1. Architecture Selection and Core Components

1.1 Architecture Comparison

Two primary architectures exist in AI programming tools:

• Full-featured IDE integration (e.g., mainstream AI development platforms)
• Lightweight terminal-based assistants (focus of this guide)

Key advantages of lightweight solutions:

• <200MB memory footprint
• Cross-platform compatibility (Linux/macOS/Windows)
• Modular plugin architecture

Core components:

graph TD
    A[Terminal Interface] --> B[Code Parser]
    B --> C[AST Analyzer]
    C --> D[Agent Orchestrator]
    D --> E[Multi-agent Network]

1.2 Typical Workflow

graph TD
    A[Requirement Analysis] --> B[Code Generation]
    B --> C{Quality Check}
    C -->|Pass| D[Commit to Repo]
    C -->|Fail| E[Trigger Repair Agent]
    E --> B

Ideal for:

• Rapid prototyping
• Code standardization
• Unit test generation
• Legacy system modernization

2. Environment Setup Guide

2.1 System Requirements

Component	Minimum	Recommended
OS	Linux 4.4+	Ubuntu 20.04+
RAM	2GB	4GB+
Storage	500MB	2GB+
Node.js	v16.x	v18.x

2.2 Dependency Installation

# Node version management (recommended)
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.5/install.sh | bash
nvm install 18
# Core dependencies
npm install -g typescript ts-node

2.3 Configuration Files

1. Repository integration:

   # .opencode/config.yml
   repositories:
   - name: project_alpha
    type: git
    url: git@github.com:team/repo.git
    branch: main

2. Language server setup:

   {
   "languages": {
    "python": {
      "lsp": "pylsp",
      "lint": ["flake8", "mypy"]
    },
    "javascript": {
      "lsp": "typescript-language-server",
      "format": "prettier"
    }
   }
   }

3. Advanced Agent Configuration

3.1 Agent Types and Triggers

Agent	Function	Activation Condition
Sisyphus	Continuous code quality optimization	Code commit/Scheduled task
Hephaestus	Automated test generation	Feature completion
Oracle	Architecture decision support	Complex module design
Prometheus	Performance monitoring	Benchmark execution

3.2 Multi-agent Orchestration

class AgentOrchestrator:
    def __init__(self):
        self.agents = {
            'sisyphus': CodeOptimizer(),
            'hephaestus': TestGenerator(),
            'oracle': ArchitectureAdvisor()
        }
    async def execute_pipeline(self, context):
        tasks = []
        if context['event'] == 'code_commit':
            tasks.extend([
                self.agents['sisyphus'].run(context),
                self.agents['hephaestus'].run(context)
            ])
        if context['complexity'] > 8:
            tasks.append(self.agents['oracle'].run(context))
        await asyncio.gather(*tasks)

3.3 Resource Management

1. Dynamic allocation:
   ```yaml

   agent_resource.yml

   resources:
   default:
   cpu: 0.5
   memory: 512MB
   high_priority:
   cpu: 1.0
   memory: 1024MB

agent_mapping:
oracle: high_priority
sisyphus: default

2. Concurrency control:
```javascript
class ConcurrencyController {
  constructor(maxConcurrent = 3) {
    this.queue = [];
    this.active = 0;
    this.max = maxConcurrent;
  }
  async execute(task) {
    if (this.active >= this.max) {
      await new Promise(resolve => this.queue.push(resolve));
    }
    this.active++;
    try {
      return await task();
    } finally {
      this.active--;
      if (this.queue.length) {
        this.queue.shift()();
      }
    }
  }
}

4. Custom Development Guide

4.1 Agent Template Structure

my_agent/
├── src/
│   ├── main.ts          # Entry point
│   ├── handler.ts       # Core logic
│   └── config.ts        # Configuration
├── tests/               # Unit tests
└── agent.yml            # Metadata

4.2 Key Interfaces

interface AgentContext {
  code: string;
  language: string;
  dependencies: string[];
}
interface AgentHandler {
  execute(context: AgentContext): Promise<AgentResult>;
  validateConfig(): boolean;
}

5. CI/CD Integration

5.1 Pipeline Configuration

pipeline {
    agent any
    stages {
        stage('Code Analysis') {
            steps {
                sh 'opencode analyze --agent sisyphus --severity high'
            }
        }
        stage('Test Generation') {
            steps {
                sh 'opencode test --agent hephaestus --coverage 80'
            }
        }
    }
}

5.2 Quality Gates

{
  "gate_rules": [
    {
      "agent": "sisyphus",
      "metrics": {
        "complexity": {"max": 10},
        "duplication": {"max": 5}
      },
      "block_merge": true
    }
  ]
}

6. Performance Optimization

6.1 Common Bottlenecks

1. AST parsing optimization:

   enable_ast_cache: true
   max_ast_depth: 15

2. Agent communication:

   communication:
   protocol: grpc
   batch_size: 100
   timeout: 5000

6.2 Monitoring Tools

1. Log structure:

   /var/log/opencode/
   ├── agent.log          # Agent operations
   ├── orchestrator.log   # Scheduler logs
   └── performance.log    # Metrics

2. Metrics definition:

   const metrics = [
   { name: 'agent_latency', type: 'histogram', help: 'Agent response time' },
   { name: 'error_rate', type: 'gauge', help: 'Error percentage' }
   ];

Best Practices Summary

1. Adoption Strategy:

   • Start with core code generation
   • Gradually add quality agents
   • Finalize with test automation

2. Resource Allocation:

   • Development: 2C/4GB
   • Production: 4C/8GB (recommended)
   • Scale horizontally for high concurrency

3. Security Recommendations:

   • Implement repository access controls
   • Regularly audit agent permissions
   • Require confirmation for sensitive operations

This modular architecture supports 20+ concurrent developers on a 4C/8GB server with 87% code generation accuracy and 40% improved quality coverage. For enterprise deployments, consider containerized clustering solutions.