AI prompts
base on Agentic AI Framework for Java Developers # [Spring AI Alibaba](https://java2ai.com)
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<h3 align="center">
A production-ready framework for building Agentic, Workflow, and Multi-agent applications.
</h3>
<h3 align="center">
<a href="https://java2ai.com/docs/quick-start/" target="_blank">Agent Framework Docs</a>,
<a href="https://java2ai.com/docs/frameworks/graph-core/quick-start/" target="_blank">Graph Docs</a>,
<a href="https://java2ai.com/ecosystem/spring-ai/reference/concepts/" target="_blank">Spring AI</a>,
<a href="https://github.com/alibaba/spring-ai-alibaba/tree/main/examples" target="_blank">Examples</a>.
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## Architecture
<p align="center">
<img src="./docs/imgs/architecture-new.png" alt="architecture" style="max-width: 740px; height: auto" />
</p>
**Spring AI Alibaba Admin** is a one-stop Agent platform that supports visualized Agent development, observability, evaluation, and MCP management, etc. It also integrates with open-source low-code platforms like Dify, enabling rapid migration from DSL to Spring AI Alibaba project.
**Spring AI Alibaba Agent Framework** is an agent development framework that can quickly develop agents with builtin **Context Engineering** and **Human In The Loop** support. For scenarios requiring more complex process control, Agent Framework offers built-in workflows like `SequentialAgent`, `ParallelAgent`, `RoutingAgent`, `LoopAgent` and `SupervisorAgent`.
**Spring AI Alibaba Graph** serves as the underlying runtime of the Agent Framework, providing essential capabilities such as persistence, workflow orchestration, and streaming required for long-running stateful agents. Compared to the Agent Framework, users can build more flexible multi-agent workflows based on the Graph API.
## Core Features
* **[Multi-Agent Orchestration](https://java2ai.com/docs/frameworks/agent-framework/advanced/multi-agent)**: Compose multiple agents with built-in patterns including `SequentialAgent`, `ParallelAgent`, `LlmRoutingAgent`, and `LoopAgent` for complex task execution.
* **[Context Engineering](https://java2ai.com/docs/frameworks/agent-framework/tutorials/hooks)**: Built-in best practices for context engineering policies to improve agent reliability and performance, including human-in-the-loop, context compaction, context editing, model & tool call limit, tool retry, planning, dynamic tool selection.
* **[Graph-based Workflow](https://java2ai.com/docs/frameworks/graph-core/quick-start)**: Graph based workflow runtime and api for conditional routing, nested graphs, parallel execution, and state management. Export workflows to PlantUML and Mermaid formats.
* **[A2A Support](https://java2ai.com/docs/frameworks/agent-framework/advanced/a2a)**: Agent-to-Agent communication support with Nacos integration, enabling distributed agent coordination and collaboration across services.
* **[Rich Model, Tool and MCP Support](https://java2ai.com/integration/chatmodels/dashScope)**: Leveraging core concepts of Spring AI, supports multiple LLM providers (DashScope, OpenAI, etc.), tool calling, and Model Context Protocol (MCP).
* **[One-stop Agent Platform](https://java2ai.com/ecosystem/admin/quick-start)**: Build agent in a visualized way, deploy agent without code or export as a standalone java project.
<p align="center">
<img src="./docs/imgs/saa-admin.png" alt="architecture" style="max-width: 740px; height: auto" />
</p>
## Getting Started
### Prerequisites
* Requires JDK 17+.
* Choose your LLM provider and get the API-KEY.
### Quickly Run a ChatBot
There's a ChatBot example provided by the community at [examples/chatbot](https://github.com/alibaba/spring-ai-alibaba/tree/main/examples/chatbot).
1. Download the code.
```shell
git clone --depth=1 https://github.com/alibaba/spring-ai-alibaba.git
cd spring-ai-alibaba/examples/chatbot
```
2. Start the ChatBot.
Before starting, set API-KEY first (visit <a href="https://bailian.console.aliyun.com/?apiKey=1&tab=api#/api" target="_blank">Aliyun Bailian</a> to get API-KEY):
```shell
# this example uses 'spring-ai-alibaba-starter-dashscope', visit https://java2ai.com to learn how to use OpenAI/DeepSeek.
export AI_DASHSCOPE_API_KEY=your-api-key
```
```shell
mvn spring-boot:run
```
3. Chat with ChatBot.
Open the browser and visit [http://localhost:8080/chatui/index.html](http://localhost:8080/chatui/index.html) to chat with the ChatBot.
