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spring ai alibaba

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Basic Information

Spring AI Alibaba is an agentic AI framework for Java developers to build chatbots, workflow orchestrations, and multi-agent applications. It provides a graph-based multi-agent framework inspired by LangGraph, tooling to generate and debug graph code, and a set of starters and libraries to integrate agent capabilities into Spring applications. The project targets enterprise scenarios by offering connectors and patterns for production concerns such as model proxying, MCP discovery, observability, and RAG-based retrieval. The README highlights example applications and platforms built on the framework, including a Playground demo, JManus for plan-and-act agents, and DeepResearch for research/reporting agents. The repository includes guidance to add a BOM and starter dependency for Java projects, notes a JDK 17+ requirement, and points to additional starters for graph core, NL2SQL, and Nacos MCP client to help developers get started quickly.

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5395
App URL
https://github.com/alibaba/spring-ai-alibaba
Github Repository
https://github.com/alibaba/spring-ai-alibaba/blob/main/README.md

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App Details

Features
Spring AI Alibaba provides a graph-based multi-agent orchestration layer with prebuilt workflow nodes and support for nested and parallel graphs. It includes multi-agent modes such as ReAct and Supervisor, native streaming, human-in-the-loop execution with pause and resume, memory and persistent storage, and graph state snapshotting. The framework can export graphs to PlantUML and Mermaid formats and integrates with low-code platforms. Enterprise integrations include Nacos MCP registry for distributed discovery and load balancing, Higress LLM proxy compatibility, Alibaba Cloud Bailian for RAG and ChatBI for NL2SQL, vector store support, and hooks for observability via OpenTelemetry-compatible systems like Langfuse and ARMS. The repo also documents starters, example Playground, and agent product templates like JManus and DeepResearch.
Use Cases
The framework helps developers move AI agents from demo to production by providing reusable components, enterprise integrations, and operational patterns. Teams can author and debug agent workflows as graphs, reuse planning and tooling for deterministic enterprise agents, and leverage built-in support for model proxying and MCP discovery to route requests to model services. Integrations with Bailian enable managed RAG retrieval and NL2SQL, while observability integrations help evaluate and trace agent behavior. Example Playground and product templates lower the barrier to prototype chatbots, multi-round conversations, image generation, tool calling, and research agents. The provided Maven BOM and starters simplify dependency management and onboarding for Java projects running on JDK 17 or later.

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