15.2 Platform Deep Dive

In the previous section, we surveyed the AI orchestration platform landscape. Now it's time to roll up our sleeves, dive into each platform's engine room, and see how they actually work and which is most suitable for you.

Dify: Open-Source LLMOps Champion

Genetic Profile

Origin: Born in China in 2023, founding team deeply understands Chinese developers' pain points
Positioning: Open-source LLM application development platform, benchmarks OpenAI's GPT Builder (but more powerful)
Slogan: "Build AI apps in minutes, not months"
GitHub Stars: 50,000+ (2026 data)

Core Capabilities Breakdown

Dify isn't simply a "chatbot builder"—it's a complete LLMOps platform covering the entire lifecycle from development, testing to deployment, and monitoring.

1. Three Application Types

Chat Assistant

ChatGPT-like conversational interface
Supports multi-turn conversation, context memory
Can inject knowledge base, tool calling

User: Help me analyze this month's sales data
AI: [Call database tool] Okay, I see this month's sales are...
    [Generate chart] Here's the trend chart...
    Need me to analyze any region in depth?

Text Generator

Single input-output, no context
Suitable for content generation, translation, summarization tasks
Can batch process

Input: [Upload 100 articles]
Output: [Auto-generate 100 SEO-friendly titles]

AI Workflow

Complex multi-step orchestration
Supports conditional branches, loops, parallel execution
This is Dify's most powerful feature

[Receive customer email]
    ↓
[AI analyze sentiment] → If negative sentiment → [Mark as priority 1]
    ↓                                           ↓
[Extract key information]                    [Notify supervisor]
    ↓
[Retrieve history]
    ↓
[AI generate reply draft]
    ↓
[Send to review queue]

2. Knowledge Base System

Dify's knowledge base isn't simply "upload documents"—it's a professional-grade RAG engine:

Document Processing:

Supported formats: PDF, Word, Markdown, TXT, HTML, XLSX, CSV
Auto-parsing: Table recognition, image OCR, web scraping
Incremental updates: Auto-sync after document modifications

Chunking Strategy:

Auto mode: AI intelligent chunking (recognize paragraphs, sections)
Fixed length: Split by character count (can set overlap)
Custom: Manual control with separators

Retrieval Algorithms:

Vector retrieval: Semantic similarity (default)
Full-text retrieval: Keyword matching
Hybrid retrieval: Combine both, reranking

Embedding Model Selection:

OpenAI text-embedding-3-large (recommended, best quality)
OpenAI text-embedding-ada-002 (cost-effective)
Local models: BGE, M3E, GTE (free, requires self-deployment)

3. Prompt Orchestration

Dify's Prompt editor is an exemplary design both programmers and product managers can use:

Variable System:

jinja-raw

You are a \{\{role}}, your task is \{\{task}}.

User information:
- Name: \{\{user.name}}
- Purchase history: \{\{user.purchase_history}}

Based on the above information, \{\{instruction}}

Built-in Variables:

\{\{query}}: User input
\{\{context}}: Knowledge base retrieval results
\{\{history}}: Conversation history
\{\{sys.date}}: Current date
\{\{sys.time}}: Current time

Prompt Debugging:

Real-time preview: See actual Prompt after variable substitution
A/B testing: Compare different Prompt versions
History: Save effects of each iteration

4. Tool Calling (Function Calling)

Dify wraps OpenAI's Function Calling into visual configuration:

Built-in Tools:

Google Search
Wikipedia queries
Weather queries
DuckDuckGo Search

Custom Tools:

yaml

# Example: Query inventory
Tool name: check_inventory
Description: Query product inventory quantity
Parameters:
  - product_id (required): Product ID
  - warehouse (optional): Warehouse location
API endpoint: https://api.yourcompany.com/inventory
Method: POST
Authentication: Bearer Token

Tool Orchestration:

User: "Help me check tomorrow's Beijing weather, if it rains recommend indoor activities"

