Research & Product Updates
Maximem's AI memory research and product updates: LongMemEval and LoCoMo benchmark results, architecture deep-dives on Maximem Synap, launches and release notes.
Maximem Synap is #1 on LongMemEval at 95.6%, and the posts here explain what that number measures, how we ran them and where they fall short. Benchmarks for memory are young, so we publish the configuration alongside the score.
Alongside the benchmarks sit retrieval experiments run on real corpora, deep-dives into how Synap stores and retrieves context across its vector, graph and file stores, and the product news that follows from that work: launches and release notes, plus guides to getting started with Maximem Synap and Maximem Vity.
Start here: Benchmark methodology · How Synap works · Measuring agent memory · Maximem Synap
Synap Is #1 on LongMemEval: 95.6% on All 500 Questions
Maximem Synap is #1 on LongMemEval: 95.6% on all 500 questions with Gemini 3.8 Flash, at about 7.4K tokens per question. Every answer and verdict is public.
How Do You Make AI Agent Memory GDPR Compliant? Erasure That Reaches Derived Facts, Embeddings and Backups
AI agent memory is personal data, so GDPR applies to every fact, summary and embedding it keeps. What compliance takes in practice: a lawful basis per memory purpose, minimisation on the write path, retention limits, erasure that reaches derived facts, embeddings and backups, access and portability exports, processor contracts with the memory vendor and the LLM provider, transfer safeguards, a DPIA where risk is high, and where the EU AI Act fits after the July 2026 changes. Read against the GDPR text and EDPB guidance on 28 October 2026.
Agent Memory vs RAG: When Do You Use Which?
Use RAG for shared knowledge that changes by new versions and agent memory for per-person facts that get corrected. Plain RAG over raw chat history matched or beat memory systems in six of seven benchmark settings in October 2026, and retrieving the raw history alongside memory cut the losses memory adds.
Maximem Synap Updates: Higher Scores, 17 Integrations, and a Live Playground
Synap updates: 95.6% LongMemEval, #1 on the benchmark (up from 92% and 90.2%), 93.2% LOCOMO, 17 framework integrations, a browser playground, public pricing, and a free accuracy eval on your own agent.
How Synap Works Under the Hood
We launched Maximem Synap today. Here's a peek into how it is built.
Synap's June 2026 Benchmark Results: 92% on LongMemEval, 93.2% on LoCoMo
Synap outperforms existing memory systems by redesigning context management on leading benchmarks; delivering higher accuracy, lower latency, and stable performance at scale through structured, domain-specific architectures.
Why We Built Synap
AI agents don’t fail from lack of memory, they fail because context doesn’t evolve. This article shows why current approaches break, introduces the Context Management Trilemma, and how Synap enables agents to learn, adapt, and stop forgetting over time.
File Search vs Vector Search for RAG: 50,000 Documents, 5,000 Queries, Full Results
We ran exact-match keyword search against vector search across five datasets, 50,000 documents and 5,000 queries, scored by exact document ID with no LLM judge. Vector search wins on average, and the entire margin comes from one dataset.
How to Get Started with Maximem
A step-by-step guide to setting up your AI memory system and maximizing productivity.
Introducing Maximem: AI Memory That Actually Works
Exploring the next generation of AI capabilities and how memory will shape the future of human-AI interaction.