AI Agent Design: Dify vs LangChain vs Raw API

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When you decide to build an AI agent, the first question is “what do I use?” I’ve run all three in production. Here’s the honest breakdown.

Dify: No-code/low-code / prototypes / non-engineer teams LangChain: Python / complex chains / OSS ecosystem needed Raw API: Production / full control / Flutter + Supabase integration Enter fullscreen mode Exit fullscreen mode My project landed on raw API (Anthropic SDK + Deno Edge Function).

# Dify workflow design view [Input] → [LLM Node] → [Condition Branch] → [Tool Node] → [Output] Enter fullscreen mode Exit fullscreen mode Dify lets you build flows in a GUI. Its biggest strength: working prototype in 3 days.

✅ Use Dify when: – Non-engineers need to edit workflows – You want to test a RAG pipeline fast – You don’t want to manage hosting/infra ❌ Not Dify when: – You need tight integration with existing code – Custom logic is complex – Dify’s execution cost is climbing Enter fullscreen mode Exit fullscreen mode When LangChain Wins from langchain.agents import initialize_agent, Tool from langchain.chat_models import ChatAnthropic tools = [ Tool(name=”search”, func=search_fn, description=”Web search”), Tool(name=”calculator”, func=calc_fn, description=”Math”), ] agent = initialize_agent(tools, ChatAnthropic(model=”claude-haiku-4-5″), …) result = agent.run(“What’s the weather in Tokyo today?”) Enter fullscreen mode Exit fullscreen mode LangChain’s Python ecosystem is powerful. Rich integrations for Vector Store / Retriever / Memory.

✅ Use LangChain when: – Building a serious RAG pipeline – Need to swap between multiple LLMs – Python-based data pipelines already exist ❌ Not LangChain when: – Flutter/Dart/Deno is your main stack — bindings are thin – Simple API calls — overhead is disproportionate Enter fullscreen mode Exit fullscreen mode When Raw API Wins (My Choice) // Deno Edge Function implementation const response = await fetch(‘https://api.anthropic.com/v1/messages’, { method: ‘POST’, headers: { ‘x-api-key’: Deno.env.get(‘ANTHROPIC_API_KEY’)!, ‘anthropic-version’: ‘2023-06-01’, ‘content-type’: ‘application/json’, }, body: JSON.stringify({ model: ‘claude-haiku-4-5-20251001’, max_tokens: 1024, messages: [{ role: ‘user’, content: userMessage }], }), }); const data = await response.json(); return data.content[0].text; Enter fullscreen mode Exit fullscreen mode Why I chose raw API:

Want to build an AI agent? ↓ Non-engineers editing workflows? Yes → Dify No ↓ Python is your main stack? Yes → LangChain No ↓ Tight integration with existing stack needed? Yes → Raw API No → Dify (as prototype) Enter fullscreen mode Exit fullscreen mode Tool Use (Function Calling) Pattern Using Tool Use with raw API:

const tools = [ { name: “get_race_data”, description: “Fetch horse racing data”, input_schema: { type: “object”, properties: { race_id: { type: “string”, description: “Race ID” }, date: { type: “string”, description: “Date in YYYY-MM-DD” }, }, required: [“race_id”], }, }, ]; const response = await fetch(‘https://api.anthropic.com/v1/messages’, { method: ‘POST’, headers: { ‘x-api-key’: API_KEY, ‘anthropic-version’: ‘2023-06-01’, ‘content-type’: ‘application/json’ }, body: JSON.stringify({ model: ‘claude-sonnet-4-6’, max_tokens: 2048, tools, messages: [{ role: ‘user’, content: ‘Predict tomorrow\’s Nakayama races’ }], }), }); Enter fullscreen mode Exit fullscreen mode When Claude decides it should call get_race_data, it returns a tool_use block. Your code handles the dispatch.

function selectModel(taskType: string): string { switch (taskType) { case ‘simple_qa’: return ‘claude-haiku-4-5-20251001’; // $0.00025/1K case ‘analysis’: return ‘claude-sonnet-4-6’; // $0.003/

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