Memory that thinks. Context that evolves.
Highlights ·
Overview ·
Quick Start ·
Examples ·
Benchmark
🏆 Highlights
Memind achieves state-of-the-art results across all three benchmarks: LoCoMo, LongMemEval, and PersonaMem.
- ☕ The first Java-native SOTA memory and context engine for AI agents: built natively in Java, memind brings state-of-the-art long-memory performance into the Java ecosystem.
- 🚀 Highest reported results across all three benchmarks: achieved 86.88% on LoCoMo, 84.20% on LongMemEval, and 67.91% on PersonaMem under aligned MemOS / EverMemOS-style evaluation.
- 📈 Stronger than the strongest published baselines: surpassed EverMemOS on LoCoMo and LongMemEval, and exceeded MemOS on PersonaMem.
- 🌳 Insight Tree turns memory into structured understanding: instead of flat fact storage, memind organizes memory into hierarchical knowledge that evolves over time. See Insight Tree.
- 🔬 Full benchmark details: see the Benchmark section for complete tables, category-level comparisons, context tokens, and evaluation protocol.
Overview
What is Memind?
Memind is a hierarchical cognitive memory and context engine for AI agents, built natively in Java.
Instead of treating memory as a flat collection of isolated facts, Memind continuously extracts, organizes, and evolves knowledge from conversations into a structured Insight Tree.
It tackles two core problems of agent memory: flat, unstructured storage (memories remain disconnected facts with no higher-level organization) and no knowledge evolution (memories accumulate, but never consolidate into deeper understanding).
The result is a long-term memory and context layer that helps agents retain context, build structured understanding over time, and recall knowledge at multiple levels of abstraction.
Core Design
Insight Tree
The Insight Tree is memind's core innovation. Unlike traditional memory systems that store isolated facts, memind progressively distills knowledge through three tiers — each tier sees patterns the previous one cannot:
| Tier | Input | What it produces |
|---|
| 🍃 Leaf | Grouped memory items | Insights within a single semantic group |
| 🌿 Branch | Multiple leaves | Cross-group patterns within one dimension |
| 🌳 Root | Multiple branches | Cross-dimensional insights invisible at lower levels |
Example — understanding a user named Li Wei through conversations:
🍃 Leaf (from career_background group):
"Li Wei has 8 years of backend experience — 3 years at Alibaba, then led an 8-person team at a fintech company, designing a core trading system with Java 17 + Spring Cloud + Kafka."
🌿 Branch (integrating career + education + certifications):
"Li Wei is a senior backend architect with deep distributed systems expertise, combining Zhejiang University CS training, large-scale Alibaba experience, and hands-on fintech system design — a well-rounded technical profile with both depth and breadth."
🌳 Root (cross-dimensional — identity × preferences × behavior):
"Li Wei's preference for functional programming and high code quality (80% test coverage), combined with conservative tech adoption (requires 2+ years production validation), reveals a personality oriented toward long-term code maintainability over rapid innovation — suggesting recommendations should emphasize stability and proven patterns over cutting-edge tools."
Each tier reveals something the previous one couldn't see. Leaves know facts. Branches see patterns. Roots understand the person.
Two-Scope Memory