How to Build an AI Agent with Dify

Here’s the English version of the beginner-friendly, highly practical guide to building an Agent using Dify — designed for non-technical users, with a clear, visual, and step-by-step approach.

🤖 How to Build an AI Agent with Dify (For Absolute Beginners)

A visual, no-code guide to creating smart agents that think, decide, and act — even if you’re not a developer.

🎯 What Is an AI Agent?

An AI Agent is more than a chatbot. It can:

Understand your goal
Break it into steps
Use tools (like search, APIs)
Make decisions
Take action
Return a complete result

Example: You say, “Will it rain in Shanghai tomorrow? Remind me to bring an umbrella if so.”The agent figures out what to do, checks the weather, and gives you a smart reply.

✅ Why Use Dify?

Dify is one of the best platforms for beginners to build AI agents because:

BenefitWhy It Helps BeginnersVisual Workflow BuilderDrag-and-drop nodes — no coding neededBuilt-in LLM SupportUse GPT, Qwen, etc. out of the boxCustom ToolsConnect to APIs, databases, web servicesFull in Chinese & EnglishEasy for global usersOpen-source & Self-hostableFlexible and secure

✅ Dify turns complex agent logic into simple visual blocks.

🚀 Step-by-Step: Build a “Weather Reminder Agent”

We’ll create an agent that:

Understands if you want weather info

Checks the weather

Decides whether to remind you

Replies naturally

No code. Just drag, click, and test.

🧱 Step 1: Create a Workflow App

Go to Dify.ai → Log in

Click “Create Application”

Choose “Workflow” mode

🔧 This is where you build your agent’s “brain”.

🧩 Step 2: Design the Workflow (5 Simple Nodes)

Here’s the flow:

&#91;User Input]
     ↓
🟢 Node 1: Intent Detection (LLM) — What does the user want?
     ↓
🟡 Node 2: Condition — Should we check weather?
     ↓ Yes                 ↓ No
🔵 Node 3: Tool Call       🔵 Node 4: Simple Reply
     ↓
🟢 Node 5: Final Response (with reminder logic)
     ↓
&#91;Output to User]

Let’s configure each node.

🔧 Step 3: Configure Each Node

🟢 Node 1: Intent Detection (LLM Node)

Purpose: Extract whether the user wants weather info and which city.

Settings:

Type: LLM
Model: GPT-3.5 / Qwen / etc.
Prompt (copy-paste this):

You are a task analyzer. Analyze the user input and decide if weather check is needed.

User input: {{input}}

Return JSON format:
{
  "need_check": true or false,
  "city": "city name, e.g. Beijing"
}

✅ Enable Structured Output → Format: JSON📌 Save output as variable: intent

🟡 Node 2: Condition Branch

Purpose: Decide which path to take.

Rule:

1	`intent.need_check == true`

If true → go to weather tool
If false → go to simple reply

🔵 Node 3: Tool Call — Get Weather

🛠️ First: Create a Custom Tool

Go to: Application Settings → Tools → Create Tool

FieldValueNameget_weatherDescriptionGet weather for a cityParametersUse this JSON Schema

{
  "type": "object",
  "properties": {
    "city": {
      "type": "string",
      "description": "City name, e.g. Shanghai"
    }
  },
  "required": &#91;"city"]
}

📌 After saving, Dify gives you a Webhook URL — you’ll use this.

🌐 Build the Weather Backend (Beginner-Friendly)

You need a small service to return real weather data.

✅ Option 1: Use a Free Weather API

Example with OpenWeatherMap:

Sign up (free tier)
Build a simple FastAPI/Flask app that calls their API

✅ Option 2: Use a Ready-Made Template

We’ve prepared a simple FastAPI weather tool:

from fastapi import FastAPI
import requests

app = FastAPI()

@app.post("/weather")
def get_weather(data: dict):
    city = data.get("city")
    api_key = "YOUR_OPENWEATHER_KEY"
    url = f"http://api.openweathermap.org/data/2.5/weather?q={city}&appid={api_key}"
    response = requests.get(url).json()
    return {
        "temp": f"{response&#91;'main']&#91;'temp'] - 273.15:.1f}°C",
        "condition": response&#91;'weather']&#91;0]&#91;'description']
    }

Deploy it on:

Vercel / Render / Railway (free)
Or use Alibaba Cloud Function Compute

Then set the webhook URL in Dify.

