Message window persistence with a pluggable store

The most popular LLM library on the JVM. Ships a ChatMemory interface with MessageWindowChatMemory and TokenWindowChatMemory implementations. Persistence is bring-your-own via the ChatMemoryStore interface.

ChatMemory chatMemory = MessageWindowChatMemory.withMaxMessages(20);
chatMemory.add(UserMessage.from("I'm allergic to peanuts"));
chatMemory.add(AiMessage.from("Got it, I'll remember that."));
// Next session: chatMemory is empty unless ChatMemoryStore is implemented

Message history with the broadest backend coverage

Spring AI's ChatMemory shipped GA in 2025 with JDBC (Postgres, MySQL, Oracle, SQL Server), Cassandra, Mongo, Neo4j, and Cosmos DB backends. Three advisors plug it into ChatClient. The VectorStoreChatMemoryAdvisor indexes raw messages in a VectorStore but does not extract facts.

ChatMemory chatMemory = MessageWindowChatMemory.builder()
    .chatMemoryRepository(new JdbcChatMemoryRepository(jdbcTemplate))
    .maxMessages(20)
    .build();

ChatClient client = ChatClient.builder(chatModel)
    .defaultAdvisors(MessageChatMemoryAdvisor.builder(chatMemory).build())
    .build();

Vertex AI Memory Bank or keyword matching

Google's Agent Development Kit shipped 1.0.0 for Java in 2025 with a BaseMemoryService interface. Two implementations: InMemoryMemoryService (keyword matching only, for development) and VertexAiMemoryBankService (fully managed, Vertex AI only). Memory Bank does real fact extraction but is Google Cloud-locked.

JetBrains fact store, now with native Java API

JetBrains' JVM agent framework. As of Koog 0.7, memory splits into ChatMemory (conversation history), LongTermMemory (vector-based fact retrieval, organised by Concept/Subject/Scope), and Persistence (state checkpoints), with a separate History Compression feature for token budgeting. The nodeSaveToMemoryAutoDetectFacts node uses an LLM to extract facts automatically from chat history. Koog 0.7 also shipped a native Java API at JavaOne. Closest competitor to Mem0/Zep on the JVM.

agent {
    memory {
        scope = MemoryScope.User("user_123")
    }
    nodeSaveToMemoryAutoDetectFacts {
        subjects = listOf(Subject.User, Subject.Product)
    }
}

Blackboard pattern, long-term memory is a non-goal

Rod Johnson's (Spring creator) JVM agent framework. Uses Goal-Oriented Action Planning with a blackboard state container for domain objects passed between actions during an agent run. Per the maintainers, long-term conversational memory is explicitly out of scope.

Thin REST client, requires Python Mem0 server

The top Google result for "Mem0 Java SDK" is me.pgthinker:mem0-client-java, a community REST wrapper. It is not an SDK in the meaningful sense — it requires running a Python Mem0 server alongside your JVM application, with its own Qdrant and graph database dependencies.

Does not exist

Zep's official clients are Python, TypeScript, and Go. There is no Java SDK at any state of completeness. Java teams who want Zep's temporal knowledge graph must either consume Zep Cloud over HTTP or hand-roll a REST client.

The most common Java AI memory stack in production today

Most Java teams that need real memory assemble their own: PostgreSQL with pgvector (or Qdrant) for embeddings, JdbcChatMemoryRepository for raw messages, a custom advisor that calls an LLM to extract facts, and a cron job for decay. Roughly 1,500–3,000 lines of bespoke Java per team.

Fact extraction, hybrid retrieval, explicit supersession, benchmarked

A durable memory system for AI agents from the JamJet team. Ships LLM-driven fact extraction, hybrid retrieval (vector + FTS keyword + temporal scoring, with optional cross-encoder rerank), explicit fact supersession (supersede(old_id, new_id) with recall filtering by default), per-tenant scope isolation (user_id + org_id), and mode-aware reading (recall vs synthesis). Benchmarked at 71% overall on LongMemEval-S (500q, gpt-4o-mini judge) with per-category numbers and ablation history published. JVM access via engram-spring-boot-starter on Maven Central with Spring AI ChatMemoryRepository auto-configuration, or via MCP server (memory_record, memory_recall, memory_context) reachable from any HTTP client. Zero infrastructure by default (SQLite + hnswlib); PostgreSQL optional.

// JVM path: engram-spring-boot-starter wires Spring AI ChatMemoryRepository
@Autowired ChatMemoryRepository memory;

memory.saveAll("alice", List.of(
    new UserMessage("I'm allergic to peanuts."),
    new AssistantMessage("Got it.")
));

List<Message> recent = memory.findByConversationId("alice");

// Or run the MCP server and call it from any agent:
//   docker run -p 7080:7080 ghcr.io/jamjet-labs/engram-py-server:latest