Spring AI Alibaba

Architecture

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 built-in 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.

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: Compose multiple agents with built-in patterns including SequentialAgent, ParallelAgent, RoutingAgent, and LoopAgent for complex task execution.

• Multimodal Support: ReactAgent with text and media input (image understanding). ReactAgent with tool based image or audio generation.

• Voice Agent: WebSocket-based real-time voice agent that supports streaming audio or text input and responds with generated audio.

• Context Engineering: 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: 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: Agent-to-Agent communication support with Nacos integration, enabling distributed agent coordination and collaboration across services.

• Rich Model, Tool and MCP Support: Leveraging core concepts of Spring AI, supports multiple LLM providers (DashScope, OpenAI, etc.), tool calling, and Model Context Protocol (MCP).

• One-stop Agent Platform: Build agent in a visualized way, deploy agent without code or export as a standalone java project.

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.

1. Download the code.

   git clone --depth=1 https://github.com/alibaba/spring-ai-alibaba.git
   cd spring-ai-alibaba
   

2. Start the ChatBot.

   Before starting, set API-KEY first (visit Aliyun Bailian to get API-KEY):

   # 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
   

   # Maven installation is optional when using mvnw.
   ./mvnw -pl examples/chatbot spring-boot:run
   

3. Chat with ChatBot.

   Open the browser and visit http://localhost:8080/chatui/index.html to chat with the ChatBot.

Chatbot Code Explained

1. Add dependencies

   <dependencies>
     <dependency>
       <groupId>com.alibaba.cloud.ai</groupId>
       <artifactId>spring-ai-alibaba-agent-framework</artifactId>
       <version>1.1.2.0</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>1.1.2.1</version>
     </dependency>
   </dependencies>
   

2. Define Chatbot

   For more details of how to write a Chatbot, please check the Quick Start on our official website.

📚 Documentation

• Overview - High level overview of the framework
• Quick Start - Get started with a simple agent
• Agent Framework Tutorials - Step by step tutorials
• Use Graph API to Build Complex Workflows - In-depth user guide for building multi-agent and workflows
• Spring AI Basics - Ai Application basic concepts, including ChatModel, MCP, Tool, Messages, etc.
• Chat Memory - Spring AI reference for chat memory repositories and usage

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	A low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents.
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	Extended implementations for Spring AI core concepts, including DashScopeChatModel, MCP registry, etc.
Spring AI Alibaba Examples	Spring AI Alibaba Examples.
JManus	A Java implementation of Manus built with Spring AI Alibaba, currently used in many applications within Alibaba Group.
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	Deep Research implemented based on spring-ai-alibaba-graph.

Contact Us

• Dingtalk Group (钉钉群), search 94405033092 and join.

• WeChat Group (微信公众号), scan the QR code below and follow us.

Resources

• AI-Native Application Architecture White Paper：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.

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