Launched this week
Memmy Agent
Let every AI remember the same you.
1K followers
Let every AI remember the same you.
1K followers
🍙 Memmy Agent is a personal memory hub and local AI agent for all AI Agent and tools like Claude Code, Codex, OpenClaw and Hermes. Gives every AI one shared, full-controlled memory — they all remember the same you. Memmy turns chats, decisions, prefs, progresses, and experiences into long-term memory, brings the right context into matching task, also can take on work directly. Local-first by default. Your memory stay under your control: manage them anytime. Free start with 2M ChatGPT tokens.
This is awesome. I think the whole community been waiting for something like this.
BTW does it work with any AI tool already??? Like Antigravity, Grok build, etc...
Memmy Agent
@tuliosousapro Yep — both Antigravity and Grok Build can use Memmy through the CLI. They can run local shell commands, so they’re able to search, read, and write the Memmy memory pool with memmy-memory.
They’re not native one-click integrations yet, though, so automatic recall and capture would need some extra Skill or Hook setup. The built-in integrations just have that wiring done already.
Which one are you using more?
@yunn_chen I am using more Antigravity right now.
Memmy Agent
@tuliosousapro Quick add: if a tool can run local commands, you can usually hook into Memmy via CLI first — native one-click support is what we’re expanding next ✨
Memmy Agent
@tuliosousapro Yep — CLI gets you in now; native one-click is what we’re growing. Excited for you to try it with whichever you already live in!
Could definitely see this being used for shopping or booking flights. I want my agent to just remember my preferences and what I like in terms of clothes and flight selections for example.
Memmy Agent
@arthurdls Exactly. The real win is not having to re-enter the same little preferences every time. Memmy can carry those across agents; the actual booking flow still depends on the tools you connect.
Curious which one you’d test first — flights or shopping?
Memmy Agent
@arthurdls +1 — love this use case. Things like preferred airlines, aisle vs window, budget range, clothing sizes/brands… once those stick in memory, every new agent starts much closer to “you.” Would be fun to see which preference set you teach it first ✈️🛍️
Memmy Agent
@arthurdls Love this use case — once preferences stick, shopping/flights get so much less annoying. Memory can follow you across agents; booking still depends on the tools you wire in 🙌
Local note apps preserve information, but they do not automatically surface the right decision to the agent working on today's task.
Memmy Agent
@nicole_h94 Yes!! You totally get it 🙌 Storage is easy — the real magic is surfacing the right decision to the agent working on today’s task. That’s exactly what Memmy is built for. Love this take 💚
Memmy Agent
@nicole_h94 Spot on — saving notes is easy; the hard part is surfacing the right one while the agent is actually doing today’s work ✨
Typeless
Letting external agents both read memory and write new experience back closes the loop beautifully. That is what real continuity needs.
Memmy Agent
@yuki1028 Exactly — read-only context is useful, but continuity comes from a controlled feedback loop. Agents need a way to contribute new experience, decisions, and corrections back to memory, while users retain clear visibility and control over what becomes durable knowledge.
Does Memmy retrieve only the most relevant memories for each task, or inject a larger context block? I’d be interested to see how you balance recall quality, latency, and token usage.
Memmy Agent
@carlvert Memmy doesn’t dump the whole memory store into the prompt.
It searches across vector, full-text, pattern, and structural signals in parallel, merges the candidates, then applies relevance thresholds, dedup, and MMR to avoid near-duplicates. A final LLM filter trims the set again — up to 8 memories by default.
Each injected snippet is capped too, and Skills go in as short summaries unless full mode is needed. So the idea is broad recall first, then keep the actual context block small. You can also inspect what was considered, kept, and injected in the retrieval logs.
@yunn_chen Thanks, Pema! this is exactly the level of detail I was curious about. Broad recall followed by aggressive filtering, dedup, and MMR sounds like a thoughtful way to keep the injected context focused. The retrieval logs are a great touch too. Do they also break down latency and token usage by retrieval stage? That would make tuning the quality–cost tradeoff especially useful.
Memmy Agent
@yunn_chen @carlvert Glad that helped — and thanks for the thoughtful questions! 🫶
@yunn_chen @zhiqi_xu Thanks for sharing the details. The retrieval transparency is a really thoughtful touch. Excited to follow Memmy’s progress! 🙌
Memmy Agent
@carlvert Quick add: it’s broad recall, then aggressive filtering — not dumping the whole memory pool into the prompt 👍
Re-explaining the same context to every new AI tool is quietly one of the most annoying parts of this whole space, so shared memory across Claude Code and Cursor sounds like a small dream. Open source makes it easier to trust with this kind of data too. If I open my memory up, what does it actually look like, plain files I can edit or something more structured?
Memmy Agent
@doganakbulut Love this question. Under the hood it’s more structured than a folder of plain notes: memory lives in a local SQLite DB on your machine (~/.memmy), organized into layers (memories, experiences, world model, skills). You can browse/delete from the Memory panel, inspect or search via CLI, and export the DB anytime. Config is plain YAML — but the memories themselves are structured records, not just freeform markdown. Open source + local-first is exactly so you can trust and inspect what’s stored.
Memmy Agent
@doganakbulut
Totally get that — it’s genuinely annoying. Switching tools and having to re-explain your whole context every time is such a waste.
Memmy’s basically for that: Claude Code, Cursor, and other agents share the same local memory. It’s open source and local-first, so the data stays on your machine by default.
What you open isn’t a pile of loose plain-text files. It’s a memory panel in the desktop app — you can browse, search, open details, and delete anything that looks wrong. Under the hood it’s a structured local memory store, not a black-box cloud. You can also export the local data if you want your own copy.
So more precisely: there’s a GUI to manage it — inspect, delete, keep it local. It’s not the “open a folder and edit a bunch of markdown files” shape.
Really interesting direction. How does Memmy handle outdated or conflicting memories—for example, when a project decision changes later? Does the newer context automatically override the old one, or can users review the conflict?
Memmy Agent
@luke_pioneero It doesn’t simply overwrite the old memory. When a decision changes, Memmy keeps the original context as an L1 trace and can create a decision-repair record that feeds into updated L2 guidance. So the history is preserved, while later retrieval can work from the revised decision instead of treating both versions as equally valid.
You can also inspect and delete memories yourself. What we don’t have yet is a polished side-by-side conflict review flow — that part is still being improved.
@yunn_chen Got it, that makes sense. Keeping the history without treating both versions as equally valid sounds like a solid approach. Thanks for clarifying!
Memmy Agent
@luke_pioneero pls be welcome to upvote and try~