AI execution process:
1. Call weather tool → Get tomorrow's Beijing weather
2. Judge: If weather.condition == "rain"
3. Call search tool → Search "Beijing indoor activity recommendations"
4. Synthesize results to generate reply

Deployment Methods

Docker One-Click Deployment (Recommended):

bash

git clone https://github.com/langgenius/dify.git
cd dify/docker
docker-compose up -d

# Access http://localhost:3000
# Default account: admin@example.com
# Default password: Check logs

Cloud Service (Official Hosting):

Free tier: 200 calls/day
Paid version: Starting at $29/month (unlimited calls, more models)
Enterprise version: Private deployment + technical support

Resource Requirements:

Minimum configuration:
- CPU: 2 cores
- Memory: 4GB
- Storage: 20GB

Recommended configuration (production):
- CPU: 4+ cores
- Memory: 8GB+
- Storage: 100GB+
- Plus vector database (Qdrant/Weaviate)

Suitable Scenarios

Best suited for:

Small to medium teams rapidly building AI products
Enterprises requiring private deployment
Those wanting open-source free but full-featured solutions
Chinese users (documentation, community all very friendly)

Not suited for:

Large-scale applications requiring extreme performance optimization (recommend code optimization)
Complex multi-Agent collaboration (can do but not strong suit)
Highly customized requirements (may need to modify source code)

Coze: ByteDance's AI LEGO

Genetic Profile

Origin: Launched by ByteDance in 2024, backed by AI technology accumulated from TikTok and Toutiao
Positioning: Next-generation Bot building platform, benchmarks GPTs but emphasizes enterprise capabilities
Slogan: "No code, just logic"
Access: https://www.coze.com

Core Capabilities Breakdown

1. Bot Building

Coze's core is Bot (robot), not a simple chat window, but an intelligent agent with skills:

Persona:

Name: Customer Service Xiaomei
Role: Professional e-commerce customer service
Personality: Enthusiastic, patient, professional
Language style: Warm and natural, uses emoji
Expertise: Order queries, returns/exchanges, product consultation

Skill System:

Plugins: Pre-built capability modules (search, translation, image generation)
Workflows: Custom complex logic
Knowledge: Proprietary document data
Database: Structured data storage

Conversation Strategy:

Opening: "Hello! I'm Customer Service Xiaomei, how can I help you?"
Guidance: "You can ask me about orders, logistics, returns"
Fallback: "Sorry, I'm not quite clear on this question, still learning..."
Transfer to human: When complex issues or negative emotions detected, guide to human transfer

2. Plugin Marketplace

Coze's strongest feature is plugin ecosystem, similar to ChatGPT's GPT Store but more open:

Official Plugins:

Web Search (Google, Bing)
Image Generation (DALL-E, Midjourney API)
Video Analysis (extract subtitles, summarize content)
Code Execution (Python sandbox)
OCR Recognition

Community Plugins:

Express Tracking (SF Express, JD)
Weather Forecast (Amap)
Stock Quotes (Sina Finance)
Music Search (NetEase Cloud Music)

Custom Plugins:

javascript

// Coze plugins are standard HTTP APIs
// Just define OpenAPI specification

{
  "openapi": "3.0.0",
  "info": {
    "title": "Customer Data Query",
    "version": "1.0.0"
  },
  "paths": {
    "/customer/{id}": {
      "get": {
        "summary": "Get customer information",
        "parameters": [
          {
            "name": "id",
            "in": "path",
            "required": true,
            "schema": { "type": "string" }
          }
        ]
      }
    }
  }
}

3. Workflow Engine

Coze's workflows are similar to Dify but more focused on business logic orchestration:

Node Types:

LLM Node: Call different LLMs (GPT-4, Claude, Doubao)
Plugin Node: Call plugin capabilities
Knowledge Base Node: Retrieve documents
Code Node: Execute custom JavaScript code
Condition Node: if-else logic
Loop Node: Batch processing
Parallel Node: Execute multiple tasks simultaneously

