--- name: clade-architecture-variants-6b04a3d31ddc description: "Build different types of Claude-powered applications \u2014 chatbots,\ \ RAG systems,\nUse when working with architecture-variants patterns.\nagents, content\ \ pipelines, and code generation tools.\nTrigger with \"claude architecture\", \"\ anthropic rag\", \"build with claude\",\n\"claude agent pattern\", \"anthropic app\ \ design\".\n" allowed-tools: Read, Write, Edit version: 1.1.0 license: MIT author: Jeremy Longshore tags: - saas - anthropic - claude - architecture - rag - agents compatibility: Designed for Claude Code --- # 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. ```typescript // 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. ```typescript 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. ```typescript 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. ```typescript 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. ```typescript 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 | Pattern | Latency | Cost | Complexity | |---------|---------|------|------------| | Chatbot | Low (streaming) | Low | Simple | | RAG | Medium (embed + search + generate) | Medium | Medium | | Agent | High (multi-turn) | High | Complex | | Pipeline | High (async batch) | Low (50% off) | Simple | | Evaluation | Medium | Varies | Simple | ## 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 | Error | Cause | Solution | |-------|-------|----------| | API Error | Check error type and status code | See `clade-common-errors` | ## 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](https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview) - [Prompt Engineering](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/overview) ## 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. --- Source attribution: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/claude-pack/skills/clade-architecture-variants/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 Jeremy Longshore Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/claude-pack/skills/clade-architecture-variants/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Product Management Roles: Backend Developer, Frontend Developer, Growth Product Manager, ML / AI Engineer, Product Manager Specialties: API, Database, Design, Word Documents, Frontend, Testing Tools: Anthropic, Saas Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 9d917cd3f982aa0fa84cc8cd9a7838089ec712c30c1ecf0ab4619e5e4a3129a3