Memmy Agent Review (2026): Pricing, Features & Honest Verdict

Memmy Agent is an MIT-licensed local memory hub that reads your conversation history across Cursor, Claude Code and Codex, turns it into structured memory, and hands the relevant pieces to whichever AI tool you switch to next. Real research lineage, genuinely local storage, and no published pricing at all. Best for: developers running three or more AI tools daily. Price: Free and open source, 2M token trial. Rating: 7/10.

What is Memmy Agent?

Memmy solves the tax you pay every time you change AI tools. You explain your stack to Cursor, then run out of credits and explain it again to Claude Code, then start a fresh session and explain it a third time. Memmy scans the local conversation history of the AI tools already on your machine, distils it into deduplicated structured memory covering preferences, technical decisions, project background and known pitfalls, and injects the matching pieces into whatever tool you are using now. It launched on Product Hunt on 30 July 2026 and took third place with 283 upvotes.

The team is MemTensor, a Shanghai company that is not new to this problem. They publish MemOS, a memory operating system for language models developed with Shanghai Jiao Tong University, Renmin University of China and the research institute of China Telecom, built around a memory abstraction called MemCube that handles parametric, activation and plaintext memory as one resource. Memmy is the consumer-facing product sitting on that research, and its retrieval is described as MemOS-powered hybrid search. That lineage is worth weighing: this category is full of weekend projects, and this is not one of them.

What Are Memmy Agent’s Key Features?

Scanning what you already said

Most memory tools start empty and ask you to build up context from today onward. Memmy starts by reading backwards. After you explicitly authorise it, it scans the conversation history of Cursor, Claude, Codex and other tools already installed, then deduplicates and categorises what it finds. So the memory is useful on day one rather than after three weeks of use. That authorisation is per source and can be revoked at any time, and nothing is read without it.

Relay between tools

The transfer case is the sharpest one in the pitch. You run out of Cursor credits mid-project and move to Claude. Memmy carries the project context, your stated preferences and your progress across, so the new tool starts where the old one stopped. It supports Cursor, Claude Code, Codex, OpenCode, OpenClaw and Hermes Agent. Critically, it injects precise on-demand matches rather than dumping the whole store into the context window, which is the difference between memory that helps and memory that eats your token budget.

You also control the grant per tool. Each agent can be given access to specific memories and a set amount of context rather than the whole store, any memory can be viewed or deleted on demand, and any source can be switched off. That granularity is the part most competitors skip, and it matters here more than usual, because the memory being moved around includes the technical decisions and personal preferences you would not paste into a public issue tracker.

It is an agent, not just a store

Memmy has its own runtime with model calls, tool registration and skill management, so you can hand it work directly. Merge the plans discussed across three agents into one document. Turn this week’s technical decisions into a doc. Remember that you do not want sugary marketing copy. That last one is the interesting case, because it is a memory write phrased as an instruction, and it means curating your own memory does not require a settings screen.

One memory, four front doors

The memory service runs locally on port 18960 and handles storage, search, retrieval and analytics. A desktop app, a CLI, a TUI and an OpenAI-compatible API all read and write that same store. The integration layer connects external tools over MCP and custom skills. The OpenAI-compatible endpoint is the quietly important one: anything that can point at a custom base URL can use your memory, including tools Memmy has never heard of.

Installing it takes one of three routes. The desktop app downloads from the site or GitHub releases. The CLI runs a single memmy onboard command after install. From source you clone the repository and run the dev script, which needs Node.js 22 or newer. The MemOS project underneath it claims 35.24 percent token savings from reusing memory rather than re-establishing context, which is the number to test against your own usage rather than take on faith, but it points at the real payoff: a memory layer earns its keep by shrinking prompts, not by adding a feature.

How Much Does Memmy Agent Cost?

Memmy is free. The code is MIT licensed on GitHub, you can self-host it, and the desktop app for macOS 12 or later and Windows 10 or later costs nothing. Registration includes a trial with 2 million tokens of model credit. Beyond that you bring your own key and pay your model provider directly, so your ongoing cost is whatever your own API usage costs and nothing goes to MemTensor.

That compares well on paper to a category that charges real money. Mem0 offers a free tier at 10,000 memories then runs Starter at $19 per month, Growth at $79 and Pro at $249, with graph memory locked behind the $249 plan. Supermemory includes $5 of monthly usage free and about $20 of usage on a $19 Pro plan. Greplica, launched the same week, is also free and open source but scoped to a single repository rather than to you as a person. The gap in Memmy’s story is that after those 2 million tokens run out, nothing on the site tells you what your monthly bill will look like, because it depends entirely on how much memory processing your usage triggers, and no worked example is published.

Who is Memmy Agent Best For?

Use Memmy if you use three or more AI tools every day and you have felt the cost of repeating yourself. It is also the right pick if you want a memory layer but will not put your working history on a vendor’s servers, since storage is local by default and the licence lets you audit exactly what it does. And use it if you switch tools for credit reasons, because the relay case is what it does best.

Skip Memmy if you use one AI tool exclusively, since that tool’s own memory feature will cover you with less setup. Skip it if you cannot grant a desktop application permission to read your AI conversation history, because that is the whole mechanism and there is no lighter mode. And skip it if you need a support commitment, because the contact address on the site is an outlook.com mailbox and the GitHub repository sits at 198 stars with 8 open issues, which is early even by 2026 standards.

Best Memmy Agent Alternatives

Mem0 is the commercial standard for agent memory, with a free tier covering 10,000 memories and paid plans at $19, $79 and $249 per month. It is a hosted API built for production applications rather than a desktop app for your own tools, and graph memory, its most compelling capability, sits on the $249 plan.

Supermemory prices on usage: $5 of it free each month, and roughly $20 included on the $19 Pro plan. It is closer to a developer service than a personal memory hub, so it fits if you are building memory into something you ship rather than trying to stop repeating yourself to Cursor.

Greplica is free, open source and fully local, and it launched days earlier. It builds a self-updating linked knowledge graph of a codebase from your coding sessions and stores it in a local database. It is repository memory rather than personal memory, so the two solve adjacent problems and would sit happily side by side.

Final Verdict: Is Memmy Agent Worth It?

Worth installing, with a clear head about what it is. The core idea is correct, the backwards scan makes it useful immediately instead of eventually, and the MIT licence plus local storage means the privacy claim is checkable rather than promised. Coming out of the MemOS research line gives it an architecture most competitors in this space do not have, and at zero dollars against $19 to $249 a month for Mem0, the trial costs you nothing but disk space.

The reservations are worth stating plainly. Local-first describes where your memory is stored, not where it is processed: the agent runtime calls a hosted model, so the content of your memory reaches a model provider when Memmy acts on it, and the free trial credits are served from an optional cloud endpoint on a memtensor.cn domain that some teams will need to clear with a data residency policy first. There is also no pricing at all, which sounds like good news and is actually an unknown, since your real bill is your own model spend and no one has published what typical usage costs. Grant it access to one project’s history, not all of them, watch your API spend for a week, and expand from there.