New research from Maximem. Agentic Context Management: Agent Memory is an architecture problem. Read the paper →

Build AI that remembers, learns and gets better over time.

Maximem gives AI agents persistent, structured memory and active context management; so every interaction makes them more personal, accurate and useful, while reducing token costs, and so that what your agents know becomes something a competitor cannot trivially copy.

Try in Playground
ACCURACY OVER A LONG CONVERSATIONPRODUCTION ZONE · ≥ 90%DEMOPRODUCTIONas the conversation grows →context window only93.2%LoCoMo

Trusted by builders at

  • NVIDIA
  • IIT Delhi
  • Loopdesk
  • Razorpay
  • Aiden
  • Warrant
  • Omnidimension
  • TruCommerce
  • Praxel
  • UC Santa Cruz
  • IIT Roorkee
  • Uncypher
  • Agno
  • BITS Pilani
  • ByteDance
  • Mindloop
  • ClearVUE
  • HandaUncle
  • Supatest
  • Nexus Venture Partners
  • helllo
  • Hector AI
  • Unpaved

Supported by

  • NVIDIA Inception Program
  • Google for Startups
  • Neo4j
  • Cloudflare
  • Powered by AWS

AWS is a trademark of Amazon.com, Inc. or its affiliates.

What is Maximem?

Maximem builds memory infrastructure: Synap, so the agents you build can remember, and Vity, a personal memory that stays encrypted even from us, so the AI you use can too.

Maximem Synap

For organizations and development teams

The context management stack for the AI agents you build.

  • Persistent memory + anticipatory retrieval
  • Entity resolution + knowledge pipelines
  • 92% LongMemEval · 93.2% LoCoMo accuracy

Maximem Vity

For power users and AI enthusiasts

A secure personal context layer that follows you everywhere.

  • Private cloud vault, encrypted by default
  • Remembers preferences, work history & tasks
  • Carries context across every AI you use

01The problem

Why memory, and why the alternatives fall short.

Bigger context windows do not fix it. Vector search is not it. Files do not scale, and the platforms will not own it for you. We answer the nine loudest objections to agent memory, with proof.

Read the case for memory →

02How it works

How Synap actually works

A turn does not land in a database. It is ingested, its meaning is extracted into structure rather than raw text, and it is stored across a vector, graph, and file store, asynchronously, so the write call returns before any of that happens and never blocks your agent. Retrieval then nets across all three stores at once, and most reads never leave your process, because context is pre-fetched while the conversation is still going. The context management pipeline below is where the accuracy and latency numbers come from.

03Benchmarks

How Synap compares

The quick snapshot, measured on the same open-source LongMemEval harness.

 SynapMem0ZepSupermemory
LongMemEval92%57.5%63.8% (third-party reported)71.3%
Entity resolutionAutomatic, every tierPro tier onlyAutomaticFact extraction

Measured on Maximem's open eval harness, same hardware, same prompts, same conversations, same scoring. Vendor self-reported figures differ and are shown separately. Zep's figure is third-party reported and has not been run on our harness.

See the full comparison →

The difference between a demo and a product is memory.

Talk to the founder