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tessl/maven-dev-langchain4j--langchain4j

Build LLM-powered applications in Java with support for chatbots, agents, RAG, tools, and much more

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memory.mddocs/

Chat Memory

Chat memory implementations for maintaining conversation context. Supports message window and token window strategies with optional persistence through ChatMemoryStore.

Capabilities

ChatMemory Interface

Core interface for managing conversation history. All chat memory implementations extend this interface.

package dev.langchain4j.memory;

import dev.langchain4j.data.message.ChatMessage;
import java.util.List;

/**
 * Interface for managing conversation history
 */
public interface ChatMemory {
    /**
     * Get memory ID
     * @return Memory identifier
     */
    Object id();

    /**
     * Add message to memory
     * @param message Chat message to add
     */
    void add(ChatMessage message);

    /**
     * Add multiple messages to memory
     * @param messages Messages to add
     */
    void add(Iterable<ChatMessage> messages);

    /**
     * Get all messages in memory
     * @return List of chat messages
     */
    List<ChatMessage> messages();

    /**
     * Clear all messages
     */
    void clear();

    /**
     * Replace all messages (added in 1.11.0)
     * @param messages New messages
     */
    void set(List<ChatMessage> messages);
}

Thread Safety

NOT thread-safe. Individual ChatMemory instances do not provide internal synchronization. Concurrent calls to add(), messages(), clear(), or set() from multiple threads can result in:

  • Race conditions where messages are lost or duplicated
  • Inconsistent message ordering
  • ConcurrentModificationException if iterating messages while modifying

Solution: Use ChatMemoryProvider to ensure each thread/user gets isolated memory instances, or implement external synchronization if sharing a single instance across threads.

Common Pitfalls

  1. Shared Memory Across Users: Using a single ChatMemory instance for multiple users causes conversation leakage. Always use ChatMemoryProvider with @MemoryId for multi-user scenarios.

  2. Missing Memory ID: When using ChatMemoryProvider, forgetting to annotate the user/session parameter with @MemoryId will cause all users to share the same default memory instance.

  3. Memory Leaks with Provider: ChatMemoryProvider lambda implementations that create new instances without bounds can cause memory leaks. Implement proper cleanup or use weak references for inactive conversations.

  4. Assuming Persistence: By default, memory is in-memory only. Without a ChatMemoryStore, all conversation history is lost on application restart.

Edge Cases

  • Empty Memory: Calling messages() on newly created memory returns an empty list (not null). Safe to iterate without null checks.
  • Null Messages: Passing null to add(ChatMessage) or add(Iterable) behavior is implementation-dependent. Some implementations may throw NullPointerException.
  • Set with Null/Empty: Calling set(null) typically throws NullPointerException. Calling set(Collections.emptyList()) clears all messages.
  • ID Equality: The memoryId passed to ChatMemoryProvider.get() relies on proper equals() and hashCode() implementations. Using mutable objects or objects without proper equality can cause memory isolation failures.

Performance Notes

  • In-Memory Storage: Default implementations store messages in ArrayList. Access is O(1) for indexed operations, but eviction in windowed memories requires shifting elements.
  • Messages() Call: Returns a defensive copy of the internal list in most implementations. Frequent calls can cause allocation overhead.
  • Clear vs Set: clear() followed by add() may be less efficient than set() for bulk replacements, depending on implementation.

Cost Considerations

  • Context Window: All messages in memory are sent with each LLM request. Larger memory windows directly increase:
    • Token usage (input tokens charged by provider)
    • Latency (more tokens to process)
    • Cost (especially with pay-per-token models)
  • Optimization: Use token-based windows or smaller message counts to control costs while maintaining sufficient context.

Exception Handling

  • NullPointerException: Thrown when passing null messages or when memory ID is null (depending on implementation).
  • IllegalArgumentException: May be thrown by builder methods when invalid parameters are provided (e.g., negative maxMessages).
  • UnsupportedOperationException: Returned by set() in implementations prior to version 1.11.0.
  • Storage Exceptions: If using ChatMemoryStore, persistence operations can throw database-specific exceptions (e.g., SQLException, connection timeouts). These are typically unchecked and propagated to the caller.

Related APIs

  • ChatMemoryStore: For persistence implementations
  • ChatMemoryProvider: For multi-user/session memory management
  • MessageWindowChatMemory: Message count-based windowing
  • TokenWindowChatMemory: Token count-based windowing
  • @MemoryId: Annotation for marking memory ID parameters in AI Services

ChatMemoryStore Interface

Interface for persisting chat memory. Implementations can store to databases, files, etc.

package dev.langchain4j.store.memory.chat;

import dev.langchain4j.data.message.ChatMessage;
import java.util.List;

/**
 * Interface for persisting chat memory
 */
public interface ChatMemoryStore {
    /**
     * Get messages for given memory ID
     * @param memoryId Memory identifier
     * @return List of messages for this ID
     */
    List<ChatMessage> getMessages(Object memoryId);

    /**
     * Update messages for given memory ID
     * @param memoryId Memory identifier
     * @param messages New message list
     */
    void updateMessages(Object memoryId, List<ChatMessage> messages);

    /**
     * Delete messages for given memory ID
     * @param memoryId Memory identifier
     */
    void deleteMessages(Object memoryId);
}

Thread Safety

Implementation-dependent. The interface does not mandate thread safety. Most database-backed implementations are thread-safe due to underlying database connection pooling and transaction handling. However:

  • In-memory implementations (e.g., custom HashMap-based stores) are typically NOT thread-safe
  • File-based implementations require proper file locking
  • Always check the specific implementation's documentation

Best Practice: Assume stores are NOT thread-safe unless explicitly documented. Use connection pooling or external synchronization for concurrent access.

Common Pitfalls

  1. Blocking Operations: Store operations (especially getMessages and updateMessages) can block on I/O. Calling these on the main thread in UI applications causes freezing.

  2. No Transactions: The interface doesn't define transactional semantics. Multiple concurrent updateMessages() calls can result in lost updates if the implementation doesn't handle concurrency properly.

  3. Unbounded Storage: Implementations that don't implement cleanup logic accumulate messages indefinitely, causing database bloat and query performance degradation.

  4. Serialization Issues: Custom message types or content fields may not serialize correctly. Always test with your specific message types.

Edge Cases

  • Missing Memory ID: getMessages() with non-existent memoryId should return empty list (not null), but behavior varies by implementation.
  • Null Memory ID: Most implementations throw NullPointerException when memoryId is null, but this is not guaranteed by the interface.
  • Empty Message List: Calling updateMessages(id, emptyList) may either clear messages or be treated as a no-op, depending on implementation.
  • Delete on Non-existent ID: deleteMessages() on non-existent memoryId is typically a no-op but may throw exceptions in some implementations.