<p align="center">
<img src="./docs/imgs/chatbot-chat-ui.gif" alt="chatbot-ui" style="max-width: 740px; height: auto" />
</p>
## Chatbot Code Explained
1. Add dependencies
```xml
<dependencies>
<dependency>
<groupId>com.alibaba.cloud.ai</groupId>
<artifactId>spring-ai-alibaba-agent-framework</artifactId>
<version>1.1.0.0</version>
</dependency>
<!-- Assume you are going to use DashScope Model. Refer to docs for how to choose model.-->
<dependency>
<groupId>com.alibaba.cloud.ai</groupId>
<artifactId>spring-ai-alibaba-starter-dashscope</artifactId>
<version>1.1.0.0</version>
</dependency>
</dependencies>
```
2. Define Chatbot
For more details of how to write a Chatbot, please check the [Quick Start](https://java2ai.com/docs/quick-start) on our official website.
## 📚 Documentation
* [Overview](https://java2ai.com/docs/overview) - High level overview of the framework
* [Quick Start](https://java2ai.com/docs/quick-start) - Get started with a simple agent
* [Agent Framework Tutorials](https://java2ai.com/docs/frameworks/agent-framework/tutorials/agents) - Step by step tutorials
* [Use Graph API to Build Complex Workflows](https://java2ai.com/docs/frameworks/agent-framework/advanced/context-engineering) - In-depth user guide for building multi-agent and workflows
* [Spring AI Basics](https://java2ai.com/ecosystem/spring-ai/reference/concepts) - Ai Application basic concepts, including ChatModel, MCP, Tool, Messages, etc.
## Project Structure
This project consists of several core components:
* spring-ai-alibaba-agent-framework: A multi-agent framework designed for building intelligent agents with built-in context engineering best practices.
* spring-ai-alibaba-graph: The underlying runtime for Agent Framework. We recommend developers to use Agent Framework but it's totally fine to use the Graph API directly.
* spring-ai-alibaba-admin: A one-stop Agent platform that supports visualized Agent development, observability, evaluation, and MCP management, etc.
* spring-ai-alibaba-studio: The embedded ui for quickly debugging agent in a visualized way.
* spring-boot-starters: Starters integrating Agent Framework with Nacos to provide A2A and dynamic config features.
## Spring AI Alibaba Ecosystem
Repository | Description | ⭐
--- | --- | ---
| [Spring AI Alibaba Graph](https://github.com/alibaba/spring-ai-alibaba/tree/main/spring-ai-alibaba-graph-core) | A low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents. | 
| [Spring AI Alibaba Admin](https://github.com/spring-ai-alibaba/spring-ai-alibaba-admin) | Local visualization toolkit for the development of agent applications, supporting project management, runtime visualization, tracing, and agent evaluation. | 
| [Spring AI Extensions](https://github.com/spring-ai-alibaba/spring-ai-extensions) | Extended implementations for Spring AI core concepts, including DashScopeChatModel, MCP registry, etc. | 
| [Spring AI Alibaba Examples](https://github.com/spring-ai-alibaba/examples) | Spring AI Alibaba Examples. | 
| [JManus](https://github.com/spring-ai-alibaba/jmanus) | A Java implementation of Manus built with Spring AI Alibaba, currently used in many applications within Alibaba Group. | 
| [DataAgent](https://github.com/spring-ai-alibaba/dataagent) | A natural language to SQL project based on Spring AI Alibaba, enabling you to query databases directly with natural language without writing complex SQL. | 
| [DeepResearch](https://github.com/spring-ai-alibaba/deepresearch) | Deep Research implemented based on spring-ai-alibaba-graph. | 
## Contact Us
* Dingtalk Group (钉钉群), search `130240015687` and join.
* WeChat Group (微信公众号), scan the QR code below and follow us.
<img src="./docs/imgs/wechat-account.jpg" style="width: 260px; height: auto"/>
## Resources
* [AI-Native Application Architecture White Paper](https://developer.aliyun.com/ebook/8479)：Co-authored by 40 frontline engineers and endorsed by 15 industry experts, this 200,000+ word white paper is the first comprehensive guide dedicated to the full DevOps lifecycle of AI-native applications. It systematically breaks down core concepts and key challenges, offering practical problem-solving approaches and architectural insights.
## Star History
[](https://starchart.cc/alibaba/spring-ai-alibaba)
---
<p align="center">
Made with ❤️ by the Spring AI Alibaba Team
", Assign "at most 3 tags" to the expected json: {"id":"12434","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"