LangChain4j

Apr 2026

Trial

LangChain4j is the leading framework-agnostic Java library for LLM integration — supporting 20+ model providers, 30+ vector stores, RAG, tool calling (including MCP), and agents, without requiring Spring or any specific web framework.

Why It's in Trial

LangChain4j is the right choice when:

You're not on Spring Boot (Quarkus, Micronaut, plain Java, Jakarta EE)
You want more explicit control over how AI components are wired together
You're building agents with complex tool execution loops
You want to mix and match providers and stores without Spring's auto-configuration

It launched in early 2023 and is now at version 1.14.1 (May 11, 2026) with a stable API. The Fall 2025 Java AI comparison describes it as a "two-horse race" between Spring AI and LangChain4j — both are production-ready, with LangChain4j winning on flexibility and agent maturity.

Key Capabilities

AI Services — declarative interface pattern:

interface CustomerSupport {
    @SystemMessage("You are a helpful customer support agent for ACME Corp.")
    String chat(String userMessage);
}

CustomerSupport support = AiServices.builder(CustomerSupport.class)
    .chatLanguageModel(openAiChatModel)
    .chatMemory(MessageWindowChatMemory.withMaxMessages(10))
    .tools(new OrderLookupTool())
    .build();

The AiServices pattern is LangChain4j's standout feature — define an interface, get an LLM-backed implementation. This is cleaner than Spring AI's ChatClient fluent API for service-oriented code.

Tool calling: Define @Tool-annotated methods on any POJO. LangChain4j handles the function-call loop automatically — the model can call your tools, receive results, and continue reasoning.

class CalendarTool {
    @Tool("Get the user's upcoming calendar events")
    List<Event> getCalendarEvents(String userId, int daysAhead) {
        return calendarService.getEvents(userId, daysAhead);
    }
}

MCP support: LangChain4j 1.x supports MCP tool calling — any MCP server can be used as a tool source, giving access to the full ecosystem of Stripe, Figma, Vercel, Postgres MCP servers without custom integration.

Broad provider support: 20+ LLM providers, 30+ embedding/vector stores. Switching providers means swapping one ChatLanguageModel implementation — all higher-level code stays the same.

RAG: Full ingestion pipeline (document loading, splitting, embedding) and retrieval with metadata filtering, re-ranking, and query expansion.

Streaming agents (1.11.0): The TokenStream interface enables streaming agent responses — tokens arrive as they're generated while tool calls execute mid-stream. This closes a gap that previously required workarounds for real-time chat UIs backed by tool-calling agents.

Agent Observability: AgentListener and AgentMonitor (added in 1.10.0) provide observability hooks for monitoring agent execution in production.

Agentic state persistence (1.13.0): Execution state of agentic systems can now be persisted and recovered — agents can checkpoint mid-run and resume without repeating expensive LLM calls. This closes a gap that previously required custom state management or was only available in Koog.

Skill-scoped tools (1.13.0): The new ClassPathSkillLoader groups tools into skills that can be selectively loaded per agent, reducing token overhead from irrelevant tool descriptions.

Spring Boot 4 support (1.13.0): First-class compatibility with the upcoming Spring Boot 4 release, alongside continued Spring Boot 3.x support.

Agentic A2A modules (v1.3.0): langchain4j-agentic and langchain4j-agentic-a2a are now first-class modules — not experimental. The agentic module provides workflow patterns (sequential, loop, parallel, conditional) and a supervisor pattern for dynamic agent routing via AgenticScope. The A2A module adds @A2AClientAgent for calling remote A2A servers. A companion langchain4j-agentic-mcp module wraps MCP tools as non-AI agents. Both Red Hat and Microsoft back the project, with Microsoft reporting hundreds of customers in production. 1.13.1 (April 23, 2026) patches a bug where typed A2A agents received incorrect argument types.

Polymorphic return types and ReturnBehavior.IMMEDIATE_IF_LAST (1.14.0): AI Services can now return polymorphic types from tool parameters, and the new ReturnBehavior.IMMEDIATE_IF_LAST flag lets agents return early when the last tool call is ready — removing the extra inference round-trip for simple pipelines.

OpenAI Responses API expansion (1.14.0): Non-streaming ChatModel support, reasoning summaries, encrypted reasoning, and server-side tool execution are now available through the Responses API integration. PDF file input and logprobs are also supported.

Anthropic thinking visibility (1.14.0): The new thinkingDisplay option controls whether the extended thinking token stream is exposed to callers — useful for debugging agents that use Claude's thinking mode without surfacing raw token streams in production logs.

MCP: multiple listeners and session IDs (1.14.0): MCP tool call sessions now expose a stable sessionId and support multiple concurrent listeners, making it easier to correlate tool calls with agent traces in observability pipelines. (1.14.0 changelog)

Security dependency updates (1.14.1, May 11, 2026): Patch release updating opennlp-tools to 2.5.9 and PostgreSQL driver to v42.7.11, which includes security fixes. No API changes. (1.14.1 changelog)

Spring AI vs LangChain4j: When to Use Which

Scenario	Use
Existing Spring Boot app	Spring AI
Quarkus or Micronaut app	LangChain4j (via native extensions)
Plain Java or Jakarta EE	LangChain4j
Need complex multi-tool agents	LangChain4j
Prefer annotation-driven DI style	Spring AI
Prefer explicit, testable interfaces	LangChain4j

Getting Started

<dependency>
  <groupId>dev.langchain4j</groupId>
  <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
  <version>1.0.0</version>
</dependency>

Key Characteristics

Property	Value
Status	1.14.1 (May 11, 2026)
License	Apache 2.0
Requires	Java 11+
Provider	Community (Red Hat + Microsoft backing)
GitHub	langchain4j/langchain4j
Website	docs.langchain4j.dev

Jan 2026