Spring AI Alibaba
A Java framework for orchestrating agents, workflows, and long-running stateful execution.
- Source repo
- alibaba/spring-ai-alibaba
- Stars
- ★ 11k
- Last updated
- 1mo ago
- License
- Apache-2.0
- Primary language
- Java
- FA score
- 53/100 · Major gaps
At a glance
- How it runs
- Works with
- Portable with changesOpenAI API (Partial support)
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- A Java team breaking a task into sequential, parallel, routed, or looping subtasks can use the built-in multi-agent patterns.
- Not a fit if
- Non-Java teams: requires JDK 17+, Spring, and Maven builds
- Users wanting a ready-made hosted UI; Admin has no local start command here
- Source review
- 53/100 · Major gaps 1 safety controls not found
What does this agent do, and when should you use it?
Spring AI Alibaba is a Java-focused framework for building agentic, workflow, and multi-agent applications. Its Agent Framework includes Context Engineering and Human In The Loop support, with built-in SequentialAgent, ParallelAgent, RoutingAgent, and LoopAgent patterns. The underlying spring-ai-alibaba-graph runtime provides persistence, workflow orchestration, and streaming for long-running stateful agents, while also exposing a Graph API for custom workflows. The repository also identifies Spring AI Alibaba Admin for visual development, observability, evaluation, and MCP management, plus spring-ai-alibaba-studio for visual debugging. It uses Spring AI concepts to support providers including DashScope and OpenAI, tool calling, MCP, and Nacos-backed A2A coordination.
Java developers can compose SequentialAgent, ParallelAgent, RoutingAgent, and LoopAgent to run multi-step agent workflows. The Agent Framework supplies policies for human-in-the-loop handling, context compaction and editing, model and tool-call limits, tool retry, planning, and dynamic tool selection. For conditional routing, nested graphs, parallel execution, and state management, developers can use the spring-ai-alibaba-graph Graph API and export workflows as PlantUML or Mermaid. The framework works with Spring AI concepts including ChatModel, MCP, tools, and messages, and its A2A support integrates with Nacos for distributed coordination. The included ChatBot example uses spring-ai-alibaba-starter-dashscope and serves a browser chat UI at http://localhost:8080/chatui/index.html.
- A Java team breaking a task into sequential, parallel, routed, or looping subtasks can use the built-in multi-agent patterns.
- A team building a long-running, stateful workflow with conditional branches, nested graphs, and parallel work can use the Graph API directly.
- A Spring AI application using DashScope, OpenAI, or another supported provider and requiring tool calling or MCP can integrate this framework.
- A product team that needs human review, context compaction, retry behavior, or tool-call limits can use the documented context-engineering policies.
- A real-time voice application that streams audio or text over WebSocket and returns generated audio can use the voice-agent example as a reference.
- A Nacos-based distributed system coordinating agents across services can use the documented A2A support.
How do you install or deploy this agent?
Prerequisites: JDK 17+, a selected LLM provider, and that provider's API key. To run the repository ChatBot example:
git clone --depth=1 https://github.com/alibaba/spring-ai-alibaba.git
cd spring-ai-alibaba
export AI_DASHSCOPE_API_KEY=your-api-key
./mvnw -pl examples/chatbot spring-boot:runThe example uses spring-ai-alibaba-starter-dashscope. Maven is optional when using mvnw.
How do you use this agent?
After the ChatBot starts, open http://localhost:8080/chatui/index.html in a browser. The README's dependency example includes com.alibaba.cloud.ai:spring-ai-alibaba-agent-framework:1.1.2.0 and, for DashScope, com.alibaba.cloud.ai:spring-ai-alibaba-starter-dashscope:1.1.2.1; set AI_DASHSCOPE_API_KEY before running. Choose SequentialAgent, ParallelAgent, RoutingAgent, or LoopAgent for built-in orchestration patterns, or use the Graph API when the workflow needs lower-level control.
What are this agent's strengths and limitations?
- Provides Java/Spring AI teams with built-in SequentialAgent, ParallelAgent, RoutingAgent, and LoopAgent orchestration patterns.
- Its graph runtime explicitly covers persistence, streaming, conditional routing, nested graphs, parallel execution, and state management, with PlantUML and Mermaid export.
- Includes concrete context-engineering policies such as human-in-the-loop handling, context compaction, tool retry, planning, and dynamic tool selection.
- Covers model integration, tool calling, MCP, and Nacos-backed A2A coordination in the same framework.
- The documented ChatBot path requires JDK 17+, network access, and an LLM-provider API key.
- The fastest documented example is based on the DashScope starter and AI_DASHSCOPE_API_KEY; using another provider requires the relevant Spring AI integration configuration.