Real Case: Intelligent Recruitment Assistant

[Receive Resume (PDF)]
    ↓
[OCR Node: Extract text]
    ↓
[LLM Node: Structured parsing]
    Output: { "Name": "Zhang San", "Education": "Bachelor", "Years": 5 }
    ↓
[Condition Node: Years >= 3?]
    ├─ Yes → [Knowledge Base Retrieval: Match suitable positions]
    │         ↓
    │       [LLM Node: Generate interview invitation]
    │         ↓
    │       [Email Plugin: Send invitation]
    └─ No → [Database: Store in talent pool]
              ↓
            [End: Mark as "potential candidate"]

4. Multi-Platform Publishing

A major highlight of Coze is build once, publish everywhere:

Supported Platforms:

Web pages (generate embed code)
WeChat Official Account (official integration)
Feishu (ByteDance's own ecosystem)
Slack
Discord
Telegram
Custom API (integrate any platform)

Publishing Process:

1. Complete Bot building on Coze platform
2. Click "Publish"
3. Select target platform (e.g., WeChat Official Account)
4. Scan code to authorize
5. Auto-configure Webhook
6. Test → Launch ✅

Users send messages on WeChat, behind the scenes it's your Coze Bot responding, no backend code needed.

Pricing

Free version: 100 conversations/day, basic plugins
Pro version: ¥99/month, unlimited conversations, all plugins, priority models
Team version: ¥999/month, multi-person collaboration, data analysis, API calls
Enterprise version: Custom pricing, private deployment, SLA guarantee

Suitable Scenarios

Best suited for:

Enterprise customer service bots (WeChat, Feishu integration convenient)
Content creation assistants (rich plugins)
Marketing automation (multi-platform social media publishing)
Developers in ByteDance product ecosystem

Not suited for:

Need open-source/self-deployment (Coze is cloud service)
Complex data processing flows (workflow capabilities limited)
Non-China markets (mainly for domestic)

n8n + AI: Old Automation's AI Evolution

Genetic Profile

Origin: Born in Germany in 2019, originally an automation workflow tool
Positioning: Fair-code (source visible, commercial use charged) automation platform
Transformation: 2024 heavily embraced AI, added many AI nodes
GitHub Stars: 45,000+

Why is n8n Special?

If Dify and Coze are "born for AI," then n8n is "born for connection." Its core advantage is: connect anything.

Integration Count: 400+ applications (Google, Slack, GitHub, Notion, databases...)
Triggers: Scheduled, Webhook, email, file monitoring, database changes...
AI Capabilities: Icing on the cake, not the core

AI Nodes Detailed

1. AI Agent Node

n8n's Agent is AI that can use tools:

[Trigger: Webhook receives user request]
    ↓
[AI Agent Node]
  - LLM: GPT-4
  - Tools:
    * Google Search
    * Calculator
    * Database queries
    * Send email
  - Strategy: ReAct (Reason + Act)
    ↓
[Return result]

Actual Execution:

User: "Help me check Tesla's stock price, if it went up send me an email"

AI Agent thinking process:
1. [Call tool: Stock query] → Tesla $250, +3% gain
2. [Reasoning] → Stock went up, need to send email
3. [Call tool: Send email] → Send to user@example.com
4. [Return] → "Sent email notifying you Tesla stock went up"

2. AI Langchain Node

Integrates core capabilities of LangChain:

Vector Storage: Pinecone, Qdrant, Weaviate
Embedding: OpenAI, Cohere, HuggingFace
Retrievers: Similarity search, MMR, compression retrieval
Chain Types: Stuff, Map-Reduce, Refine

RAG Workflow:

[Trigger: New document uploaded to Google Drive]
    ↓
[Google Drive Node: Download file]
    ↓
[Document Loader: Parse PDF]
    ↓
[Text Chunking: Recursive Character Splitter]
    ↓
[OpenAI Embeddings: Vectorize]
    ↓
[Pinecone Node: Store vectors]
    ↓
[Slack Notification: "Document indexing complete"]

3. AI Chat Node

Simple conversation interface:

