solon-ai

Java AI application development framework (supports LLM-tool,skill; RAG; MCP; Agent-ReAct,Team-Agent). Compatible with java8 ~ java25. It can also be embedded in SpringBoot, jFinal, Vert.x, Quarkus, and other frameworks.

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Solon-AI is a Java AI & MCP application development framework that supports various AI development capabilities. It is designed to be versatile, efficient, and open for integration with frameworks like SpringBoot, jFinal, and Vert.x. The framework provides examples of embedding solon-ai(& mcp) and showcases interfaces for chat models, function calling, vision, RAG (EmbeddingModel, Repository, DocumentLoader, RerankingModel), Ai Flow, MCP server, MCP client, and MCP Proxy. Solon-AI is part of the Solon project ecosystem, which includes other repositories for different functionalities.

README:

Solon-AI

Java LLM(tool, skill) & RAG & MCP & Agent(ReAct, Team) Application development framework
Restraint, efficiency and openness
It is the same type of development framework as LangChain, LangGraph and LlamaIndex

https://solon.noear.org/article/learn-solon-ai

Language: English | 中文

简介

Solon AI is one of the core subprojects of the Solon project. It is a full-scenario Java AI development framework, which aims to deeply integrate LLM large model, RAG knowledge base, MCP protocol and Agent collaboration choreography.

Full use case support: fits perfectly into the Solon ecosystem and can be seamlessly integrated into frameworks like SpringBoot, Vert.X, Quarkus, etc.
Multi-model dialects: Adapt model differences by dialect using ChatModel's unified interface (OpenAI, Gemini, Claude, Ollama, DeepSeek, Dashscope, etc.).
Graph-driven orchestration: supports the transformation of Agent reasoning into observable and governable computation flow graphs.

Examples of embeddings (including third-party frameworks) for solon-ai:

What types of applications can be developed?

General-purpose Autonomous Agents (e.g., Manus, OpenOperator)
Intelligent Assistants & RAG Knowledge Bases (e.g., Dify, Coze)
Multi-Agent Collaborative Orchestration (e.g., AutoGPT, MetaGPT)
Business-Driven Controlled Workflows (e.g., AI-enhanced DingTalk/Lark approvals, SAP Intelligent Modules)
Intelligent Document Processing & ETL (e.g., Instabase, Unstructured.io)
Real-time Data Insights & Dashboards (e.g., Text-to-SQL applications)
Automated Testing & Quality Assurance (e.g., GitHub Copilot Workspace)
Low-Code/Visual AI Workflow Platforms (e.g., LangFlow, Flowise)
And more...

Core Module Experience

ChatModel(General Purpose LLM call interface)

Support for synchronous and Reactive calls, built-in dialect adaptation, Tool, Skill, ChatSession, etc.

ChatModel chatModel = ChatModel.of("http://127.0.0.1:11434/api/chat")
                .provider("ollama") //Need to specify vendor, used to identify interface style (also called dialect)
                .model("qwen2.5:1.5b")
                .defaultSkillAdd(new ToolGatewaySkill())
                .build();

// Synchronize the call and print the response message
AssistantMessage result = ChatchatModel.prompt("The weather in Hangzhou today？")
         .options(op->op.toolAdd(new WeatherTools())) //Adding tools
         .call()
         .getMessage();
System.out.println(result);

// Stream call
chatModel.prompt("hello").stream(); //Publisher<ChatResponse>

Skills（Solon AI Skills）

Skill skill = new SkillDesc("order_expert")
        .description("Order Assistant")
        // Dynamic admission: Activated only when "order" is mentioned
        .isSupported(prompt -> prompt.getUserMessageContent().contains("order"))
        // Dynamic instructions: Inject different Sops depending on whether the user is a VIP or not
        .instruction(prompt -> {
            if ("VIP".equals(prompt.getMeta("user_level"))) {
                return "This is a VIP customer, please call fast_track_tool first.";
            }
            return "Process the order inquiry according to the normal process.";
        })
        .toolAdd(new OrderTools());

chatModel.prompt("Where is my order from yesterday？")
         .options(o->o.skillAdd(skill))
         .call();

RAG（知识库）

It provides full-link support from DocumentLoader, DocumentSplitter, EmbeddingModel, and RerankingModel.

