zep, or Letta serve that use case better.
Worth Installing Today
If you regularly switch between two or more AI coding tools, Memmy solves a real daily annoyance. The local-first model keeps your data on your machine. The OpenAI-compatible API means zero-friction integration. The four-layer MemOS engine improves with use instead of just accumulating noise.
It’s a solo tool for now, and it’s still finding its edges. But the core problem — every AI session starting cold — is solved. That alone is worth an afternoon of setup.