Performance Notes

  • Latency: Database-backed stores add latency to each conversation turn (typically 10-100ms for remote databases).
  • Batching: The interface doesn't support batch operations. Loading messages for multiple users requires N separate queries.
  • Caching: Consider implementing a caching layer (e.g., Redis) in front of persistent stores for frequently accessed conversations.
  • Index Strategy: Ensure memoryId columns are indexed in database implementations for fast lookups.

Cost Considerations

  • Database Costs: Persistent stores incur database storage and operation costs. Message-heavy applications can accumulate significant storage over time.
  • Network Costs: Remote database stores add network latency and potential bandwidth costs for large message histories.
  • Scaling: Database-backed stores may require connection pooling, read replicas, or sharding for high-throughput applications.

Exception Handling

  • Connection Failures: Database stores can throw connection-related exceptions (e.g., timeouts, network errors). Implement retry logic for transient failures.
  • Serialization Errors: Implementations may throw serialization exceptions when storing/retrieving complex message types.
  • Storage Limit Exceeded: Some stores (e.g., cloud services with quotas) may throw exceptions when storage limits are reached.
  • Concurrent Modification: Some implementations may throw optimistic locking exceptions when multiple threads update the same memoryId simultaneously.

Related APIs

  • ChatMemory: The interface that uses this store
  • MessageWindowChatMemory.Builder.chatMemoryStore(): Configuration method
  • TokenWindowChatMemory.Builder.chatMemoryStore(): Configuration method

ChatMemoryProvider

Provider interface for obtaining ChatMemory instances for different users or conversations.

package dev.langchain4j.memory.chat;

/**
 * Provider interface for obtaining ChatMemory instances
 */
public interface ChatMemoryProvider {
    /**
     * Get ChatMemory for given memory ID (user/conversation)
     * @param memoryId Identifier for the memory (can be any type with proper equals/hashCode)
     * @return ChatMemory instance for the given ID
     */
    ChatMemory get(Object memoryId);
}

Thread Safety

Thread-safe by design. The provider pattern enables thread safety by ensuring each memoryId maps to an isolated ChatMemory instance. However:

  • The provider implementation itself must handle concurrent get() calls safely
  • Lambda-based providers using ConcurrentHashMap.computeIfAbsent() are thread-safe
  • Simple lambda providers that create new instances on every call are thread-safe but inefficient

Recommended Pattern:

Map<Object, ChatMemory> memoryMap = new ConcurrentHashMap<>();
ChatMemoryProvider provider = memoryId ->
    memoryMap.computeIfAbsent(memoryId, id ->
        MessageWindowChatMemory.builder()
            .id(id)
            .maxMessages(10)
            .build()
    );

Common Pitfalls

  1. Memory Leaks: Provider implementations that cache memory instances indefinitely cause memory leaks for inactive users. Implement eviction policies (LRU, time-based) or use weak references.

  2. Creating New Instance Every Time: Lambda that returns new MessageWindowChatMemory(...) on every get() call creates a new memory for each message, losing conversation context.

  3. Shared State in Lambda: Capturing mutable state in the provider lambda can cause thread-safety issues:

    // WRONG: captures mutable map without synchronization
Map<Object, ChatMemory> map = new HashMap<>();
provider = id -> map.computeIfAbsent(id, ...);

  4. Missing @MemoryId Annotation: Forgetting @MemoryId on the AI Service method parameter causes all users to receive the same default memoryId (typically "default" or null).

Edge Cases

  • Null Memory ID: Behavior when memoryId is null depends on implementation. Some use "default" as fallback, others throw NullPointerException.
  • Memory ID Equality: Objects used as memoryId must have correct equals() and hashCode(). Using mutable objects or objects without proper equality breaks memory isolation.
  • Concurrent First Access: When multiple threads call get() with the same new memoryId simultaneously, ensure only one memory instance is created (use computeIfAbsent).
  • Provider Returning Null: If get() returns null, the AI service will typically throw NullPointerException when trying to use the memory.

Performance Notes

  • Caching: Provider implementations should cache memory instances. Creating new instances on every get() call is extremely inefficient.
  • Eviction Overhead: LRU caches or weak reference-based providers add computational overhead for eviction tracking.
  • Persistent Store Latency: If the provider creates memories with ChatMemoryStore backends, first access for each memoryId incurs database lookup latency.
  • Memory Overhead: Each cached memory instance holds message lists in heap. Applications with millions of users need eviction strategies.

Cost Considerations

  • Token Costs: Provider doesn't directly affect costs, but the memory window sizes configured in the provider do. Larger windows = more tokens per request.
  • Memory Overhead: In-memory provider caches consume RAM. For cloud deployments, this affects instance sizing and costs.
  • Database Costs: If memories use persistent stores, each unique memoryId creates database entries, affecting storage costs.

Exception Handling

  • NullPointerException: Thrown when memoryId is null and provider doesn't handle it gracefully.
  • Storage Exceptions: If the provider creates memories with persistent stores, get() may throw database exceptions during memory initialization.
  • OutOfMemoryError: Provider implementations without eviction can cause OOM when too many memory instances are cached.

Related APIs

  • @MemoryId: Annotation for marking memory ID parameters in AI Services
  • MessageWindowChatMemory: Common memory implementation used with providers
  • TokenWindowChatMemory: Alternative memory implementation
  • ChatMemoryStore: For persistent memory backing

MessageWindowChatMemory

ChatMemory implementation that retains a fixed number of most recent messages.

package dev.langchain4j.memory.chat;

/**
 * ChatMemory implementation that retains a fixed number of most recent messages
 * When the limit is reached, oldest messages are evicted
 */
public class MessageWindowChatMemory implements ChatMemory {
    /**
     * Create with max message limit
     * Uses default in-memory storage
     * @param maxMessages Maximum number of messages to retain
     * @return MessageWindowChatMemory instance
     */
    public static MessageWindowChatMemory withMaxMessages(int maxMessages);

    /**
     * Create builder for full configuration
     * @return Builder instance
     */
    public static Builder builder();
}

Thread Safety

NOT thread-safe. Concurrent access to the same instance can cause:

  • Lost messages due to race conditions in eviction logic
  • Inconsistent message counts
  • ConcurrentModificationException when iterating during modification

Solution: Use one instance per thread/user via ChatMemoryProvider, or wrap with external synchronization (e.g., Collections.synchronizedList() for internal storage).

Common Pitfalls

  1. Window Size Too Small: Setting maxMessages=1 or 2 loses context rapidly. Most conversations need at least 10-20 messages for coherent context.

  2. Window Size Too Large: Very large windows (e.g., 1000+ messages) cause:

    • High token costs (all messages sent with each request)
    • Increased latency
    • Potential model context limit exceeded errors

  3. System Messages Counted: System messages, user messages, and assistant messages ALL count toward the limit. A 10-message window might only hold 5 actual conversation turns.