- Admin's visual development, observability, evaluation, and MCP management are described, but this README does not provide a copyable local startup command for Admin.
- Agent Framework and the Graph API operate at different abstraction levels; direct Graph API use leaves workflow design more directly to the adopter.
How does this agent compare with similar options?
The repository positions Agent Framework as the recommended higher-level path and spring-ai-alibaba-graph as its underlying runtime: the former supplies built-in agent and workflow patterns, while the latter enables more flexible custom multi-agent workflows through its Graph API. It also states that Admin can integrate with open-source low-code platforms such as Dify for migration from DSL to a Spring AI Alibaba project.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Spring AI Alibaba This agent | 53 · Major gaps | FrameworkFree + model costs | ★ 11k | 1mo ago | Java | — |
| tRPC-Agent-Go | 53 · Major gaps | Library / SDKFree + model costs | ★ 1.9k | today | Go | OpenAI API |
| Pocket Flow | 41 · Major gaps | Library / SDKFree + model costs | ★ 11k | 3mo ago | Python | — |
| Polter Terminal Agent Supervisor | 80 · Good | Desktop appFree + model costs | ★ 20 | 1d ago | Zig | Claude Code |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows the framework provides context engineering and human-in-the-loop (HITL) features, but least privilege or user confirmation mechanisms are not explicit. Data flow transparency is limited, and sensitive data handling is not detailed. Dependency security has gitleaks protection but no vulnerability scanning details. External effects (e.g., tool calls) are not explicitly restricted. Rollback mechanisms are not mentioned. Source attribution has Apache-2.0 license and submodule copyright notices, but publisher is unverified.
Self-consistency: README description aligns with project structure, but version numbers are inconsistent in README (1.1.2.2 vs 1.1.2.0/1.1.2.1). Dependency availability: Maven Central and CI exist, but no dependency locking. Failure messages: test resources show error handling examples, but overall failure message quality not assessed.
Audience is clearly Java developers, scenarios include multi-agent, voice, multimodal. Capability boundaries are listed in README, but trigger precision (e.g., tool call conditions) not detailed. Environment fit: requires JDK 17+, supports multiple LLM providers, but deployment environment requirements not specified.
Information architecture is clear with documentation links and project structure. Install notes provide quick start. Naming stability: version numbers visible on Maven Central. Examples and FAQ: examples directory exists. Known limitations not explicitly listed. License is Apache-2.0 with submodule copyrights. Versioning changelog not provided. Maintenance responsibility has team contact info.
Output usability: provides chatbot example and UI. Marginal value: offers multi-agent orchestration, context engineering, etc. Cost-benefit: open-source free, but no performance or cost data.
Claim traceability: README features have documentation links. Cross-source corroboration: CI status and Maven Central badges exist, but not independently verified. Fact-inference separation: README distinguishes feature descriptions and doc links, but not explicit fact vs inference.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Publisher identity is unverified; be cautious about supply chain risks.
- Version numbers in README are inconsistent, which may affect dependency selection.
- Known limitations and rollback mechanisms are not provided; additional evaluation needed for production use.
- Dependency security relies only on gitleaks; no vulnerability scanning details provided.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
v2.0.0-M1.1 Pre-release
Release notes excerpt
Highlights Milestone release upgrading to Spring AI 2.0.0-M1 and Spring Boot 4.0.0. Dependency Upgrade - Spring AI: 1.1.2 → 2.0.0-M1 - Spring AI Alibaba Extensions → 2.0.0-M1.1 - Spring Boot: 3.5.8 → 4.0.0 - Redisson: 3.22.0 → 3.52.0 - Removed explicit driver dependency versions for MySQL, PostgreSQL, and Oracle (now managed by Spring Boot BOM)View on GitHub ↗ -
Release notes excerpt
AgentScope Integration [AgentScope Java](https://github.com/agentscope-ai/agentscope-java) is an agent-oriented programming framework for building LLM-powered applications. - AgentScope – AgentScopeAgent wraps [AgentScope ReActAgent as a BaseAgent for use in graph workflows](https://github.com/alibaba/spring-ai-alibaba/tree/main/examples/agentscope). ```xml Add the following dependency to ochestrate AgentScope using SAA Graph <dependency> <groupId>com.alibaba.cloud.ai</groupId> <artifactId>spring-ai-alibaba-starter-agentscope</artifactId> <version>1.1.2.2</version> …View on GitHub ↗ -
Release notes excerpt
Please use 1.1.2.2 instead. 1.1.2.1 was reported with several bugs that have been fixed in 1.1.2.2.View on GitHub ↗