//Building a Knowledge Warehouse
EmbeddingModel embeddingModel = EmbeddingModel.of(apiUrl).apiKey(apiKey).provider(provider).model(model).batchSize(10).build();
RerankingModel rerankingModel = RerankingModel.of(apiUrl).apiKey(apiKey).provider(provider).model(model).build();
InMemoryRepository repository = new InMemoryRepository(TestUtils.getEmbeddingModel()); //3.初始化知识库

repository.insert(new PdfLoader(pdfUri).load());

//retrieval
List<Document> docs = repository.search(query);

//You can rearrange it if you want
docs = rerankingModel.rerank(query, docs);

//Cue enhancement is
ChatMessage message = ChatMessage.ofUserAugment(query, docs);

//Calling the llm
chatModel.prompt(message) 
    .call();

MCP (Model Context Protocol)

Deep integration with MCP protocol (MCP_2025_06_18), supporting cross-platform tool, resource, and prompt sharing.

//server
@McpServerEndpoint(channel = McpChannel.STREAMABLE, mcpEndpoint = "/mcp") 
public class MyMcpServer {
    @ToolMapping(description = "Checking the weather")
    public String getWeather(@Param(description = "city") String location) {
        return "It's sunny, 25 degrees";
    }
}

//client
McpClientProvider clientProvider = McpClientProvider.builder()
        .channel(McpChannel.STREAMABLE)
        .url("http://localhost:8080/mcp")
        .build();

Agent (An Agent Experience with Computational Flow Graphs)

The Solon AI Agent transforms reasoning logic into graph-driven collaboration flows, enabling ReAct introspective reasoning and multi-agent Team collaboration.

//Reflective intelligent agent:
ReActAgent agent = ReActAgent.of(chatModel) // 或者用 SimpleAgent.of(chatModel)
    .name("weather_expert")
    .description("Check the weather and provide advice")
    .defaultToolAdd(weatherTool) // Inject MCP or local tools
    .build();

agent.prompt("What to wear in Beijing today？").call(); // Autocomplete: Think -> Call tool -> Observe -> Summarize

// Constructing a team agent: Automatically arranging member roles through protocols
TeamAgent team = TeamAgent.of(chatModel)
    .name("marketing_team")
    .protocol(TeamProtocols.HIERARCHICAL) // Hierarchical collaboration (6 preset protocols)
    .agentAdd(copywriterAgent) // Copywriter expert
    .agentAdd(illustratorAgent) // Illustrator expert
    .build();

team.prompt("Plan a promotion scheme for deep-sea mineral water").call(); // Supervisor automatically decomposes tasks and assigns them to corresponding experts    .defaultToolAdd(weatherTool) // Inject MCP or local tools

Ai Flow（Process orchestration experience）

The low-code flow application of Dify is simulated, and the links such as RAG, hint word enhancement and model call are YAML arranged.

id: demo1
layout:
  - type: "start"
  - task: "@VarInput"
    meta:
      message: "Solon 是谁开发的？"
  - task: "@EmbeddingModel"
    meta:
      embeddingConfig: # "@type": "org.noear.solon.ai.embedding.EmbeddingConfig"
        provider: "ollama"
        model: "bge-m3"
        apiUrl: "http://127.0.0.1:11434/api/embed"
  - task: "@InMemoryRepository"
    meta:
      documentSources:
        - "https://solon.noear.org/article/about?format=md"
      splitPipeline:
        - "org.noear.solon.ai.rag.splitter.RegexTextSplitter"
        - "org.noear.solon.ai.rag.splitter.TokenSizeTextSplitter"
  - task: "@ChatModel"
    meta:
      systemPrompt: "你是个知识库"
      stream: false
      chatConfig: # "@type": "org.noear.solon.ai.chat.ChatConfig"
        provider: "ollama"
        model: "qwen2.5:1.5b"
        apiUrl: "http://127.0.0.1:11434/api/chat"
  - task: "@ConsoleOutput"

# FlowEngine flowEngine = FlowEngine.newInstance();
# ...
# flowEngine.eval("demo1");

Solon Project code repository

Code repository	Description
/opensolon/solon	Solon ,Main code repository
/opensolon/solon-examples	Solon ,Official website supporting sample code repository

/opensolon/solon-expression	Solon Expression ,Code repository
/opensolon/solon-flow	Solon Flow ,Code repository
/opensolon/solon-ai	Solon Ai ,Code repository
/opensolon/solon-cloud	Solon Cloud ,Code repository
/opensolon/solon-admin	Solon Admin ,Code repository
/opensolon/solon-integration	Solon Integration ,Code repository
/opensolon/solon-java17	Solon Java17 ,Code repository（base java17）
/opensolon/solon-java25	Solon Java25 ,Code repository（base java25）

/opensolon/solon-gradle-plugin	Solon Gradle ,Plugin code repository
/opensolon/solon-idea-plugin	Solon Idea ,Plugin code repository
/opensolon/solon-vscode-plugin	Solon VsCode ,Plugin code repository

For Tasks:

Click tags to check more tools for each tasks

build chat model call function/tool analyze vision implement ai flow develop mcp server

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