  4. Forgetting Persistence: Without chatMemoryStore(), messages are lost on restart. Don't assume memory persists unless explicitly configured.

Edge Cases

  • maxMessages=0: Creates memory that retains zero messages. Every message is immediately evicted. Conversation has no context.
  • maxMessages=1: Only the most recent message is retained. The model sees only one message at a time (no context from previous turns).
  • Negative maxMessages: Throws IllegalArgumentException in most implementations.
  • Eviction Boundary: When adding the (maxMessages+1)th message, the oldest message is removed before adding the new one.
  • Batch Add Beyond Limit: Calling add(List) with more messages than maxMessages retains only the most recent maxMessages from the batch.

Performance Notes

  • Eviction Cost: O(1) for tracking, but removing from head of ArrayList requires O(n) element shifting. For high-frequency updates, consider LinkedList-based implementations.
  • Memory Overhead: Each message stores full content. A window of 100 messages with 1KB each = ~100KB per user. With 10K concurrent users = ~1GB RAM.
  • Persistence Latency: With ChatMemoryStore, each add() triggers a store update. Consider batching or async writes for high-throughput scenarios.

Cost Considerations

  • Token Usage: A 20-message window with average 50 tokens/message = 1000 tokens of context per request. At $0.01/1K tokens = $0.01 per request.
  • Right-Sizing: Balance context quality vs cost:
    • Customer support: 15-25 messages
    • Casual chat: 10-15 messages
    • Single-turn Q&A: 2-5 messages
  • Dynamic Sizing: Consider adjusting window size based on conversation type or user tier.

Exception Handling

  • IllegalArgumentException: Thrown by withMaxMessages() if maxMessages < 0 (zero may be allowed but creates useless memory).
  • NullPointerException: Thrown when adding null messages (behavior not standardized).
  • Storage Exceptions: If using persistent store, database errors propagate from add(), clear(), and set() operations.

Related APIs

  • TokenWindowChatMemory: Alternative using token count instead of message count
  • ChatMemoryProvider: For per-user memory instances
  • ChatMemoryStore: For persistence
  • MessageWindowChatMemory.Builder: For advanced configuration

MessageWindowChatMemory Builder

/**
 * Builder for MessageWindowChatMemory
 */
public class Builder {
    /**
     * Set memory ID
     * @param id Memory identifier
     * @return Builder instance
     */
    public Builder id(Object id);

    /**
     * Set max messages to retain
     * @param maxMessages Maximum number of messages
     * @return Builder instance
     */
    public Builder maxMessages(int maxMessages);

    /**
     * Set chat memory store for persistence
     * @param store Chat memory store implementation
     * @return Builder instance
     */
    public Builder chatMemoryStore(ChatMemoryStore store);

    /**
     * Build the MessageWindowChatMemory
     * @return MessageWindowChatMemory instance
     */
    public MessageWindowChatMemory build();
}

Thread Safety

NOT thread-safe. Builder instances should not be shared across threads. Each thread should create its own builder or use external synchronization.

Common Pitfalls

  1. Reusing Builder: Calling build() multiple times on the same builder may return separate instances sharing the same underlying store reference (implementation-dependent).

  2. Forgetting maxMessages: If maxMessages() is not called, behavior is implementation-dependent (may default to Integer.MAX_VALUE or throw exception).

  3. Store Without ID: Setting a store without an ID may cause the memory to use a default/null ID, leading to all instances sharing the same storage.

Edge Cases

  • Building Without maxMessages: Some implementations may throw IllegalStateException, others may use a default value (e.g., 10).
  • Null Store: Passing null to chatMemoryStore() is typically equivalent to not calling it (uses in-memory storage).
  • Null ID: Some implementations allow null ID (uses default), others throw NullPointerException.

Exception Handling

  • IllegalStateException: May be thrown by build() if required parameters are missing.
  • IllegalArgumentException: Thrown if maxMessages is negative or invalid parameters are provided.

Related APIs

  • MessageWindowChatMemory.withMaxMessages(): Shortcut factory method
  • ChatMemoryStore: For persistence configuration
  • ChatMemoryProvider: For managing built instances

TokenWindowChatMemory

ChatMemory implementation that retains messages within a token limit.

package dev.langchain4j.memory.chat;

/**
 * ChatMemory implementation that retains messages within a token limit
 * When adding a message would exceed the limit, oldest messages are evicted
 * until the new message fits
 */
public class TokenWindowChatMemory implements ChatMemory {
    /**
     * Create with max token limit
     * Uses default in-memory storage
     * @param maxTokens Maximum number of tokens to retain
     * @param tokenizer Token count estimator for counting tokens
     * @return TokenWindowChatMemory instance
     */
    public static TokenWindowChatMemory withMaxTokens(
        int maxTokens,
        TokenCountEstimator tokenizer
    );

    /**
     * Create builder for full configuration
     * @return Builder instance
     */
    public static Builder builder();
}

Thread Safety

NOT thread-safe. Same concurrency issues as MessageWindowChatMemory. Use ChatMemoryProvider for isolated instances per thread/user.

Common Pitfalls

  1. Tokenizer Mismatch: Using a different tokenizer than the actual LLM model leads to incorrect token counts:

    • Overestimation: Wastes context window
    • Underestimation: May exceed model's actual token limit, causing API errors

  2. Tokenization Overhead: Token counting is CPU-intensive. Called on every add() operation. Can become a bottleneck in high-throughput scenarios.

  3. Token Estimation Errors: Most tokenizers are estimates. Actual token count by the model may differ by 1-5%, potentially causing context limit exceeded errors.

  4. System Message Overhead: System prompts consume tokens but are often forgotten in capacity planning. A 500-token system prompt reduces effective context window.

  5. Window Size vs Model Limit: Setting maxTokens equal to model limit (e.g., 4096 for GPT-3.5) leaves no room for completion. Reserve 20-40% for response tokens.

Edge Cases

  • maxTokens=0: Creates memory that retains zero tokens. Every message is immediately evicted.
  • Single Message Exceeds Limit: If a message is larger than maxTokens, it is still added (memory temporarily exceeds limit), then older messages are evicted. Memory may contain just this one oversized message.
  • Negative maxTokens: Throws IllegalArgumentException.
  • Null Tokenizer: Throws NullPointerException when attempting to add messages.
  • Tokenizer Returns Negative: Undefined behavior. Most implementations don't validate tokenizer output.

Performance Notes

  • Tokenization Overhead: Tokenizers typically take 0.1-5ms per message depending on length and tokenizer implementation (regex-based vs native).
  • Eviction Complexity: O(n) where n = number of messages. Must sum tokens from newest to oldest to determine eviction point.
  • Memory Overhead: Stores both messages and their token counts. Slightly higher memory usage than MessageWindowChatMemory.
  • Persistent Store Impact: Token counts must be recalculated on load from persistent store (not typically stored), adding startup latency.

