OpenCode vs Spring Boot AI Agents

Summary

Introduction: OpenCode CLI vs Spring Boot AI Agents

Which orchestrates your Spring Boot AI coding workflow better?

Two powerful approaches compete to deliver coder + reviewer + docs automation:

OpenCode CLI: Zero-code, markdown-driven agents with native file editing and bash integration. Your existing AGENTS.md, memory/, skills/ structure works immediately.

Spring Boot AI: Production-grade REST API using Spring AI’s TaskExecutor, ChatMemory, and FileSystemTools. Enterprise deployment with monitoring and scaling.

This comparison analyzes 16 key criteria to determine the best fit for your Spring Boot multi-agent orchestration needs - from personal prototyping to team production systems.

References

Spring AI Architecture

Spring Boot Orchestrator β†’ Local LLM (Ollama)
     ↓
Loads: AGENTS.md, memory/*.md, skills/*.md as prompts

Orchestrator delegates to 3 worker agents via Task tool (Spring AI 1.0.2+).

Complete Setup:

1. pom.xml

<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
        <version>1.0.2</version>
    </dependency>
</dependencies>

2. application.yml

spring:
  ai:
    ollama:
      base-url: http://localhost:11434
      chat:
        options:
          model: llama3.2:8b
    agent:
      orchestrator:
        chat-model: ollama
      coder:
        chat-model: ollama
        system-prompt-file: classpath:agents/coder.md
      reviewer:
        chat-model: ollama  
        system-prompt-file: classpath:agents/reviewer.md
      docs:
        chat-model: ollama
        system-prompt-file: classpath:agents/docs.md

3. OrchestratorAgent.java

@Component
@Agent("orchestrator")
public class OrchestratorAgent {

    private final TaskExecutor taskExecutor;

    public OrchestratorAgent(AgentRegistry registry) {
        this.taskExecutor = new TaskExecutor(registry);
    }

    @AgentTask
    public String orchestrate(@Input String featureRequest) {
        // Read AGENTS.md + memory/project.md as context
        String context = loadFiles("AGENTS.md", "memory/project.md");

        // Delegate via Task tool
        String codeResult = taskExecutor.delegate("coder", 
            "Implement: " + featureRequest + "\nContext: " + context);

        String reviewResult = taskExecutor.delegate("reviewer", codeResult);
        String docsResult = taskExecutor.delegate("docs", codeResult);

        // Log run
        saveRunLog(featureRequest, codeResult, reviewResult, docsResult);

        return "Complete";
    }
}

4. Keep Your .md Files

src/main/resources/
β”œβ”€β”€ AGENTS.md                 # Global rules
β”œβ”€β”€ agents/
β”‚   β”œβ”€β”€ orchestrator.md       # Orchestrator prompt
β”‚   β”œβ”€β”€ coder.md             # Coder system prompt
β”‚   β”œβ”€β”€ reviewer.md          # Reviewer system prompt
β”‚   └── docs.md              # Docs system prompt
β”œβ”€β”€ memory/
β”‚   └── project.md
└── skills/                  # Optional prompt snippets

5. REST Controller

@RestController
public class OrchestratorController {

    private final OrchestratorAgent orchestrator;

    @PostMapping("/feature")
    public String addFeature(@RequestBody String featureRequest) {
        return orchestrator.orchestrate(featureRequest);
    }
}

Operations & Setting up

Local LLM Setup

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Pull models
ollama pull llama3.2:8b      # Orchestrator/Coder
ollama pull mistral:7b       # Reviewer
ollama pull phi3:14b         # Docs

ollama serve

File Loading

Spring Boot loads .md files as system prompts automatically via system-prompt-file.

AGENTS.md becomes orchestrator’s base context

Run It

  1. mvn spring-boot:run
  2. curl -X POST http://localhost:8080/feature -d "Add JWT login"

Comparison

Criteria OpenCode CLI Agents Spring Boot AI Agents
Deployment Terminal only, single user REST API, Docker, Kubernetes, load-balanced
File Editing Native (edit, bash, git) FileSystemTools + ShellTools required
Multi-Agent Manual @agent coordination Native TaskExecutor, parallel execution
Memory/State Session-based, manual files ChatMemory + Redis + database persistent
Local LLM Mixed cloud/local 100% local (Ollama/LM Studio)
Monitoring Terminal logs Actuator, Micrometer, tracing
Scalability Single process Horizontal scaling, clustering
Development Speed 5 minutes setup 2+ hours Spring Boot config
Team Sharing Folder structure in git JAR deployment + config management
CI/CD Integration opencode in GitHub Actions Full Spring Boot pipeline
Your .md Files Native discovery Manual resource loading
Skills/Tools Native SKILL.md files Spring AI tool annotations
Production Use Prototyping, personal Enterprise, teams, services
Learning Curve Low (CLI + markdown) High (Spring AI ecosystem)
Cost Free (OpenCode) Free (open source)
Debugging Session transcripts Logs + debug endpoints
Customization Agent prompts + skills Java code + annotations

πŸ† Winner by Use Case

Use OpenCode if Use Spring Boot if
Personal projects Team/production systems
Quick prototyping REST API needed
Terminal workflow Database integration
Learning AI agents Enterprise deployment
Git-only sharing Monitoring required

🎯 Recommendation

Start with OpenCode for your Spring Boot project:

  • βœ… Zero Java coding
  • βœ… Uses your exact folder structure
  • βœ… Native file editing
  • βœ… Works today

Migrate to Spring Boot when you need:

  • Team API access
  • Production monitoring
  • Database run history
  • Horizontal scaling
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