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How is LangChain4j better than SpringAI? Understand one article

Popularity：346 ℃/2025-05-06 15:09:06

LangChain4j and Spring AI are the two most important frameworks for implementing large-scale application development in the Java ecosystem, but what is the difference between the two? Which framework should be used at the production level? This has made many people difficult, so this article will give you a brief discussion, hoping to give you a simple reference when selecting technology.

1. Function comparison

The functions of LangChain4j and Spring AI are relatively similar, and the two can even be used together, such as using Spring AI to implement the MCP server side, and then using LangChain4j to implement the MCP client to call Spring AI, and the two can be seamlessly connected. So what is the difference between the two?

Overall,More features provided by LangChain4j, for example, when implementing RAG function, LangChain4j provides three modes:

  1. Simple mode
  2. Native mode
  3. Advanced Mode

Provided when implementing the latter two modes:

  1. Text loader.
  2. Document parser can realize automatic parsing of various text formats, such as automatic parsing of PDF, DOC, TXT, MD, HTML and other formats.
  3. Text Converter
  4. Text splitter

The responsibilities of each detail and module are clearly defined, soIt is more recommended to use LangChain4j when implementing complex functions and production-level services.。

2. Cost of use and learning

The cost of using and learning in LangChain4j is much higher than that in Spring AI. For example, for Spring AI to implement streaming dialogue, it only takes one line of code to complete it:

@RequestMapping(value = "/streamChat", produces = "text/event-stream")
public Flux<String> streamChat(@RequestParam(value = "msg") String msg) {
    return (msg);
}

The implementation steps of LangChain4j are as follows:

  1. Add langchain4j-reactor dependency.
  2. Set the configuration file, configure streaming-chat-model api-key and model-name.
  3. Creates an AI Service and returns a Flux object.
  4. Only by calling Ai Service can stream output be achieved.

The specific implementation will not be listed here. You can see the complexity of LangChain4j's implementation?

There are many similar scenarios. For example, Spring AI implements MCP Client. You only need to add dependencies, set configuration information, and then set a line of defaultTools or tools to achieve it, as follows:

(chatModel)
            .defaultTools(()) 
            .build();

But the implementation of LangChain4j is very complicated. In addition to adding dependencies, you also need:

  1. Create a Transport Protocol McpTransport.
  2. Create an MCP client McpClient.
  3. Create Tools (Provider) object ToolProvider.
  4. Build AiService.
  5. Execute an MCP Server call.

The specific implementation code is as follows:

@RequestMapping("/chat")
 public String chat(@RequestParam String question) {
     // 1. Create a transmission protocol
     McpTransport transport = new ()
             .sseUrl("http://localhost:8686/sse")
             .logRequests(true) // if you want to see the traffic in the log
             .logResponses(true)
             .build();
     // 2. Create an MCP client
     McpClient mcpClient = new ()
             .transport(transport)
             .build();
     // 3. Create Tools (Provider) object
     ToolProvider toolProvider = ()
             .mcpClients((mcpClient))
             .build();
     // 4. Build AiService
     ToolsAiService aiService = ()
             .chatLanguageModel(chatModel)
             .toolProvider(toolProvider)
             .build();
     // 5. Call MCP Server
     return (question);
 }

summary

In addition to the complex use of LangChain4j, the documentation of LangChain4j is not complete. Either there are no key implementation code cases or the documentation is simply written incorrectly. LangChain4j has many pitfalls, and in the end, they can only solve and use related functions by looking at the latest source code. Therefore, the learning and use cost of LangChain4j is very high.

Ecological support

Spring AI is officially provided by Spring, so it is better to support the entire Spring ecosystem., and it has better stability; and LangChain4j not only supports Spring, but also supports Java native writing and Quarkus framework.But LangChain4j's overall support for the Spring ecosystem is a little worse, for example, the ImageModel inside it does not provide Spring Boot automatic assembly implementation, and some big models such as Zhipu AI does not provide Spring Boot support at all.

summary

If it is a simple function and has a tight development cycle, you can use Spring AI; if it is a complex function and has many customization requirements, you can use LangChain4j, which has higher functions and flexibility. But using LangChain4j means you need to endure the inconspicuous writing of LangChain4j and the high cost of learning and use.

This article has been included in my technical site, which contains contents: Spring AI, LangChain4j, MCP, Function Call, RAG, vector database, Prompt, multimodal, vector database, embedding model and other contents.

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