Cost Considerations

  • Precise Control: Token windows provide more precise cost control than message windows since cost is directly proportional to tokens.
  • Model Pricing: Different models have different token costs:
    • GPT-3.5-turbo: ~$0.002/1K tokens
    • GPT-4: ~$0.03/1K tokens
    • Claude: ~$0.008/1K tokens
  • Right-Sizing: Match maxTokens to conversation type:
    • Q&A: 500-1000 tokens
    • Conversations: 2000-4000 tokens
    • Document analysis: 6000-8000 tokens (if model supports)

Exception Handling

  • IllegalArgumentException: Thrown if maxTokens < 0 or other invalid parameters.
  • NullPointerException: Thrown if tokenizer is null or when adding null messages.
  • Storage Exceptions: Database errors propagate from persistence operations.
  • Tokenizer Exceptions: If tokenizer throws during token counting, exception propagates and message add fails.

Related APIs

  • MessageWindowChatMemory: Alternative using message count
  • TokenCountEstimator: Interface for token counting
  • OpenAiTokenizer: Common tokenizer implementation
  • ChatMemoryProvider: For per-user memory instances

TokenWindowChatMemory Builder

/**
 * Builder for TokenWindowChatMemory
 */
public class Builder {
    /**
     * Set memory ID
     * @param id Memory identifier
     * @return Builder instance
     */
    public Builder id(Object id);

    /**
     * Set max tokens to retain
     * @param maxTokens Maximum number of tokens
     * @return Builder instance
     */
    public Builder maxTokens(int maxTokens);

    /**
     * Set token count estimator
     * @param tokenCountEstimator Token estimator for counting tokens
     * @return Builder instance
     */
    public Builder tokenCountEstimator(TokenCountEstimator tokenCountEstimator);

    /**
     * Set chat memory store for persistence
     * @param store Chat memory store implementation
     * @return Builder instance
     */
    public Builder chatMemoryStore(ChatMemoryStore store);

    /**
     * Build the TokenWindowChatMemory
     * @return TokenWindowChatMemory instance
     */
    public TokenWindowChatMemory build();
}

Thread Safety

NOT thread-safe. Don't share builder instances across threads.

Common Pitfalls

  1. Missing Tokenizer: Forgetting tokenCountEstimator() typically throws IllegalStateException or NullPointerException on build.

  2. Wrong Tokenizer: Using OpenAiTokenizer with Claude or other models causes inaccurate token counts.

  3. Reusing Builder: May create instances sharing the same tokenizer reference (usually safe) but separate memory state.

Edge Cases

  • Building Without maxTokens or Tokenizer: Most implementations throw IllegalStateException from build().
  • Null Store or ID: Same behavior as MessageWindowChatMemory.Builder.

Exception Handling

  • IllegalStateException: Thrown by build() if maxTokens or tokenCountEstimator not set.
  • IllegalArgumentException: Thrown if maxTokens is negative.
  • NullPointerException: May be thrown if tokenCountEstimator is explicitly set to null.

Related APIs

  • TokenWindowChatMemory.withMaxTokens(): Shortcut factory method
  • TokenCountEstimator: Required for token counting
  • ChatMemoryStore: For persistence

Usage Examples

Simple Message Window Memory

import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.service.AiServices;

interface Assistant {
    String chat(String message);
}

// Create AI service with shared memory
Assistant assistant = AiServices.builder(Assistant.class)
    .chatModel(chatModel)
    .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
    .build();

// Conversation maintains context
String response1 = assistant.chat("My name is Alice");
String response2 = assistant.chat("What is my name?"); // Will respond "Alice"

Per-User Memory with ChatMemoryProvider

import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.service.AiServices;
import dev.langchain4j.service.MemoryId;

interface Assistant {
    String chat(@MemoryId String userId, String message);
}

// Create AI service with per-user memory
Assistant assistant = AiServices.builder(Assistant.class)
    .chatModel(chatModel)
    .chatMemoryProvider(memoryId -> MessageWindowChatMemory.withMaxMessages(10))
    .build();

// Each user has separate conversation context
String response1 = assistant.chat("user1", "My name is Alice");
String response2 = assistant.chat("user2", "My name is Bob");
String response3 = assistant.chat("user1", "What is my name?"); // Will respond "Alice"
String response4 = assistant.chat("user2", "What is my name?"); // Will respond "Bob"

Token Window Memory

import dev.langchain4j.memory.chat.TokenWindowChatMemory;
import dev.langchain4j.model.openai.OpenAiTokenizer;
import dev.langchain4j.service.AiServices;

interface Assistant {
    String chat(String message);
}

// Create AI service with token-based memory
Assistant assistant = AiServices.builder(Assistant.class)
    .chatModel(chatModel)
    .chatMemory(TokenWindowChatMemory.withMaxTokens(
        1000,
        new OpenAiTokenizer()
    ))
    .build();

// Memory automatically manages messages within token limit
String response = assistant.chat("Tell me about Java");

Memory with Persistence

import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.store.memory.chat.ChatMemoryStore;

interface Assistant {
    String chat(@MemoryId String userId, String message);
}

// Implement or use a ChatMemoryStore for persistence
ChatMemoryStore store = // ... your persistence implementation

Assistant assistant = AiServices.builder(Assistant.class)
    .chatModel(chatModel)
    .chatMemoryProvider(memoryId ->
        MessageWindowChatMemory.builder()
            .id(memoryId)
            .maxMessages(20)
            .chatMemoryStore(store)
            .build()
    )
    .build();

// Messages are persisted across application restarts
String response = assistant.chat("user1", "Hello");

Memory with Custom ID Types

import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.service.AiServices;
import dev.langchain4j.service.MemoryId;

// Custom memory ID type
record ConversationId(String userId, String sessionId) {
    // Proper equals/hashCode automatically generated
}

interface Assistant {
    String chat(@MemoryId ConversationId conversationId, String message);
}

Assistant assistant = AiServices.builder(Assistant.class)
    .chatModel(chatModel)
    .chatMemoryProvider(memoryId -> MessageWindowChatMemory.withMaxMessages(10))
    .build();

// Use custom ID for fine-grained memory separation
ConversationId id1 = new ConversationId("user1", "session1");
ConversationId id2 = new ConversationId("user1", "session2");

String response1 = assistant.chat(id1, "My favorite color is blue");
String response2 = assistant.chat(id2, "My favorite color is red");
String response3 = assistant.chat(id1, "What is my favorite color?"); // Will respond "blue"