🔧 Back in Dify: Call the Tool

Type: Tool
Tool: get_weather
Parameters: {“city”: ““}
Save result as: weather_info

🟢 Node 5: Generate Final Reply (LLM Node)

Prompt:

You are a helpful assistant. Based on the weather info, decide if a reminder is needed.

Weather info:
{{weather_info}}

Reply in natural language. If it's raining, remind the user to bring an umbrella.

This is your agent’s final answer.

🔵 Node 4: Simple Reply (for non-weather queries)

Prompt:

1 2	`The user didn’t ask about weather. Just reply politely: {{input}}`

▶️ Step 4: Test It!

Input:

1	`Will it rain in Hangzhou tomorrow? If yes, remind me.`

Expected Output:

1	`It will rain in Hangzhou tomorrow. Don’t forget your umbrella!`

🎉 Success! Your first AI Agent is live.

📈 Level Up: Make Your Agent Smarter

FeatureHow to AddRemember past chatsEnable session context in DifyPlan a tripAdd a “task planner” LLM node to break goals into stepsBook hotelsAdd a booking API as a toolMulti-step loopsUse parallel or retry nodes (Pro feature)

🧰 Starter Kit for Beginners

🎁 1. Ready-to-Use Weather Webhook (Test Only)

We provide a demo endpoint (for testing):

1
2
3

POST https://demo-agent-tools.example.com/weather
Body: {"city": "Beijing"}
→ Returns: {"temp": "22°C", "condition": "Sunny"}

🔒 For real use, deploy your own for security.

🧩 2. Exportable Workflow Template (JSON)

{
  "nodes": &#91;
    {
      "id": "intent",
      "type": "llm",
      "config": {
        "prompt": "You are a task analyzer...\nReturn JSON..."
      },
      "output_var": "intent"
    },
    {
      "id": "condition",
      "type": "condition",
      "expression": "intent.need_check == true"
    },
    {
      "id": "tool_weather",
      "type": "tool",
      "tool": "get_weather",
      "params": {"city": "{{intent.city}}"},
      "output_var": "weather_info"
    },
    {
      "id": "final_reply",
      "type": "llm",
      "config": {
        "prompt": "Based on {{weather_info}}, generate a reply..."
      }
    }
  ]
}

You can import this structure into Dify (if supported).

📘 3. Learning Resources

ResourceLinkDify Official Docshttps://docs.dify.aiYouTube: “Build AI Agents with Dify”Search on YouTubeDify Community (Discord/WeChat)Join for help and templates

🧭 Learning Path for Beginners

WeekGoalWeek 1Build a Q&A bot with DifyWeek 2Add one tool (e.g. weather, search)Week 3Create a decision-making agentWeek 4Build a real-world agent (e.g. travel planner, daily report generator)

🎉 Summary: How Beginners Can Succeed

TipExplanation🧱 Think in BlocksEach node is a step: Understand → Decide → Act → Reply🤖 LLM = BrainUse it for understanding and reasoning🔌 Tools = HandsThey do the real work (APIs, search, etc.)🖼️ Visual = CodeNo coding needed — just drag and connect🔄 Test Early, Iterate FastAdd one feature at a time

❓ FAQ

Q: I’m not a developer. Can I really do this?A: Yes! If you can use a mouse and understand logic, you can build agents.

Q: Do I need to code the tools?A: Not always. Use free APIs (like weather, translation). Only complex tools need coding.

Q: Can it remember past conversations?A: Yes! Enable session context in Dify settings.

Q: Can I connect to Slack, WeChat, or DingTalk?A: Yes! Dify supports API integration and webhooks.

📎 Next Steps

Want me to:

Generate a full exportable workflow file?
Provide a Docker-ready weather tool?
Help you build a custom agent (e.g. sales assistant, customer support)?

Just ask! I’ll guide you step by step. 🚀

🎯 Start now: Log in to Dify → Create a Workflow → Drag an LLM Node → Try it!Your first AI agent is just minutes away.

https://www.calcguide.tech/2025/08/28/how-to-build-an-ai-agent-with-dify/

如何基于 Dify 平台开发 Agent智能代理 - LinuxGuide 如何基于 Dify 平台开发 Agent智能代理如何基于 Dify 平台开发 Agent智能代理,如何基于Dify平台,开发,如何基于 Dify 平台开发 Agent,Dify 平台 Agent 开发教程,Dify 如何创建智能代理,Agent 智能代理开发指南,Dify 平台低代码开发 Agent,小白也能学的 Agent 开发,Dify 平台智能代理教程,如何快速开发智能代理,Dify Agent 开发入门指南,智能代理开发步骤详解LinuxGuide