[Trigger: Telegram receives message]
    ↓
[AI Chat Node]
  - Model: GPT-4
  - System Prompt: "You are a friendly assistant"
  - Context memory: Keep last 10 conversations
    ↓
[Telegram Node: Send reply]

n8n's Killer Scenarios

Scenario 1: Automated Content Pipeline

[RSS Subscription: Fetch industry news]
    ↓
[AI Node: Filter relevant articles (spam filtering)]
    ↓
[AI Node: Generate summary]
    ↓
[AI Node: Rewrite in Twitter style]
    ↓
[Condition: Quality score > 8?]
    ├─ Yes → [Twitter API: Auto-publish]
    │         ↓
    │       [Notion: Record published]
    └─ No → [Slack: Send to review channel]

Scenario 2: Intelligent Customer Onboarding Process

[Trigger: CRM new customer]
    ↓
[AI Analysis: Customer industry, size, needs]
    ↓
[Conditional Branch: Customer type]
    ├─ Enterprise → [Send enterprise materials] → [Assign senior account manager]
    ├─ SMB → [Send standard materials] → [Auto-schedule Demo]
    └─ Individual → [Send quick guide] → [Add to user community]
        ↓
[Email Node: Send welcome email (AI generates personalized content)]
    ↓
[Slack: Notify team]
    ↓
[Google Sheets: Update customer report]

Deployment

Docker Self-Deployment:

bash

docker run -it --rm \
  --name n8n \
  -p 5678:5678 \
  -v ~/.n8n:/home/node/.n8n \
  n8nio/n8n

Cloud Service:

n8n.cloud: Starting at $20/month (hosted version)
Self-deployment: Free (production environment recommends commercial license)

Suitable Scenarios

Best suited for:

Marketing automation (multi-platform content distribution)
Data sync (between CRM, ERP, analytics tools)
Personal productivity tools (automate daily repetitive tasks)
Scenarios needing to connect many SaaS tools

Not suited for:

Pure AI conversation applications (use Dify/Coze more suitable)
Complex AI logic (workflows become very complex)
Non-technical pure business people (has some learning curve)

Zapier AI: Automation Giant's AI Ambition

Genetic Profile

Origin: Founded in 2011, veteran in automation field
Users: 2 million+, connecting 6000+ applications
AI Transformation: Launched Zapier AI in 2023, large-scale adoption in 2024

AI Features

1. AI Actions

Insert AI capabilities into Zapier workflows:

AI Text Generator: Generate text, rewrite, translate
AI Classifier: Classify, tag, sentiment analysis
AI Extractor: Extract information from unstructured text
AI Chatbot: Embeddable website chatbot

Example: Email Auto-Classification

[Trigger: Gmail receives new email]
    ↓
[AI Classifier: Classify email type]
    Output: "Complaint"
    ↓
[Condition: If complaint]
    ↓
[AI Text Generator: Generate apology template]
    ↓
[Gmail: Send draft for review]
    ↓
[Slack: Notify customer service supervisor]

2. Zapier Chatbots

Build your own AI assistant with Zapier:

Connect your data sources (Google Drive, Notion, databases)
Auto-train AI (based on document content)
One-click embed in website (generate code snippet)

Value: Non-technical people can also build enterprise knowledge base Q&A systems.

Pricing

Free version: 100 tasks/month, basic integrations
Starter: $29.99/month, 750 tasks/month
Professional: $73.50/month, 2000 tasks/month, AI Actions
Team: $103.50/month, team collaboration, advanced AI
Enterprise: Custom pricing

Note: Zapier is relatively expensive, for high-frequency tasks, n8n self-deployment might be more cost-effective.