Testing Patterns

Unit Testing Chat Memory

import org.junit.jupiter.api.Test;
import static org.assertj.core.api.Assertions.assertThat;

class MessageWindowChatMemoryTest {
    @Test
    void shouldRetainMaxMessages() {
        ChatMemory memory = MessageWindowChatMemory.withMaxMessages(3);

        memory.add(new UserMessage("Message 1"));
        memory.add(new UserMessage("Message 2"));
        memory.add(new UserMessage("Message 3"));
        memory.add(new UserMessage("Message 4"));

        assertThat(memory.messages()).hasSize(3);
        assertThat(memory.messages().get(0).text()).isEqualTo("Message 2");
        assertThat(memory.messages().get(2).text()).isEqualTo("Message 4");
    }

    @Test
    void shouldClearAllMessages() {
        ChatMemory memory = MessageWindowChatMemory.withMaxMessages(10);
        memory.add(new UserMessage("Test"));

        memory.clear();

        assertThat(memory.messages()).isEmpty();
    }
}

Testing Memory Provider Isolation

@Test
void shouldIsolateMemoryByUserId() {
    Map<Object, ChatMemory> memoryMap = new ConcurrentHashMap<>();
    ChatMemoryProvider provider = memoryId ->
        memoryMap.computeIfAbsent(memoryId,
            id -> MessageWindowChatMemory.withMaxMessages(10)
        );

    ChatMemory memory1 = provider.get("user1");
    ChatMemory memory2 = provider.get("user2");

    memory1.add(new UserMessage("User 1 message"));
    memory2.add(new UserMessage("User 2 message"));

    assertThat(memory1.messages()).hasSize(1);
    assertThat(memory2.messages()).hasSize(1);
    assertThat(provider.get("user1")).isSameAs(memory1); // Same instance
}

Testing Token Window Memory

@Test
void shouldEvictByTokenCount() {
    TokenCountEstimator tokenizer = text -> text.split("\\s+").length; // Simple word count
    ChatMemory memory = TokenWindowChatMemory.withMaxTokens(10, tokenizer);

    memory.add(new UserMessage("one two three")); // 3 tokens
    memory.add(new UserMessage("four five six seven")); // 4 tokens
    memory.add(new UserMessage("eight nine ten eleven twelve")); // 5 tokens

    // Total would be 12, exceeds 10, so oldest message evicted
    assertThat(memory.messages()).hasSize(2);
}

Integration Testing with Persistent Store

@Test
void shouldPersistAndRestore() {
    ChatMemoryStore store = new InMemoryChatMemoryStore(); // Or real DB store

    // Create memory, add messages, let it persist
    ChatMemory memory1 = MessageWindowChatMemory.builder()
        .id("user1")
        .maxMessages(10)
        .chatMemoryStore(store)
        .build();

    memory1.add(new UserMessage("First message"));
    memory1.add(new UserMessage("Second message"));

    // Create new memory instance with same ID
    ChatMemory memory2 = MessageWindowChatMemory.builder()
        .id("user1")
        .maxMessages(10)
        .chatMemoryStore(store)
        .build();

    // Should restore from store
    assertThat(memory2.messages()).hasSize(2);
    assertThat(memory2.messages().get(0).text()).isEqualTo("First message");
}

Testing Memory Leak Prevention

@Test
void shouldEvictInactiveUsers() {
    // Use WeakHashMap or LRU cache for provider
    Map<Object, ChatMemory> memoryMap = Collections.synchronizedMap(
        new WeakHashMap<>()
    );

    ChatMemoryProvider provider = memoryId ->
        memoryMap.computeIfAbsent(memoryId,
            id -> MessageWindowChatMemory.withMaxMessages(10)
        );

    // Create memory for user
    ChatMemory memory = provider.get("user1");
    memory.add(new UserMessage("Test"));

    // Remove strong reference
    memory = null;

    // Force GC
    System.gc();
    Thread.sleep(100);

    // Map should eventually be empty (weak references collected)
    // Note: This is non-deterministic, use for demonstration only
}

Mocking for Testing AI Services

@Test
void shouldUseMemoryInAiService() {
    ChatModel mockModel = mock(ChatModel.class);
    when(mockModel.generate(any())).thenReturn(new AiMessage("Mocked response"));

    Assistant assistant = AiServices.builder(Assistant.class)
        .chatModel(mockModel)
        .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
        .build();

    assistant.chat("First message");
    assistant.chat("Second message");

    // Verify model received both messages in context
    ArgumentCaptor<List<ChatMessage>> captor = ArgumentCaptor.forClass(List.class);
    verify(mockModel, times(2)).generate(captor.capture());

    List<ChatMessage> secondCall = captor.getAllValues().get(1);
    assertThat(secondCall).hasSize(3); // 2 user + 1 AI response
}

Error Recovery Patterns

Handling Storage Failures

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

class ResilientChatMemoryStore implements ChatMemoryStore {
    private static final Logger log = LoggerFactory.getLogger(ResilientChatMemoryStore.class);
    private final ChatMemoryStore delegate;
    private final ChatMemoryStore fallback; // E.g., in-memory store

    @Override
    public List<ChatMessage> getMessages(Object memoryId) {
        try {
            return delegate.getMessages(memoryId);
        } catch (Exception e) {
            log.error("Failed to get messages from primary store, using fallback", e);
            return fallback.getMessages(memoryId);
        }
    }

    @Override
    public void updateMessages(Object memoryId, List<ChatMessage> messages) {
        try {
            delegate.updateMessages(memoryId, messages);
            // Sync to fallback after successful primary write
            fallback.updateMessages(memoryId, messages);
        } catch (Exception e) {
            log.error("Failed to update messages in primary store", e);
            // Continue with in-memory fallback only
            fallback.updateMessages(memoryId, messages);
        }
    }

    @Override
    public void deleteMessages(Object memoryId) {
        try {
            delegate.deleteMessages(memoryId);
        } catch (Exception e) {
            log.error("Failed to delete messages from primary store", e);
        }
        fallback.deleteMessages(memoryId);
    }
}

Retry Logic for Transient Failures

class RetryingChatMemoryStore implements ChatMemoryStore {
    private final ChatMemoryStore delegate;
    private final int maxRetries = 3;
    private final long retryDelayMs = 100;

    @Override
    public List<ChatMessage> getMessages(Object memoryId) {
        return executeWithRetry(() -> delegate.getMessages(memoryId));
    }

    @Override
    public void updateMessages(Object memoryId, List<ChatMessage> messages) {
        executeWithRetry(() -> {
            delegate.updateMessages(memoryId, messages);
            return null;
        });
    }

    private <T> T executeWithRetry(Supplier<T> operation) {
        Exception lastException = null;
        for (int attempt = 1; attempt <= maxRetries; attempt++) {
            try {
                return operation.get();
            } catch (Exception e) {
                lastException = e;
                if (attempt < maxRetries) {
                    try {
                        Thread.sleep(retryDelayMs * attempt);
                    } catch (InterruptedException ie) {
                        Thread.currentThread().interrupt();
                        throw new RuntimeException("Interrupted during retry", ie);
                    }
                }
            }
        }
        throw new RuntimeException("Operation failed after " + maxRetries + " attempts", lastException);
    }
}

