Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/clade-architecture-variants/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill clade-architecture-variants. Copyright stays with the author (MIT).

Claude Architecture Variants

Overview

Five architecture patterns for Claude-powered applications: Chatbot (stateless API wrapper), RAG (retrieval-augmented generation with vector search), Agent (tool use loop), Content Pipeline (batch processing), and Evaluation (using Claude as a judge). Each includes complete code and a comparison table.

1. Chatbot (Stateless API Wrapper)

Simplest pattern — proxy Claude with a system prompt.

// api/chat.ts
export async function POST(req: Request) {
  const { messages } = await req.json();
  const response = await client.messages.create({
    model: 'claude-sonnet-4-20250514',
    max_tokens: 2048,
    system: 'You are a helpful assistant for our SaaS product.',
    messages,
    stream: true,
  });
  return new Response(response.toReadableStream());
}

Best for: Customer support, Q&A, simple conversational interfaces.

2. RAG (Retrieval-Augmented Generation)

Fetch relevant context, inject into prompt, generate grounded answer.

async function ragQuery(question: string) {
  // 1. Embed the question (use Voyage, OpenAI, or Cohere — not Anthropic)
  const embedding = await embeddingClient.embed(question);

  // 2. Search vector DB for relevant chunks
  const chunks = await vectorDb.query(embedding, { topK: 5 });

  // 3. Send to Claude with context
  const message = await client.messages.create({
    model: 'claude-sonnet-4-20250514',
    max_tokens: 2048,
    system: `Answer based on the provided context. If the context doesn't contain the answer, say so.`,
    messages: [{
      role: 'user',
      content: `Context:\n${chunks.map(c => c.text).join('\n---\n')}\n\nQuestion: ${question}`,
    }],
  });
  return message.content[0].text;
}

Best for: Documentation Q&A, knowledge bases, support with source citations.

3. Agent (Tool Use Loop)

Claude decides which tools to call, you execute them, loop until done.

async function agentLoop(userInput: string, tools: Anthropic.Tool[]) {
  let messages: MessageParam[] = [{ role: 'user', content: userInput }];

  while (true) {
    const response = await client.messages.create({
      model: 'claude-sonnet-4-20250514',
      max_tokens: 4096,
      tools,
      messages,
    });
    messages.push({ role: 'assistant', content: response.content });

    if (response.stop_reason === 'end_turn') {
      return response.content.find(b => b.type === 'text')?.text;
    }

    // Execute tools
    const results = [];
    for (const block of response.content) {
      if (block.type === 'tool_use') {
        const result = await executeTool(block.name, block.input);
        results.push({ type: 'tool_result', tool_use_id: block.id, content: JSON.stringify(result) });
      }
    }
    messages.push({ role: 'user', content: results });
  }
}

Best for: Data analysis, code generation, multi-step workflows.

4. Content Pipeline (Batch Processing)

Process thousands of documents through Claude asynchronously.

const batch = await client.messages.batches.create({
  requests: documents.map((doc, i) => ({
    custom_id: doc.id,
    params: {
      model: 'claude-haiku-4-5-20251001', // Cheap for bulk
      max_tokens: 512,
      messages: [{ role: 'user', content: `Extract entities: ${doc.text}` }],
    },
  })),
});
// 50% cheaper, processes within 24h

Best for: Summarization, classification, extraction at scale.

5. Evaluation / Grading

Use Claude to evaluate other AI outputs or human content.

const evaluation = await client.messages.create({
  model: 'claude-opus-4-20250514', // Best judgment
  max_tokens: 1024,
  system: `You are an expert evaluator. Score the response 1-5 on accuracy, relevance, and completeness. Return JSON: { "accuracy": N, "relevance": N, "completeness": N, "reasoning": "..." }`,
  messages: [{
    role: 'user',
    content: `Question: ${question}\nResponse to evaluate: ${candidateResponse}`,
  }],
});

Best for: AI output quality, content moderation, automated grading.

Choosing a Pattern

Output

• Architecture pattern selected based on requirements
• Implementation code for chosen pattern
• Cost and latency characteristics understood
• Scaling strategy identified (streaming for chatbots, batches for pipelines)

Error Handling

Examples

See five numbered pattern sections with complete TypeScript code, and the Choosing a Pattern comparison table with latency, cost, and complexity ratings.

Resources

• Tool Use Guide
• Prompt Engineering

Next Steps

See clade-known-pitfalls for common mistakes.

Prerequisites

• Completed clade-install-auth and clade-model-inference
• Understanding of your use case requirements (latency, cost, complexity)
• For RAG: vector database and embedding model (Voyage, OpenAI, or Cohere)

Instructions

Step 1: Review the patterns below

Each section contains production-ready code examples. Copy and adapt them to your use case.

Step 2: Apply to your codebase

Integrate the patterns that match your requirements. Test each change individually.

Step 3: Verify

Run your test suite to confirm the integration works correctly.