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<br />
Solon-AI
</h1>
<p align="center">
<strong>Java LLM(tool, skill) & RAG & MCP & Agent(ReAct, Team) Application development framework</strong>
<br/>
<strong>Restraint, efficiency and openness</strong>
<br/>
<strong>It is the same type of development framework as LangChain, LangGraph and LlamaIndex</strong>
</p>
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<a href="https://solon.noear.org/article/learn-solon-ai">https://solon.noear.org/article/learn-solon-ai</a>
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##### Language: English | [中文](README_CN.md)
<hr />
## 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:
* https://gitee.com/solonlab/solon-ai-mcp-embedded-examples
* https://gitcode.com/solonlab/solon-ai-mcp-embedded-examples
* https://github.com/solonlab/solon-ai-mcp-embedded-examples
## 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...
## Example Agent synthesis project (can be used directly for production or customization)
* [SolonCode (Java impl version of "Claude Code")](../../../../opensolon/soloncode)
* [SolonClaw (Java impl version of "OpenClaw")](../../../../opensolon/solonclaw)
## Core Module Experience
* ChatModel(General Purpose LLM call interface)
Support for synchronous and Reactive calls, built-in dialect adaptation, Tool, Skill, ChatSession, etc.
```java
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")
.defaultTalentAdd(new McpGatewayTalent())
.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>
```
* Talents（Solon AI Talents）
```java
Talent talent = new TalentDesc("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.talentAdd(talent))
.call();
```
* RAG（知识库）
It provides full-link support from DocumentLoader, DocumentSplitter, EmbeddingModel, and RerankingModel.
```java
//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.
```java
//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.
```java
//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.
```yaml
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](../../../../opensolon/solon) | Solon ,Main code repository |
| [/opensolon/solon-examples](../../../../opensolon/solon-examples) | Solon ,Official website supporting sample code repository |
| | |
| [/opensolon/solon-ai](../../../../opensolon/solon-ai) | Solon Ai ,Code repository |
| [/opensolon/solon-flow](../../../../opensolon/solon-flow) | Solon Flow ,Code repository |
| [/opensolon/solon-expression](../../../../opensolon/solon-expression) | Solon Expression ,Code repository |
| [/opensolon/solon-cloud](../../../../opensolon/solon-cloud) | Solon Cloud ,Code repository |
| [/opensolon/solon-admin](../../../../opensolon/solon-admin) | Solon Admin ,Code repository |
| [/opensolon/solon-integration](../../../../opensolon/solon-integration) | Solon Integration ,Code repository |
| [/opensolon/solon-java17](../../../../opensolon/solon-java17) | Solon Java17 ,Code repository（base java17） |
| [/opensolon/solon-java25](../../../../opensolon/solon-java25) | Solon Java25 ,Code repository（base java25） |
| | |
| [/opensolon/soloncode](../../../../opensolon/soloncode) | SolonCode(Java8 impl version of "Claude Code") ,Code repository |
| [/opensolon/solonclaw](../../../../opensolon/solonclaw) | SolonClaw(Java8 impl version of "OpenClaw") ,Code repository |
| | |
| [/opensolon/solon-maven-plugin](../../../../opensolon/solon-gradle-plugin) | Solon Maven ,Plugin code repository |
| [/opensolon/solon-gradle-plugin](../../../../opensolon/solon-gradle-plugin) | Solon Gradle ,Plugin code repository |
| | |
| [/opensolon/solon-idea-plugin](../../../../opensolon/solon-idea-plugin) | Solon Idea ,Plugin code repository |
| [/opensolon/solon-vscode-plugin](../../../../opensolon/solon-vscode-plugin) | Solon VsCode ,Plugin code repository |
## FAQ
### What is Solon AI?
Solon AI is a full-scenario Java AI development framework that deeply integrates LLM large models, RAG knowledge bases, MCP protocol, and Agent collaboration orchestration. It's designed for building production-grade AI applications with Java.
### How does Solon AI differ from Python frameworks like LangChain?
Solon AI is built **specifically for Java developers** with seamless integration into the Java ecosystem:
**Key differences:**
- **Java-native**: Fits perfectly into Solon, SpringBoot, Vert.X, Quarkus ecosystems
- **JDK 8-25 support**: Broad Java version compatibility
- **Multi-model dialects**: Unified interface adapts model differences automatically
- **Graph-driven orchestration**: Transforms Agent reasoning into observable computation flow graphs
### What components does Solon AI provide?
- **ChatModel**: General-purpose LLM call interface with Tool, Skill, ChatSession support
- **Skills**: Dynamic admission and instruction injection
- **RAG**: Full-link support (DocumentLoader, DocumentSplitter, EmbeddingModel, RerankingModel)
- **MCP**: Deep integration with Model Context Protocol (MCP_2025_06_18)
- **Agent**: ReAct introspective reasoning and Team collaboration
- **Ai Flow**: YAML-based flow orchestration (Dify-like low-code experience)
### What LLM providers are supported?
Supported via dialect adaptation:
- OpenAI, Gemini, Claude
- Ollama (local models)
- DeepSeek, Dashscope (Alibaba)
- Custom endpoints
### How do I get started?
Add Maven dependency:
```xml
<dependency>
<groupId>org.noear</groupId>
<artifactId>solon-ai</artifactId>
</dependency>
```
Basic usage:
```java
ChatModel chatModel = ChatModel.of("http://127.0.0.1:11434/api/chat")
.provider("ollama")
.model("qwen2.5:1.5b")
.build();
AssistantMessage result = chatModel.prompt("Hello").call().getMessage();
```
### How do I add tools?
```java
chatModel.prompt("What's the weather?")
.options(op -> op.toolAdd(new WeatherTools()))
.call();
```
### How do I use RAG?
```java
EmbeddingModel embeddingModel = EmbeddingModel.of(apiUrl)
.apiKey(apiKey).provider(provider).model(model).build();
InMemoryRepository repository = new InMemoryRepository(embeddingModel);
repository.insert(new PdfLoader(pdfUri).load());
List<Document> docs = repository.search(query);
ChatMessage message = ChatMessage.ofUserAugment(query, docs);
chatModel.prompt(message).call();
```
### What is MCP integration?
Solon AI provides both MCP server and client:
**Server:**
```java
@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:**
```java
McpClientProvider client = McpClientProvider.builder()
.channel(McpChannel.STREAMABLE)
.url("http://localhost:8080/mcp")
.build();
```
### What are the Agent patterns?
- **ReActAgent**: Reflective agent with Think → Call → Observe → Summarize loop
- **TeamAgent**: Multi-agent collaboration with 6 preset protocols (HIERARCHICAL, etc.)
### Where can I find help?
- **Documentation**: [solon.noear.org/article/learn-solon-ai](https://solon.noear.org/article/learn-solon-ai)
- **Examples**: [solonlab/solon-ai-mcp-embedded-examples](https://github.com/solonlab/solon-ai-mcp-embedded-examples)
- **SolonCode**: Java version of "Claude Code" - [github.com/opensolon/soloncode](https://github.com/opensolon/soloncode)
- **SolonClaw**: Java version of "OpenClaw" - [github.com/opensolon/solonclaw](https://github.com/opensolon/solonclaw)