Suitable Scenarios

Best suited for:

Non-technical teams (easiest to use)
Connect mainstream foreign SaaS (most complete integrations)
SMBs for rapid automation (no IT team needed)

Not suited for:

High-frequency tasks (high cost)
Need complex AI logic (limited capabilities)
Need private deployment (cloud service only)

FlowiseAI: LangChain's Visual Frontend

Genetic Profile

Origin: Open-sourced in 2023, focused on LangChain visualization
Positioning: LangChain for developers who don't want to write code
GitHub Stars: 30,000+

Core Features

1. Nodes Correspond to LangChain Components

Each drag node directly corresponds to LangChain's Python/JS classes:

Document Loaders → langchain.document_loaders
Text Splitters → langchain.text_splitter
Embeddings → langchain.embeddings
Vector Stores → langchain.vectorstores
Retrievers → langchain.retrievers
LLMs → langchain.llms
Chains → langchain.chains
Agents → langchain.agents

Value: If you know LangChain, getting started with FlowiseAI has zero learning curve. If you don't, using FlowiseAI is the best way to learn LangChain.

2. Fine Parameter Control

Unlike Dify's "out-of-the-box," FlowiseAI exposes all underlying parameters:

Text Splitter Node:
- Chunk Size: 1000 (adjustable)
- Chunk Overlap: 200 (adjustable)
- Separator: "\n\n" (customizable)
- Length Function: len (can choose tiktoken)

This is a double-edged sword: flexible but complex.

3. Local First

FlowiseAI emphasizes local deployment and privacy:

Supports Ollama (local large models)
Supports local vector databases (Chroma, Qdrant)
Data doesn't leave your server

Suitable Scenarios

Best suited for:

AI engineers for rapid prototyping
Developers learning LangChain
Scenarios requiring fine control of RAG pipeline
Privacy-focused enterprises (local deployment)

Not suited for:

Non-technical people (too complex)
Simple chatbots (overkill)
Production environment (stability not as good as code)

Platform Comparison Table

Dimension	Dify	Coze	n8n	Zapier	FlowiseAI
Open-Source	✅ MIT	❌ Closed	✅ Fair-code	❌ Closed	✅ Apache 2.0
Self-Deploy	✅	❌	✅	❌	✅
Chinese Support	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐	⭐⭐⭐
Learning Curve	Low	Low	Medium	Low	Medium-High
AI Capability	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐
Integration Count	50+	100+ (plugins)	400+	6000+	100+
Free Tier	200 calls/day	100 calls/day	Unlimited (self-deploy)	100 tasks/month	Unlimited (self-deploy)
Paid Starting Price	$29/month	¥99/month	$20/month	$29.99/month	Free (open-source)
Community Activity	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
Production Ready	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐

Selection Suggestion Matrix

Your Situation	Recommended Platform	Reasoning
Chinese startup team, rapid MVP	Dify	Open-source free, Chinese-friendly, full-featured
Enterprise customer service bot (WeChat/Feishu)	Coze	Multi-platform publishing convenient, ByteDance ecosystem
Marketing automation, connect multiple SaaS	n8n or Zapier	Rich integrations, mature workflows
AI engineer, need low-level control	FlowiseAI	Expose all parameters, correspond to LangChain
Personal project, zero budget	Dify or n8n	Open-source self-deploy, completely free
Large enterprise, need private deployment	Dify or n8n	Support local deployment, data security
Non-technical team, easiest to use	Coze or Zapier	Most friendly interface, detailed documentation

Real Case: Build Customer Service Bot with Dify

Let's use Dify to build an e-commerce customer service bot from scratch, entirely without code.

Requirements

Can answer order queries, logistics tracking, return/exchange policies
Can call backend API to query real-time data
Transfer to human when negative emotion detected
Support multi-turn conversation

Steps

Step 1: Create Application

Login to Dify
Click "Create Application"
Select "Chat Assistant" template
Name: "E-commerce Customer Service Xiaomei"

Step 2: Configure Knowledge Base

Create knowledge base "Customer Service Documentation"
Upload documents:
- Return-Exchange-Policy.pdf
- FAQ.docx
- Product Manuals (multiple)
Select chunking strategy: "Auto mode"
Select Embedding: "OpenAI text-embedding-3-large"
Click "Process and Save" (wait for vectorization to complete)

