# spring-ai-alibaba
**Repository Path**: alibaba/spring-ai-alibaba
## Basic Information
- **Project Name**: spring-ai-alibaba
- **Description**: An Application Framework for Java Developers
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 103
- **Forks**: 71
- **Created**: 2024-10-31
- **Last Updated**: 2026-01-08
## Categories & Tags
**Categories**: ai
**Tags**: None
## README
# [Spring AI Alibaba](https://java2ai.com)
[](https://www.apache.org/licenses/LICENSE-2.0.html)
[](https://github.com/alibaba/spring-ai-alibaba/actions?query=workflow%3A%22%F0%9F%9B%A0%EF%B8%8F+Build+and+Test%22)
[](https://deepwiki.com/alibaba/spring-ai-alibaba)
[](https://maven-badges.herokuapp.com/maven-central/com.alibaba.cloud.ai/spring-ai-alibaba)
A production-ready framework for building Agentic, Workflow, and Multi-agent applications.
## 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 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.
## 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 Aliyun Bailian 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.
## Chatbot Code Explained
1. Add dependencies
```xml
com.alibaba.cloud.ai
spring-ai-alibaba-agent-framework
1.1.0.0
com.alibaba.cloud.ai
spring-ai-alibaba-starter-dashscope
1.1.0.0
```
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.
## 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)
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Made with ❤️ by the Spring AI Alibaba Team