memind

🍃 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.

🏆 SOTA with Lightweight Model

Achieved 86.88% overall on the LoCoMo benchmark using only gpt-4o-mini — a lightweight, cost-effective model. This proves that intelligent memory architecture matters more than brute-force model power.

☕ Java-Native — First SOTA Memory for the Java Ecosystem

The first Java-based AI memory system to achieve SOTA-level performance. Built with Spring Boot 4.0 and Spring AI 2.0, memind integrates naturally into Java/Kotlin enterprise stacks with a one-line Maven dependency.

Architecture

memind processes conversations through a multi-stage pipeline, from raw dialogue to structured knowledge:

Two-Scope Memory

memind maintains separate memory scopes for comprehensive agent cognition:

| Scope | Categories | Purpose | |-------|-----------|---------| | USER | Profile, Behavior, Event | User identity, preferences, relationships, experiences | | AGENT | Tool, Procedural | Tool usage patterns, reusable procedures, learned workflows |

Dual Retrieval Strategies

| Strategy | How it works | Best for | |----------|-------------|----------| | Simple | Vector search + BM25 keyword matching, merged via RRF (Reciprocal Rank Fusion), with adaptive truncation | Low-latency, cost-sensitive scenarios | | Deep | LLM-assisted query expansion, sufficiency checking, and reranking | Complex queries requiring reasoning |

Benchmark

LoCoMo

Evaluation on the LoCoMo benchmark using gpt-4o-mini:

| Method | Single Hop | Multi Hop | Temporal | Open Domain | Overall | |--------|-----------|-----------|----------|-------------|---------| | memind (gpt-4o-mini) | 91.56 | 83.33 | 82.24 | 71.88 | 86.88 |

memind achieves SOTA-level performance using only gpt-4o-mini — a lightweight, cost-effective model.

Quick Start

Installation

Build and install locally:

git clone https://github.com/openmemind-ai/memind.git
cd memind
mvn clean install

Then add the Spring Boot Starter to your project's pom.xml:

<dependency>
  <groupId>com.openmemind.ai</groupId>
  <artifactId>memind-spring-boot-starter</artifactId>
  <version>0.1.0-SNAPSHOT</version>
</dependency>

Configuration

Configure in application.yml:

spring:
  ai:
    openai:
      api-key: ${OPENAI_API_KEY}
      base-url: ${OPENAI_BASE_URL:https://api.openai.com}
      chat:
        options:
          model: gpt-4o-mini
      embedding:
        options:
          model: text-embedding-3-small

memind:
  store:
    type: sqlite
    sqlite:
      path: ./data/memind.db

Usage

// Create a memory identity (user + agent)
MemoryId memoryId = DefaultMemoryId.of("user-1", "my-agent");

// Extract knowledge from conversations
memory.addMessages(memoryId, messages).block();

// Retrieve relevant memories
var result = memory.retrieve(memoryId, "What does the user prefer?",
        RetrievalConfig.Strategy.SIMPLE).block();

Pure Java Bootstrap

Outside Spring Boot, assemble the runtime objects directly and pass them into Memory.builder():

OpenAiApi openAiApi = OpenAiApi.builder()
        .apiKey(System.getenv("OPENAI_API_KEY"))
        .baseUrl(System.getenv().getOrDefault("OPENAI_BASE_URL", "https://api.openai.com"))
        .build();

OpenAiChatModel chatModel = OpenAiChatModel.builder()
        .openAiApi(openAiApi)
        .defaultOptions(OpenAiChatOptions.builder().model("gpt-4o-mini").build())
        .observationRegistry(ObservationRegistry.NOOP)
        .build();

EmbeddingModel embeddingModel = new OpenAiEmbeddingModel(
        openAiApi,
        MetadataMode.NONE,
        OpenAiEmbeddingOptions.builder().model("text-embedding-3-small").build());

JdbcMemoryAccess jdbc = JdbcStore.sqlite("./data/memind.db");

Memory memory = Memory.builder()
        .chatClient(new SpringAiStructuredChatClient(ChatClient.builder(chatModel).build()))
        .store(jdbc.store())
        .textSearch(jdbc.textSearch())
        .vector(SpringAiFileVector.file("./data/vector-store.json", embeddingModel))
        .options(MemoryBuildOptions.builder()
                .insightBuild(new InsightBuildConfig(2, 2, 8, 2))
                .build())
        .build();

Examples

Clone and run examples to see memind in action:

git clone https://github.com/openmemind-ai/memind.git
cd memind

Examples now live under memind-examples/ and share one data directory at memind-examples/data.

Configure OPENAI_API_KEY, then run one of the Spring Boot examples:

# Basic extract + retrieve
mvn -pl memind-examples/memind-example-spring-boot -am spring-boot:run \
  -Dspring-boot.run.mainClass=com.openmemind.ai.memory.example.springboot.quickstart.QuickStartExample

Pure Java examples are available in memind-examples/memind-example-java. Run the same scenario mains from your IDE, or invoke them with Maven Exec Plugin using the fully qualified class names below.

They now use the same object-first builder approach shown above.

| Runtime | Example | Main Class | Description | |---------|---------|------------|-------------| | Spring Boot | QuickStart | com.openmemind.ai.memory.example.springboot.quickstart.QuickStartExample | Basic extract + retrieve flow | | Spring Boot | Agent Scope | com.openmemind.ai.memory.example.springboot.agent.AgentScopeMemoryExample | Agent-scope extraction, insight tree flush, and retrieval for directives, playbooks, and resolutions | | Spring Boot | Insight | com.openmemind.ai.memory.example.springboot.insight.InsightTreeExample | Insight Tree multi-tier generation (Leaf → Branch → Root) | | Spring Boot | Foresight | com.openmemind.ai.memory.example.springboot.foresight.ForesightExample | Predictive memory — anticipate user needs | | Spring Boot | Tool | `com.openmemind.ai.memory.example.springboot.tool.ToolMemoryEx