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:
| Benefit | Why It Helps Beginners |
|---|---|
| Visual Workflow Builder | Drag-and-drop nodes — no coding needed |
| Built-in LLM Support | Use GPT, Qwen, etc. out of the box |
| Custom Tools | Connect to APIs, databases, web services |
| Full in Chinese & English | Easy for global users |
| Open-source & Self-hostable | Flexible 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:
[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)
↓
[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:
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
| Field | Value |
|---|---|
| Name | get_weather |
| Description | Get weather for a city |
| Parameters | Use this JSON Schema |
{
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "City name, e.g. Shanghai"
}
},
"required": ["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['main']['temp'] - 273.15:.1f}°C",
"condition": response['weather'][0]['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": "{{intent.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:
The user didn’t ask about weather. Just reply politely:
{{input}}▶️ Step 4: Test It!
Input:
Will it rain in Hangzhou tomorrow? If yes, remind me.Expected Output:
It will rain in Hangzhou tomorrow. Don’t forget your umbrella!🎉 Success! Your first AI Agent is live.
📈 Level Up: Make Your Agent Smarter
| Feature | How to Add |
|---|---|
| Remember past chats | Enable session context in Dify |
| Plan a trip | Add a “task planner” LLM node to break goals into steps |
| Book hotels | Add a booking API as a tool |
| Multi-step loops | Use 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):
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": [
{
"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
| Resource | Link |
|---|---|
| Dify Official Docs | https://docs.dify.ai |
| YouTube: “Build AI Agents with Dify” | Search on YouTube |
| Dify Community (Discord/WeChat) | Join for help and templates |
🧭 Learning Path for Beginners
| Week | Goal |
|---|---|
| Week 1 | Build a Q&A bot with Dify |
| Week 2 | Add one tool (e.g. weather, search) |
| Week 3 | Create a decision-making agent |
| Week 4 | Build a real-world agent (e.g. travel planner, daily report generator) |
🎉 Summary: How Beginners Can Succeed
| Tip | Explanation |
|---|---|
| 🧱 Think in Blocks | Each node is a step: Understand → Decide → Act → Reply |
| 🤖 LLM = Brain | Use it for understanding and reasoning |
| 🔌 Tools = Hands | They do the real work (APIs, search, etc.) |
| 🖼️ Visual = Code | No coding needed — just drag and connect |
| 🔄 Test Early, Iterate Fast | Add 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.