LangChain4j

Health v2 · maintenance only

Adoption not part of the score

13266 stars · 2741 forks observed · 2026-10-20

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

LangChain4j is an open-source Java library for building LLM-powered applications on the JVM, providing a unified API over 20+ LLM providers and 40+ embedding/vector stores. It includes a toolbox spanning prompt templating, chat memory, tool calling (with MCP support), agents, and RAG pipelines, with integrations for Spring Boot, Quarkus, and Helidon.

Use cases

• build a chatbot in Java with OpenAI or Anthropic
• implement RAG over documents in a Spring Boot app
• call LLMs from Java with a unified API
• let LLMs call Java methods via tool calling
• build AI agents on the JVM
• swap between vector stores like Pinecone and Milvus without rewriting code
• add MCP tool support to a Java application
• integrate embeddings and retrieval into Quarkus services

When to choose

• you are building LLM applications in Java, Kotlin, or another JVM language
• you want provider-agnostic APIs to switch between LLMs and vector stores
• you need agents, RAG, or tool calling with Spring Boot or Quarkus integration
• you want MCP support and enterprise Java framework compatibility

When to avoid

• your stack is Python, JavaScript, or another non-JVM language
• you need a ready-made end-user application rather than a library
• you want the original LangChain Python ecosystem and its tooling

Facets

library · maturity active

llm-inference rag agent-framework mcp prompt-engineering chatbot sdk nlp large-language-models chatbots developer-tools machine-learning jvm cross-platform langchain java llm-integrations vector-stores tool-calling spring-boot quarkus embeddings ai-agents retrieval-augmented-generation spring

Member repositories

For agents

markdown · JSON · MCP: product_card(name="langchain4j/langchain4j")