Maximem Synap
Memory for AI agents. #1 on LongMemEval at 95.6%
258 followers
Memory for AI agents. #1 on LongMemEval at 95.6%
258 followers
Maximem Synap is memory and context infrastructure for AI agents, so every conversation does not start from zero. It is the fastest and most accurate memory system on public benchmarks, 95.6% on LongMemEval and 93.2% on Locomo, with sub-15ms P75 recall. It handles entity resolution, temporal reasoning, and multi-level scoping automatically, no vector database or ranker to tune.Native across 22 frameworks including LangChain, LangGraph, and the Claude Agent SDK. Free tier, no credit card required
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Launch Team / Built With
Render Sandboxes: a computer for every agentGive every agent its own machine. Starts in under a second
Promoted
Hello Product Hunt. I am Gaurav, founder of Maximem.
This is our third launch here, after Maximem Wrapped in December and Maximem Vity for OpenClaw in February. Today it is Synap: memory and context infrastructure for AI agents, built so a conversation does not start over every time a session ends.
Synap resolves entities across sessions, tracks what is current versus stale, scopes memory from one user up to an entire customer deployment, and forgets what stops being relevant instead of holding onto it forever. On public benchmarks it is the fastest and most accurate memory system available, 92% on LongMemEval and 93.2% on Locomo, and it plugs natively into LangChain, LangGraph, the Claude Agent SDK, and a dozen other agent frameworks.
Thank you to Flo Merian for hunting us today. There is a free tier with no credit card required, and I will be around all day for questions on the architecture, the benchmarks, or anything else you want to poke at.
@gaurav_ships Glad to see LangGraph support right out of the box, congrats for launch
@priya_kushwaha1 Of course! Not just LangGraph; but nearly every prominent agent framework out there is supported.
@gaurav_ships How does the system decide what information should be remembered or forgotten ?
@dipanshu_kushwaha5 Great question, Dipanshu!
We use a combination of techniques to decide what's worth storing. But the most important is that we create a custom context architecture for every agent (agentically). This ensures, the decision of what needs to be remembered is not merely guesswork, but intelligent understanding of your agents and customer needs.
Hello Product Hunt, I'm Anish, Founding Engineer at Maximem AI
If you’re building AI agents and have reached the point where managing context is becoming a problem of its own, you should definitely give Synap a try.
What looks simple at first gets surprisingly complicated once you have multiple sessions, users, entities, changing information and a lot of context to deal with.
That’s exactly the problem we built Synap to solve.
It’s live today with a free tier, so go give it a shot and let us know what you think
Hi AI Devlopers, if you're reading this and thinking whether famous agent frameworks like Langraph, Langchain, CrewAI, Google ADK and many are supported or not.
So YES, definitely we have a first-class support for all these frameworks. You name it, we support it.
And tbh, if you use these frameworks, the integration becomes less than writing 4-5 lines of code.
I came across Maximem when I read about the Agentic Context Management Paper you guys wrote.
Your technique of solving for memory is very thoughtful and mature to work in various production workloads.
Great on benchmarks, great in real life results. I am a happy customer of your product!
@samyakk Thanks Samyak for using our product and your constructive feedback that improves it.
Tried maximem while i was exploring factual memory for agent harness, so far havent found anything close to its quality and accuracy.
@shivama205 Thanks Shivam for using it and also advocating it!
@gaurav_ships evals are the most impressive part of this.
@kushal_l Also the hardest.
It can get really tempting to tune the product just to score high on evals (Evalmaxxing). But resisting that, creating a better product for developers while scoring higher on evals; that's what we work hard towards as a team.
Hello PH, Shreyansh this side, Founding Engineer in Maximem.
Trust me, if you're building any voice agent, and you've already spent some time building your own context/memory management stack, just check out Maximem Synap once.
We have especially kept voice ai companies in mind while building this product. There are no more retrieval calls in the critical path. This is what you want.
Go check it out.