Graceful Degradation

class GracefullyDegradingAssistant {
    private final Assistant assistant;
    private final ChatMemory emergencyMemory = MessageWindowChatMemory.withMaxMessages(5);

    public String chat(String userId, String message) {
        try {
            return assistant.chat(userId, message);
        } catch (Exception e) {
            log.error("AI service failed, using degraded mode", e);

            // Use emergency memory to maintain minimal context
            emergencyMemory.add(new UserMessage(message));

            // Return fallback response
            return "I'm experiencing technical difficulties. Your message has been recorded.";
        }
    }
}

Circuit Breaker Pattern

class CircuitBreakerChatMemoryStore implements ChatMemoryStore {
    private final ChatMemoryStore delegate;
    private final AtomicInteger failureCount = new AtomicInteger(0);
    private final int failureThreshold = 5;
    private volatile boolean circuitOpen = false;
    private volatile long circuitOpenedAt = 0;
    private final long resetTimeoutMs = 60_000; // 1 minute

    @Override
    public List<ChatMessage> getMessages(Object memoryId) {
        if (isCircuitOpen()) {
            throw new RuntimeException("Circuit breaker open - store unavailable");
        }

        try {
            List<ChatMessage> result = delegate.getMessages(memoryId);
            onSuccess();
            return result;
        } catch (Exception e) {
            onFailure();
            throw e;
        }
    }

    private boolean isCircuitOpen() {
        if (!circuitOpen) return false;

        if (System.currentTimeMillis() - circuitOpenedAt > resetTimeoutMs) {
            circuitOpen = false;
            failureCount.set(0);
            return false;
        }
        return true;
    }

    private void onSuccess() {
        failureCount.set(0);
        circuitOpen = false;
    }

    private void onFailure() {
        if (failureCount.incrementAndGet() >= failureThreshold) {
            circuitOpen = true;
            circuitOpenedAt = System.currentTimeMillis();
        }
    }
}

Handling Token Estimation Failures

class SafeTokenWindowChatMemory implements ChatMemory {
    private final TokenWindowChatMemory delegate;
    private final int fallbackMaxMessages;

    @Override
    public void add(ChatMessage message) {
        try {
            delegate.add(message);
        } catch (Exception e) {
            log.error("Token estimation failed, using fallback message count", e);

            // Fall back to simple message count eviction
            List<ChatMessage> messages = delegate.messages();
            if (messages.size() >= fallbackMaxMessages) {
                messages.remove(0); // Evict oldest
            }
            // Re-add message (this may fail again, handle appropriately)
        }
    }
}

Recovery from Corrupted Memory State

class SelfHealingChatMemoryStore implements ChatMemoryStore {
    private final ChatMemoryStore delegate;

    @Override
    public List<ChatMessage> getMessages(Object memoryId) {
        try {
            List<ChatMessage> messages = delegate.getMessages(memoryId);

            // Validate messages
            if (messages == null) {
                log.warn("Null messages returned for {}, initializing empty", memoryId);
                return new ArrayList<>();
            }

            // Remove corrupted messages
            List<ChatMessage> valid = messages.stream()
                .filter(msg -> msg != null && msg.text() != null)
                .collect(Collectors.toList());

            if (valid.size() < messages.size()) {
                log.warn("Removed {} corrupted messages for {}",
                    messages.size() - valid.size(), memoryId);
                // Persist cleaned version
                delegate.updateMessages(memoryId, valid);
            }

            return valid;
        } catch (Exception e) {
            log.error("Failed to get messages, returning empty list", e);
            return new ArrayList<>();
        }
    }
}

Memory Cleanup Patterns for Long-Running Applications

Time-Based Eviction

class TimeBasedMemoryProvider implements ChatMemoryProvider {
    private final Map<Object, MemoryWithTimestamp> memoryMap = new ConcurrentHashMap<>();
    private final long maxIdleMs = 3600_000; // 1 hour
    private final ScheduledExecutorService cleaner = Executors.newSingleThreadScheduledExecutor();

    public TimeBasedMemoryProvider() {
        // Run cleanup every 10 minutes
        cleaner.scheduleAtFixedRate(this::cleanup, 10, 10, TimeUnit.MINUTES);
    }

    @Override
    public ChatMemory get(Object memoryId) {
        MemoryWithTimestamp entry = memoryMap.computeIfAbsent(memoryId, id ->
            new MemoryWithTimestamp(MessageWindowChatMemory.withMaxMessages(10))
        );
        entry.updateAccessTime();
        return entry.memory;
    }

    private void cleanup() {
        long now = System.currentTimeMillis();
        memoryMap.entrySet().removeIf(entry ->
            now - entry.getValue().lastAccessTime > maxIdleMs
        );
    }

    public void shutdown() {
        cleaner.shutdown();
    }

    private static class MemoryWithTimestamp {
        final ChatMemory memory;
        volatile long lastAccessTime;

        MemoryWithTimestamp(ChatMemory memory) {
            this.memory = memory;
            this.lastAccessTime = System.currentTimeMillis();
        }

        void updateAccessTime() {
            this.lastAccessTime = System.currentTimeMillis();
        }
    }
}

LRU Cache-Based Provider

import com.google.common.cache.Cache;
import com.google.common.cache.CacheBuilder;

class LruChatMemoryProvider implements ChatMemoryProvider {
    private final Cache<Object, ChatMemory> cache;

    public LruChatMemoryProvider(int maxSize) {
        this.cache = CacheBuilder.newBuilder()
            .maximumSize(maxSize)
            .expireAfterAccess(Duration.ofHours(1))
            .removalListener(notification -> {
                // Optional: persist memory before eviction
                ChatMemory memory = (ChatMemory) notification.getValue();
                if (memory != null) {
                    log.info("Evicting memory for {}", notification.getKey());
                }
            })
            .build();
    }

    @Override
    public ChatMemory get(Object memoryId) {
        try {
            return cache.get(memoryId, () ->
                MessageWindowChatMemory.withMaxMessages(10)
            );
        } catch (ExecutionException e) {
            throw new RuntimeException("Failed to create memory", e);
        }
    }

    public void invalidate(Object memoryId) {
        cache.invalidate(memoryId);
    }

    public void invalidateAll() {
        cache.invalidateAll();
    }
}

Weak Reference-Based Provider

class WeakReferenceChatMemoryProvider implements ChatMemoryProvider {
    private final Map<Object, WeakReference<ChatMemory>> memoryMap =
        Collections.synchronizedMap(new WeakHashMap<>());

