Langchain4J
LangChain4j is an idiomatic, open-source Java library for building LLM-powered applications on the JVM. It offers a unified API over popular LLM providers and vector stores, and makes implementing tool calling (including MCP support), agents and RAG easy. It integrates seamlessly with enterprise Jav
Last verified:
What is Langchain4J?
LangChain4j is an open-source Java library that simplifies integrating large language models (LLMs) into Java applications through a unified API. It provides access to 20+ popular LLM providers (including OpenAI, Google Vertex AI, Anthropic) and 30+ embedding/vector stores (such as Pinecone, Milvus, Chroma), eliminating the need to learn proprietary APIs for each service. The library enables developers to build chatbots, assistants, agents, and RAG (Retrieval-Augmented Generation) applications with ease.
Key features include comprehensive tooling ranging from low-level prompt templating, chat memory management (message window and token window implementations), output parsing, and function calling to high-level patterns like Agents and RAG with complete pipelines from data ingestion to retrieval. It supports structured outputs guaranteeing type-safe JSON responses, response streaming, multimodal inputs (text and images), AI Services as a high-level declarative API, tool calling with MCP support, text classification, and document loading from various sources (PDFs, DOC, XLS, URLs, S3, Azure Blob Storage). LangChain4j offers seamless integration with enterprise Java frameworks including Quarkus, Spring Boot, Helidon, Micronaut, and Payara Micro, with two-way integration allowing LLMs to call Java code.
LangChain4j is designed for Java developers building LLM-powered applications who want native Java idioms like strong typing and annotation-driven development. It targets developers creating customer support chatbots, educational assistants, data extraction tools, content generators, summarization systems, and any application requiring LLM integration. The library is particularly suitable for enterprise environments requiring GDPR-compliant AI solutions, developers working with Quarkus or Spring Boot, and those needing reliable structured JSON output without broken responses.
Langchain4J pricing
Pricing model: Freemium
LangChain4j is an open-source library under Apache-2.0 license and is completely free to use. There are no paid plans or subscription tiers for the library itself. Users may incur costs associated with the LLM providers they choose to use (such as OpenAI API costs, Google Vertex AI costs, Anthropic costs, etc.), which are billed directly by those providers. A free demo key is provided for demonstration purposes with quota limitations and restricted to the gpt-4o-mini model only, requiring requests to go through LangChain4j's proxy server.
Langchain4J pros
- Open-source and completely free to use
- Unified API for 20+ LLM providers eliminates learning multiple proprietary APIs
- 30+ embedding/vector store integrations including Pinecone, Milvus, Chroma
- Seamless Quarkus integration with dedicated extension
- Spring Boot starters for easy integration
- Helidon and Micronaut integrations available
- High-level AI Services API hides complexity and boilerplate
- Structured outputs guarantee 100% valid JSON matching Java records
- Chat memory implementations: message window and token window, both in-memory and persistent
- Support for 15+ embedding models and 5 image generation models
- Two-way integration: Java calls LLMs and LLMs call Java code
- RAG pipeline with document loading, splitting, embedding, retrieval, and re-ranking
- Model Context Protocol (MCP) support for tool calling
- Response streaming support for low-level API
- Kotlin extensions with asynchronous non-blocking coroutines
- Multimodal support for text and image inputs
- Active development with 9.5k GitHub stars and 290 contributors
- Apache-2.0 license for commercial use
Langchain4J cons
- Minimum JDK version 17 required, not compatible with older Java versions
- Some modules still in beta (e.g., 1.10.0-beta18) with potential breaking changes
- Agents and Agentic AI module is experimental with API subject to change
- Guardrails feature is experimental with potentially changing API
- Documentation chatbot is marked as experimental
- Demo key has quota restrictions and limited to gpt-4o-mini model only
- Requires separate API keys for each LLM provider (costs not included)
- No built-in hosted platform - must self-host and manage infrastructure
- Learning curve for developers new to LLM concepts and patterns
- HTTP client customization currently only supported for OpenAI and Ollama modules
Frequently asked questions about Langchain4J
What is LangChain4j and what does it do?
LangChain4j is an open-source Java library that simplifies integrating LLMs into Java applications through a unified API. It provides access to 20+ popular LLM providers and 30+ embedding stores, eliminating the need to learn proprietary APIs for each. It makes implementing RAG, tool calling (including MCP support), and agents easy, with seamless integration into enterprise Java frameworks like Quarkus, Spring Boot, and Helidon.
What LLM providers does LangChain4j support?
LangChain4j currently supports 20+ popular LLM providers including OpenAI, Google Vertex AI, Anthropic, and many others. The unified API allows you to easily switch between different providers without rewriting your code, making it simple to experiment with different models.
How do I get started with LangChain4j?
The minimum supported JDK version is 17. For Maven, add the langchain4j-open-ai dependency (version 1.10.0) to your pom.xml. If using high-level AI Services API, also add the main langchain4j dependency. Import your OpenAI API key from environment variables, create an OpenAiChatModel instance using the builder pattern, and start chatting with model.chat().
What if I don't have an OpenAI API key?
You can temporarily use a demo key provided for free for demonstration purposes. The demo key has a quota, is restricted to the gpt-4o-mini model, and should only be used for demonstration purposes. When using the demo key, all requests go through LangChain4j's proxy which injects the real key before forwarding to OpenAI API.
What is the difference between low-level and high-level abstraction in LangChain4j?
Low-level abstraction gives you complete freedom with components like ChatModel, ChatMessage, AiMessage, EmbeddingStore, and Embedding - these are the primitives requiring more glue code but complete control. High-level abstraction uses AI Services which hides all complexity and boilerplate in a declarative manner while still allowing flexibility to adjust and fine-tune behavior.
How does structured output work in LangChain4j?
Structured Outputs force LLMs to output strict JSON that maps 1:1 to your Java records, guaranteeing type safety and 100% valid JSON without additional text or markdown wrappers. You can define structure programmatically in Java or inject raw JSON Schema strings. The output will be valid JSON matching your schema with correct field names and types.
What RAG capabilities does LangChain4j provide?
LangChain4j provides complete RAG pipeline including document ingestion (TXT, PDFs, DOC, PPT, XLS from file system, URL, GitHub, Azure Blob Storage, Amazon S3), document splitting with multiple algorithms, post-processing, embedding segments, storing in vector stores, retrieval (simple and advanced), query transformation (expansion, compression), routing, re-ranking, and Reciprocal Rank Fusion. Each step in the RAG flow is customizable.
What chat memory implementations does LangChain4j offer?
LangChain4j provides implementation of both persistent and in-memory chat memory algorithms including message window and token window approaches. This maintains chat history without manual management of ChatMessages, which would otherwise be cumbersome.
How does LangChain4j integrate with enterprise Java frameworks?
LangChain4j offers seamless integration with Quarkus (via quarkus-langchain4j extension), Spring Boot (with dedicated starters), Helidon (via io.helidon.integrations.langchain4j), Micronaut (via micronaut-langchain4j), and Payara Micro. The integration enables two-way communication: you can call LLMs from Java and allow LLMs to call your Java code in return.
What is the Model Context Protocol (MCP) support in LangChain4j?
LangChain4j supports the Model Context Protocol (MCP) to communicate with tools and enable tool calling functionality. This allows LLMs beyond generating text to also trigger actions, with support for both static Tools and Dynamic Tools (execution of dynamically generated LLM code).