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, 92% 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.
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.
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.
super cool product, better accuracy than mem0, exactly what i was looking for
Been following what @gaurav_ships building for a while, and it's great to see Synap out in the open. Memory is the part of agents everyone hand-rolls and then regrets, so a managed layer with real benchmark numbers behind it makes a lot of sense.
Congrats on shipping! How are you thinking about self-hosting for teams with strict data rules?
Been seeing the journey of Maximem for a while now and it's no surprise that the product is stellar. Congratulations on the launch!
Congrats, Gaurav and team! Having worked with you on a few cases, I understand how much care goes into getting memory right. Rooting for you!
the "forgets what stops being relevant instead of holding onto it forever" line is the part that matters most to me, most memory-layer pitches only talk about retention and never about active forgetting, even though a memory that never decays just becomes a slower vector store with extra steps. how does Synap decide something has stopped being relevant, is that a recency/access-frequency heuristic or does it need an explicit signal from the app that a fact was superseded
@gaurav_ships evals are the most impressive part of this.
Tried maximem while i was exploring factual memory for agent harness, so far havent found anything close to its quality and accuracy.
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!
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
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.
About Maximem Synap on Product Hunt
“The fastest, most accurate memory layer for AI agents”
Maximem Synap launched on Product Hunt on September 24th, 2026 and earned 134 upvotes and 28 comments, placing #9 on the daily leaderboard. 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, 92% 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.
Maximem Synap was featured in Developer Tools (520k followers), Artificial Intelligence (479.4k followers), GitHub (41.4k followers) and SDK (851 followers) on Product Hunt. Together, these topics include over 238.7k products, making this a competitive space to launch in.
Who hunted Maximem Synap?
Maximem Synap was hunted by fmerian. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Maximem Synap stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
fmerian
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.