Step 3: Write Prompt

jinja-raw

# Role
You are "Xiaomei," customer service for an e-commerce platform, professional, enthusiastic, patient.

# Capabilities
- Answer questions about orders, logistics, returns/exchanges
- Query order status (via tools)
- Retrieve company policy documents

# Knowledge Base
\{\{context}}

# Conversation History
\{\{history}}

# User Question
\{\{query}}

# Rules
1. Prioritize using knowledge base information to answer
2. If need to query order, use check_order tool
3. If user emotion is negative (e.g., complaint, anger), guide to human transfer
4. Keep replies concise, use emoji to lighten atmosphere
5. For uncertain questions, honestly say "let me check for you" or "transfer to specialist"

# Reply Format
- First understand user's question
- Provide solution or answer
- Ask if any other help needed

Step 4: Add Tools

Custom Tool: Query Order

yaml

Tool name: check_order
Description: Query order status, logistics information
Parameters:
  - order_id: Order number (required)
API:
  Endpoint: https://api.yourshop.com/orders/{order_id}
  Method: GET
  Authentication: Bearer ${API_KEY}
Return Example:
  {
    "order_id": "20240101001",
    "status": "Shipped",
    "tracking_number": "SF1234567890",
    "expected_delivery": "2024-01-05"
  }

Step 5: Test

Test Conversation 1: Knowledge Base Query

User: What's your return policy?
AI: Hello! Our return policy is as follows:
    - 7-day no-reason return (product unused)
    - Support pickup service (some regions)
    - Refund 1-3 business days to account
    Detailed policy can be found in [Help Center]. Any other questions?

Test Conversation 2: Tool Calling

User: Help me check logistics for order 20240101001
AI: [Calling check_order tool...]
    Your order has been shipped🚚
    Tracking number: SF1234567890
    Expected delivery: 2024-01-05
    You can track in real-time [here].

Test Conversation 3: Emotion Recognition

User: Why isn't my package here yet? Your efficiency is terrible!
AI: Very sorry to keep you waiting😔 I understand your anxiety.
    Let me immediately contact logistics specialist to expedite, and transfer you to human customer service.
    Please wait a moment, we'll handle your order with priority!
    [Trigger human transfer process]

Step 6: Publish

Click "Publish"
Select publish method:
- API: Get API key, integrate into your App
- Web Chat: Copy embed code, paste into website
- WeChat Official Account: Configure Webhook (requires verified account)

Embed Code Example:

html

<script src="https://api.dify.ai/embed.js" 
        data-app-id="YOUR_APP_ID"
        data-api-key="YOUR_API_KEY">
</script>

Paste into your website before </body>
Refresh page, chat bubble appears in bottom right ✅

Total Time

Prepare documents: 30 minutes
Configure Dify: 20 minutes
Test and optimize: 30 minutes
Total: About 1.5 hours

Compared to traditional development (2-4 weeks), efficiency improved 100x.

One-Line Summary

Mainstream AI orchestration platforms each have strengths:

Dify is open-source all-rounder, suitable for rapidly building any AI application; Coze is ByteDance enterprise first choice, strong multi-platform publishing and plugin ecosystem; n8n is automation king, connects all SaaS tools; Zapier is non-technical person's paradise, easy to use but expensive; FlowiseAI is AI engineer's debugging console, fine low-level control but complex—choosing the right tool is half the success.

Next Steps

Now you've mastered the landscape and details of AI orchestration platforms, time to get hands-on:

Register a Dify account (or deploy locally), follow this chapter's case to build a customer service Bot
Try Coze, experience ByteDance's plugin ecosystem
Install n8n, build an automation workflow (e.g., RSS → AI rewrite → post to Twitter)
Compare FlowiseAI, understand LangChain's underlying principles

Next chapter, we'll explore how AI programming empowers "everyone can code"—when your product managers, designers, and operations can all build tools with AI, what will the world become?