    @Override
    public ChatMemory get(Object memoryId) {
        WeakReference<ChatMemory> ref = memoryMap.get(memoryId);
        ChatMemory memory = ref != null ? ref.get() : null;

        if (memory == null) {
            memory = MessageWindowChatMemory.withMaxMessages(10);
            memoryMap.put(memoryId, new WeakReference<>(memory));
        }

        return memory;
    }

    // Memory is automatically collected when no longer referenced
}

Proactive Memory Persistence and Cleanup

class PersistentMemoryManager {
    private final ChatMemoryStore store;
    private final Map<Object, ChatMemory> activeMemories = new ConcurrentHashMap<>();
    private final ScheduledExecutorService persistScheduler =
        Executors.newSingleThreadScheduledExecutor();

    public PersistentMemoryManager(ChatMemoryStore store) {
        this.store = store;

        // Persist all active memories every 5 minutes
        persistScheduler.scheduleAtFixedRate(
            this::persistAll, 5, 5, TimeUnit.MINUTES
        );
    }

    public ChatMemory getMemory(Object memoryId) {
        return activeMemories.computeIfAbsent(memoryId, id ->
            MessageWindowChatMemory.builder()
                .id(id)
                .maxMessages(20)
                .chatMemoryStore(store)
                .build()
        );
    }

    private void persistAll() {
        activeMemories.forEach((id, memory) -> {
            try {
                store.updateMessages(id, memory.messages());
            } catch (Exception e) {
                log.error("Failed to persist memory for {}", id, e);
            }
        });
    }

    public void closeMemory(Object memoryId) {
        ChatMemory memory = activeMemories.remove(memoryId);
        if (memory != null) {
            store.updateMessages(memoryId, memory.messages());
        }
    }

    public void shutdown() {
        persistAll();
        persistScheduler.shutdown();
    }
}

Monitoring Memory Usage

class MonitoredChatMemoryProvider implements ChatMemoryProvider {
    private final Map<Object, ChatMemory> memoryMap = new ConcurrentHashMap<>();
    private final AtomicInteger activeUsers = new AtomicInteger(0);
    private final AtomicLong totalMessages = new AtomicLong(0);

    @Override
    public ChatMemory get(Object memoryId) {
        return memoryMap.computeIfAbsent(memoryId, id -> {
            activeUsers.incrementAndGet();

            ChatMemory memory = MessageWindowChatMemory.withMaxMessages(10);

            // Wrap to track message additions
            return new ChatMemory() {
                @Override
                public void add(ChatMessage message) {
                    memory.add(message);
                    totalMessages.incrementAndGet();
                }

                // Delegate other methods...
                @Override
                public List<ChatMessage> messages() { return memory.messages(); }
                @Override
                public void clear() { memory.clear(); }
                @Override
                public Object id() { return memory.id(); }
                @Override
                public void set(List<ChatMessage> messages) { memory.set(messages); }
                @Override
                public void add(Iterable<ChatMessage> messages) {
                    memory.add(messages);
                    messages.forEach(m -> totalMessages.incrementAndGet());
                }
            };
        });
    }

    public int getActiveUserCount() {
        return activeUsers.get();
    }

    public long getTotalMessageCount() {
        return totalMessages.get();
    }

    public long getCurrentMemorySize() {
        return memoryMap.values().stream()
            .mapToInt(m -> m.messages().size())
            .sum();
    }
}

Distributed Memory Patterns

Redis-Based Distributed Memory Store

import redis.clients.jedis.Jedis;
import redis.clients.jedis.JedisPool;
import com.fasterxml.jackson.databind.ObjectMapper;

class RedisChatMemoryStore implements ChatMemoryStore {
    private final JedisPool jedisPool;
    private final ObjectMapper objectMapper = new ObjectMapper();
    private final String keyPrefix = "chat:memory:";

    public RedisChatMemoryStore(JedisPool jedisPool) {
        this.jedisPool = jedisPool;
    }

    @Override
    public List<ChatMessage> getMessages(Object memoryId) {
        try (Jedis jedis = jedisPool.getResource()) {
            String key = keyPrefix + memoryId;
            String json = jedis.get(key);

            if (json == null) {
                return new ArrayList<>();
            }

            return objectMapper.readValue(json,
                objectMapper.getTypeFactory().constructCollectionType(List.class, ChatMessage.class)
            );
        } catch (Exception e) {
            throw new RuntimeException("Failed to get messages from Redis", e);
        }
    }

    @Override
    public void updateMessages(Object memoryId, List<ChatMessage> messages) {
        try (Jedis jedis = jedisPool.getResource()) {
            String key = keyPrefix + memoryId;
            String json = objectMapper.writeValueAsString(messages);

            // Set with TTL to auto-cleanup inactive conversations
            jedis.setex(key, 86400, json); // 24 hour TTL
        } catch (Exception e) {
            throw new RuntimeException("Failed to update messages in Redis", e);
        }
    }

    @Override
    public void deleteMessages(Object memoryId) {
        try (Jedis jedis = jedisPool.getResource()) {
            String key = keyPrefix + memoryId;
            jedis.del(key);
        }
    }
}

Database-Based Distributed Store

import java.sql.*;

class DatabaseChatMemoryStore implements ChatMemoryStore {
    private final DataSource dataSource;
    private final ObjectMapper objectMapper = new ObjectMapper();

    @Override
    public List<ChatMessage> getMessages(Object memoryId) {
        String sql = "SELECT messages FROM chat_memory WHERE memory_id = ?";

        try (Connection conn = dataSource.getConnection();
             PreparedStatement stmt = conn.prepareStatement(sql)) {

            stmt.setString(1, memoryId.toString());
            ResultSet rs = stmt.executeQuery();

            if (rs.next()) {
                String json = rs.getString("messages");
                return objectMapper.readValue(json,
                    objectMapper.getTypeFactory().constructCollectionType(List.class, ChatMessage.class)
                );
            }

            return new ArrayList<>();
        } catch (Exception e) {
            throw new RuntimeException("Failed to get messages from database", e);
        }
    }

    @Override
    public void updateMessages(Object memoryId, List<ChatMessage> messages) {
        String sql = "INSERT INTO chat_memory (memory_id, messages, updated_at) " +
                    "VALUES (?, ?, NOW()) " +
                    "ON DUPLICATE KEY UPDATE messages = ?, updated_at = NOW()";

        try (Connection conn = dataSource.getConnection();
             PreparedStatement stmt = conn.prepareStatement(sql)) {

            String json = objectMapper.writeValueAsString(messages);
            stmt.setString(1, memoryId.toString());
            stmt.setString(2, json);
            stmt.setString(3, json);
            stmt.executeUpdate();
        } catch (Exception e) {
            throw new RuntimeException("Failed to update messages in database", e);
        }
    }

    @Override
    public void deleteMessages(Object memoryId) {
        String sql = "DELETE FROM chat_memory WHERE memory_id = ?";

        try (Connection conn = dataSource.getConnection();
             PreparedStatement stmt = conn.prepareStatement(sql)) {

            stmt.setString(1, memoryId.toString());
            stmt.executeUpdate();
        } catch (Exception e) {
            throw new RuntimeException("Failed to delete messages from database", e);
        }
    }
}

