Launching today
Memmy Agent
Let every AI remember the same you.
411 followers
Let every AI remember the same you.
411 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.
Shared memory across agents sounds powerful, especially with per-tool control. Can I create separate memory spaces for work, personal projects, and clients—and decide exactly which agent can access each one?
Memmy Agent
@blink_66 Not yet. Right now, all connected agents share one memory pool.
Separate memory spaces and per-agent access control are on our roadmap though — especially for people juggling work, personal, etc.
For now, separate Memmy instances are the cleanest workaround.
For me, the interesting challenge isn’t whether an AI can remember more, but whether it can remember the right things. How does Memmy distinguish a durable preference from a one-time instruction?
Memmy Agent
@orman_canida That’s the hard part. Memmy doesn’t treat every instruction as a permanent preference.
A one-off “use X for this task” stays with that task. Clear signals like “I prefer X,” “next time do Y,” or “use X instead of Y” can become preference memory, and repeated patterns across different tasks add stronger evidence over time.
So it’s a mix of what you explicitly say and what keeps showing up—not just remembering every command forever.
the write-back is the interesting half. dedup handles the same fact twice, but the thing that actually bites is a memory that still sounds confidently true and just is not true anymore, and nothing in the chat history tells you which one that is
Memmy Agent
@alex_watson2110 yeah, stale-but-confident memory is the much harder problem.
When Memmy catches an explicit correction, it can create a decision-repair record and update the guidance built from that memory, rather than just storing two conflicting versions side by side. We still keep the original trace for provenance.
The part we’re improving now is making those conflicts easier to spot and review, instead of resolving everything silently in the background.
What makes someone use Memmy instead of just relying on Claude or ChatGPT's built-in memory?
Memmy Agent
@reda_roqai_chaoui Fair question. If you mostly use one assistant, its built-in memory is probably enough.
The problem starts when your work is split across Claude Code, Cursor, Codex, and a few other tools. They all know different pieces of the story. Memmy gives them one shared memory, stored locally, so you’re not rebuilding context every time you switch.
That’s really the main difference: built-in memory stays inside one product, while Memmy follows you across agents.
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.
KnowU
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.
SoundGate Guitar
I constantly bounce between different AI tools and having to re-explain project context every single time is a massive pain. The fact that Memmy turns that messy chat history into structured memory is exactly what my workflow needs.
Memmy Agent
@aren_barseghyan Thanks so much! That’s exactly the pain Memmy is built for — so you don’t have to re-explain your project every time you switch tools. Glad it fits your workflow. Thanks for the support!
Memmy Agent
@aren_barseghyan Same here. that constant context reset was driving us nuts toooooo... What tools are you bouncing between most these days? Curious where the handoff gets the messiest.