💡 Practical Assignment

Build a "personal knowledge base assistant" with Dify:

Upload your notes, articles, reading excerpts
Configure a conversation bot that can answer "what have I learned before"
Try adding web search tool, let AI search online for information
Deploy as webpage, share with friends

Expected Time: 2-3 hours
Gain: Deep understanding of practical value of RAG, Prompt engineering, tool calling

⚠️ Pitfall Guide

Don't over-rely on platform defaults, especially Prompt and chunking strategy, need to optimize based on actual data
Vector database choice matters, free Chroma suitable for prototyping, production environment recommend Pinecone/Qdrant
API costs accumulate, GPT-4 is expensive (every 1M tokens $10-30), consider using GPT-4o-mini or domestic models
Knowledge base isn't bigger is better, document quality > quantity, garbage in garbage out
Test and test again, AI output is unstable, test edge cases thoroughly before launch

15.2 Platform Deep Dive ​

Dify: Open-Source LLMOps Champion ​

Genetic Profile ​

Core Capabilities Breakdown ​

1. Three Application Types ​

2. Knowledge Base System ​

3. Prompt Orchestration ​

4. Tool Calling (Function Calling) ​

Deployment Methods ​

Suitable Scenarios ​

Coze: ByteDance's AI LEGO ​

Genetic Profile ​

Core Capabilities Breakdown ​

1. Bot Building ​

2. Plugin Marketplace ​

3. Workflow Engine ​

4. Multi-Platform Publishing ​

Pricing ​

Suitable Scenarios ​

n8n + AI: Old Automation's AI Evolution ​

Genetic Profile ​

Why is n8n Special? ​

AI Nodes Detailed ​

1. AI Agent Node ​

2. AI Langchain Node ​

3. AI Chat Node ​

n8n's Killer Scenarios ​

Scenario 1: Automated Content Pipeline ​

Scenario 2: Intelligent Customer Onboarding Process ​

Deployment ​

Suitable Scenarios ​

Zapier AI: Automation Giant's AI Ambition ​

Genetic Profile ​

AI Features ​

1. AI Actions ​

2. Zapier Chatbots ​

Pricing ​

Suitable Scenarios ​

FlowiseAI: LangChain's Visual Frontend ​

Genetic Profile ​

Core Features ​

Suitable Scenarios ​

Platform Comparison Table ​

Selection Suggestion Matrix ​

Real Case: Build Customer Service Bot with Dify ​

Requirements ​

Steps ​

Step 1: Create Application ​

Step 2: Configure Knowledge Base ​

Step 3: Write Prompt ​

Step 4: Add Tools ​

Step 5: Test ​

Step 6: Publish ​

Total Time ​

One-Line Summary ​

Next Steps ​

15.2 Platform Deep Dive

Dify: Open-Source LLMOps Champion

Genetic Profile

Core Capabilities Breakdown

1. Three Application Types

2. Knowledge Base System

3. Prompt Orchestration

4. Tool Calling (Function Calling)

Deployment Methods

Suitable Scenarios

Coze: ByteDance's AI LEGO

Genetic Profile

Core Capabilities Breakdown

1. Bot Building

2. Plugin Marketplace

3. Workflow Engine

4. Multi-Platform Publishing

Pricing

Suitable Scenarios

n8n + AI: Old Automation's AI Evolution

Genetic Profile

Why is n8n Special?

AI Nodes Detailed

1. AI Agent Node

2. AI Langchain Node

3. AI Chat Node

n8n's Killer Scenarios

Scenario 1: Automated Content Pipeline

Scenario 2: Intelligent Customer Onboarding Process

Deployment

Suitable Scenarios

Zapier AI: Automation Giant's AI Ambition

Genetic Profile

AI Features

1. AI Actions

2. Zapier Chatbots

Pricing

Suitable Scenarios

FlowiseAI: LangChain's Visual Frontend

Genetic Profile

Core Features

Suitable Scenarios

Platform Comparison Table

Selection Suggestion Matrix

Real Case: Build Customer Service Bot with Dify

Requirements

Steps

Step 1: Create Application

Step 2: Configure Knowledge Base

Step 3: Write Prompt

Step 4: Add Tools

Step 5: Test

Step 6: Publish

Total Time

One-Line Summary

Next Steps