Spring AI Alibaba Admin

Spring AI Alibaba Repo: https://github.com/alibaba/spring-ai-alibaba

Spring AI Alibaba Website: https://java2ai.com

Spring AI Alibaba Website Repo: https://github.com/springaialibaba/spring-ai-alibaba-website

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Project Background

Agent Studio is an AI Agent development and evaluation platform based on Spring AI Alibaba, designed to provide developers and enterprises with a complete AI Agent lifecycle management solution. The platform supports a complete workflow from Prompt engineering, dataset management, evaluator configuration to experiment execution and result analysis, helping users quickly build, test, and optimize AI Agent applications.

Core Features

🤖 Prompt Management

Prompt Template Management: Create, update, and delete Prompt templates
Version Control: Support for Prompt version management and history tracking
Real-time Debugging: Provide online Prompt debugging and streaming responses
Session Management: Support for multi-turn conversation session management

📊 Dataset Management

Dataset Creation: Support for importing and creating datasets in multiple formats
Version Management: Dataset version control and history management
Data Item Management: Fine-grained data item CRUD operations
Create from Trace: Support for creating datasets from OpenTelemetry trace data

⚖️ Evaluator Management

Evaluator Configuration: Support for creating and configuring various evaluators
Template System: Provide evaluator templates and custom evaluation logic
Debugging Features: Support for online evaluator debugging and testing
Version Management: Evaluator version control and release management

🧪 Experiment Management

Experiment Execution: Automated execution of evaluation experiments
Result Analysis: Detailed experiment result analysis and statistics
Experiment Control: Support for starting, stopping, restarting, and deleting experiments
Batch Processing: Support for batch experiment execution and result comparison

📈 Observability

Trace Tracking: Integrated OpenTelemetry providing complete trace tracking
Service Monitoring: Support for service list and overview statistics
Trace Analysis: Provide detailed Trace details and Span analysis

🔧 Model Configuration

Multi-model Support: Support for mainstream AI models including OpenAI, DashScope, DeepSeek
Configuration Management: Unified configuration and management of model parameters
Dynamic Switching: Support for dynamic updates of model configuration at runtime

System Architecture

Overall Architecture

🚀 Quick Start

Prerequisites

🐳 Docker (for containerized deployment) + Docker Compose: 2.0+
☕ Java 17+ (for source code execution) + Maven: 3.8+
🌐 AI Model Provider API Keys, supporting OpenAI, DashScope, DeepSeek

Running from Source Code

1. Clone the Project

bash

git clone https://github.com/spring-ai-alibaba/spring-ai-alibaba-admin.git
cd spring-ai-alibaba-admin

2. Configure Your API Keys

Modify the model configuration in spring-ai-alibaba-admin-server-start/model-config.yaml according to your model provider.

If you use DashScope, please refer to the model-config-dashscope.yaml template for configuration
If you use DeepSeek, please refer to the model-config-deepseek.yaml template for configuration
If you use OpenAI, please refer to the model-config-openai.yaml template for configuration

💡 Get Your DashScope API Key: Visit Alibaba Cloud Bailian Console to get a free API key.

3. Nacos Configuration (Optional)

If you need to modify the Nacos address, please update the configuration in the spring-ai-alibaba-admin-server-start/src/main/resources/application.yml file

yaml

nacos:
  server-addr: ${nacos-address}

4. Start SAA Admin

4.1 Start Middleware Services

Navigate to the docker/middleware directory in the project root and execute the startup script to launch the required middleware services (MySQL, Elasticsearch, Nacos, Redis, RocketMQ):

bash

cd docker/middleware
sh run.sh

4.2 Start Backend Service

Navigate to the spring-ai-alibaba-admin-server-start directory and start the application:

bash

mvn spring-boot:run

4.3 Start Frontend Service

Navigate to the frontend directory in the project root, read the corresponding README to install dependencies and configure the environment, then start the service:

bash

cd packages/main
npm run dev

5. Access the Application

Open your browser and visit http://localhost:8000 to use the SAA Admin platform.

At this point, you can already manage, debug, evaluate, and observe prompts on the platform. If you expect your Spring AI Alibaba Agent application to integrate with Nacos for prompt loading and dynamic updates, and observe the online running status, you can refer to step 6 to configure your AI Agent application.

6. Connect Your AI Agent Application

In your Spring AI Alibaba Agent application, add the following dependencies

xml

<dependencies>
    <!-- Introduce spring ai alibaba agent nacos proxy module -->
    <dependency>
        <groupId>com.alibaba.cloud.ai</groupId>
        <artifactId>spring-ai-alibaba-agent-nacos</artifactId>
        <version>{spring.ai.alibaba.version}</version>
    </dependency>

    <!-- Introduce observability module -->

    <dependency>
        <groupId>com.alibaba.cloud.ai</groupId>
        <artifactId>spring-ai-alibaba-autoconfigure-arms-observation</artifactId>
        <version>{spring.ai.alibaba.version}</version>
    </dependency>
    
    
    <!-- For implementing various OTel related components, such as automatic loading of Tracer, Exporter -->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-actuator</artifactId>
    </dependency>
    
    <!-- For connecting micrometer generated metrics to otlp format -->
    <dependency>
        <groupId>io.micrometer</groupId>
        <artifactId>micrometer-registry-otlp</artifactId>
    </dependency>
    
    <!-- For replacing micrometer underlying trace tracer with OTel tracer -->
    <dependency>
        <groupId>io.micrometer</groupId>
        <artifactId>micrometer-tracing-bridge-otel</artifactId>
    </dependency>
    
    <!-- For reporting spans generated by OTel tracer according to otlp protocol -->
    <dependency>
        <groupId>io.opentelemetry</groupId>
        <artifactId>opentelemetry-exporter-otlp</artifactId>
    </dependency>

    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-autoconfigure-model-tool</artifactId>
        <version>1.0.0</version>
    </dependency>
</dependencies>

Specify Nacos address and promptKey

yaml

    spring.ai.alibaba.agent.proxy.nacos.serverAddr={replace nacos address, example: 127.0.0.1:8848}
    spring.ai.alibaba.agent.proxy.nacos.username={replace nacos username, example: nacos}
    spring.ai.alibaba.agent.proxy.nacos.password={replace nacos password, example: nacos}
    spring.ai.alibaba.agent.proxy.nacos.promptKey={replace with promptKey, example: mse-nacos-helper}

Set observability parameters

yaml

    management.otlp.tracing.export.enabled=true
    management.tracing.sampling.probability=1.0
    management.otlp.tracing.endpoint=http://{admin address}:4318/v1/traces
    management.otlp.metrics.export.enabled=false
    management.otlp.logging.export.enabled=false
    management.opentelemetry.resource-attributes.service.name=agent-nacos-prompt-test
    management.opentelemetry.resource-attributes.service.version=1.0
    spring.ai.chat.client.observations.log-prompt=true
    spring.ai.chat.observations.log-prompt=true
    spring.ai.chat.observations.log-completion=true
    spring.ai.image.observations.log-prompt=true
    spring.ai.vectorstore.observations.log-query-response=true
    spring.ai.alibaba.arms.enabled=true
    spring.ai.alibaba.arms.tool.enabled=true
    spring.ai.alibaba.arms.model.capture-input=true
    spring.ai.alibaba.arms.model.capture-output=true

License

This project is open source under the Apache License 2.0 license.

Contributing

We welcome submitting Issues and Pull Requests to help improve the project.