Hybrid Local + Distributed Cache

import com.github.benmanes.caffeine.cache.Cache;
import com.github.benmanes.caffeine.cache.Caffeine;

class HybridChatMemoryStore implements ChatMemoryStore {
    private final ChatMemoryStore distributedStore; // Redis, DB, etc.
    private final Cache<Object, List<ChatMessage>> localCache;

    public HybridChatMemoryStore(ChatMemoryStore distributedStore) {
        this.distributedStore = distributedStore;
        this.localCache = Caffeine.newBuilder()
            .maximumSize(1000)
            .expireAfterWrite(Duration.ofMinutes(5))
            .build();
    }

    @Override
    public List<ChatMessage> getMessages(Object memoryId) {
        // Try local cache first
        List<ChatMessage> cached = localCache.getIfPresent(memoryId);
        if (cached != null) {
            return new ArrayList<>(cached); // Return copy
        }

        // Fall back to distributed store
        List<ChatMessage> messages = distributedStore.getMessages(memoryId);
        localCache.put(memoryId, new ArrayList<>(messages));
        return messages;
    }

    @Override
    public void updateMessages(Object memoryId, List<ChatMessage> messages) {
        // Update both caches
        localCache.put(memoryId, new ArrayList<>(messages));
        distributedStore.updateMessages(memoryId, messages);
    }

    @Override
    public void deleteMessages(Object memoryId) {
        localCache.invalidate(memoryId);
        distributedStore.deleteMessages(memoryId);
    }
}

Distributed Memory with Event Broadcasting

import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.data.redis.listener.ChannelTopic;

class EventBroadcastingChatMemoryStore implements ChatMemoryStore {
    private final ChatMemoryStore delegate;
    private final RedisTemplate<String, MemoryUpdateEvent> redisTemplate;
    private final ChannelTopic topic = new ChannelTopic("memory:updates");
    private final Cache<Object, List<ChatMessage>> localCache;

    @Override
    public void updateMessages(Object memoryId, List<ChatMessage> messages) {
        // Update backing store
        delegate.updateMessages(memoryId, messages);

        // Update local cache
        localCache.put(memoryId, new ArrayList<>(messages));

        // Broadcast update to other instances
        redisTemplate.convertAndSend(topic.getTopic(),
            new MemoryUpdateEvent(memoryId, messages)
        );
    }

    // Subscribe to updates from other instances
    public void onMemoryUpdate(MemoryUpdateEvent event) {
        // Invalidate local cache when other instance updates
        localCache.invalidate(event.memoryId());
    }

    record MemoryUpdateEvent(Object memoryId, List<ChatMessage> messages) {}
}

Sharded Memory Store

class ShardedChatMemoryStore implements ChatMemoryStore {
    private final List<ChatMemoryStore> shards;

    public ShardedChatMemoryStore(List<ChatMemoryStore> shards) {
        this.shards = shards;
    }

    private ChatMemoryStore getShard(Object memoryId) {
        int shardIndex = Math.abs(memoryId.hashCode() % shards.size());
        return shards.get(shardIndex);
    }

    @Override
    public List<ChatMessage> getMessages(Object memoryId) {
        return getShard(memoryId).getMessages(memoryId);
    }

    @Override
    public void updateMessages(Object memoryId, List<ChatMessage> messages) {
        getShard(memoryId).updateMessages(memoryId, messages);
    }

    @Override
    public void deleteMessages(Object memoryId) {
        getShard(memoryId).deleteMessages(memoryId);
    }
}

Consistent Hashing for Distributed Memory

import com.google.common.hash.Hashing;

class ConsistentHashingMemoryStore implements ChatMemoryStore {
    private final TreeMap<Long, ChatMemoryStore> ring = new TreeMap<>();
    private final int virtualNodesPerStore = 150;

    public ConsistentHashingMemoryStore(List<ChatMemoryStore> stores) {
        for (int i = 0; i < stores.size(); i++) {
            ChatMemoryStore store = stores.get(i);
            for (int v = 0; v < virtualNodesPerStore; v++) {
                long hash = hash("store-" + i + "-vnode-" + v);
                ring.put(hash, store);
            }
        }
    }

    private ChatMemoryStore getStore(Object memoryId) {
        long hash = hash(memoryId.toString());
        Map.Entry<Long, ChatMemoryStore> entry = ring.ceilingEntry(hash);

        if (entry == null) {
            entry = ring.firstEntry();
        }

        return entry.getValue();
    }

    private long hash(String key) {
        return Hashing.murmur3_128().hashString(key, StandardCharsets.UTF_8).asLong();
    }

    @Override
    public List<ChatMessage> getMessages(Object memoryId) {
        return getStore(memoryId).getMessages(memoryId);
    }

    @Override
    public void updateMessages(Object memoryId, List<ChatMessage> messages) {
        getStore(memoryId).updateMessages(memoryId, messages);
    }

    @Override
    public void deleteMessages(Object memoryId) {
        getStore(memoryId).deleteMessages(memoryId);
    }
}

Cross-Region Replication

class ReplicatedChatMemoryStore implements ChatMemoryStore {
    private final ChatMemoryStore primaryStore;
    private final List<ChatMemoryStore> replicaStores;
    private final ExecutorService replicationExecutor = Executors.newFixedThreadPool(4);

    @Override
    public List<ChatMessage> getMessages(Object memoryId) {
        // Read from primary
        return primaryStore.getMessages(memoryId);
    }

    @Override
    public void updateMessages(Object memoryId, List<ChatMessage> messages) {
        // Write to primary synchronously
        primaryStore.updateMessages(memoryId, messages);

        // Replicate to secondaries asynchronously
        replicaStores.forEach(replica ->
            replicationExecutor.submit(() -> {
                try {
                    replica.updateMessages(memoryId, messages);
                } catch (Exception e) {
                    log.error("Failed to replicate to secondary store", e);
                }
            })
        );
    }

    @Override
    public void deleteMessages(Object memoryId) {
        primaryStore.deleteMessages(memoryId);

        replicaStores.forEach(replica ->
            replicationExecutor.submit(() -> {
                try {
                    replica.deleteMessages(memoryId);
                } catch (Exception e) {
                    log.error("Failed to delete from secondary store", e);
                }
            })
        );
    }

    public void shutdown() {
        replicationExecutor.shutdown();
    }
}

Install with Tessl CLI

npx tessl i tessl/maven-dev-langchain4j--langchain4j@1.11.0

docs

ai-services.md

chains.md

classification.md

data-types.md

document-processing.md

embedding-store.md

guardrails.md

index.md

memory.md

messages.md

models.md

output-parsing.md

prompts.md

rag.md

request-response.md

spi.md

tools.md

README.md

tile.json