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LLMn8n & Dify Research

n8n Learning Path

What is n8n?

n8n is an open-source workflow automation tool that supports 200+ app integrations, helping you automate business processes and tasks.

Core Concepts

1. Workflow Fundamentals

  • Nodes: Individual steps in a workflow, each node represents an operation
  • Connections: Links between nodes that define data flow
  • Trigger Nodes: Starting points for workflows, can be scheduled tasks, webhooks, etc.

2. Data Processing

  • Data Structure: Understanding data formats and structures in n8n
  • Data Transformation: Converting and processing data between nodes
  • Data Mapping: Mapping output from one node to input of another
  • Data Filtering and Editing: Filtering and modifying data in workflows

3. Flow Control

  • Conditionals: Execute different branches based on conditions
  • Data Merging: Combine multiple data sources
  • Looping: Process data collections in batches
  • Error Handling: Gracefully handle errors in workflows
  • Sub-workflows: Split complex processes into reusable modules

Learning Path

Phase 1: Getting Started

  1. Environment Setup

    • Choose deployment method (Cloud / Self-hosted)
    • Complete quickstart tutorials
    • Familiarize with UI interface

  2. Create Your First Workflow

    • Understand the concept of nodes
    • Learn how to connect nodes
    • Run and debug workflows

  3. Common Triggers

    • Webhook triggers
    • Schedule triggers
    • App event triggers

Phase 2: Intermediate Applications

  1. Data Operations

    • Use Set node to set data
    • Use Code node for custom logic
    • Data transformation and formatting

  2. App Integrations

    • Connect popular apps (Gmail, Slack, GitHub)
    • Understand API authentication and authorization
    • Handle API response data

  3. Complex Workflow Design

    • Use IF node for conditional branches
    • Use Switch node for multi-way branching
    • Use Loop node for batch processing

Phase 3: Advanced Techniques

  1. AI Integration

    • Use AI nodes
    • Integrate OpenAI, LangChain, etc.

  2. Performance Optimization

    • Workflow performance tuning
    • Error handling best practices
    • Logging and monitoring

  3. Enterprise Applications

    • Team collaboration
    • Version control
    • Environment management (dev/production)
  • Official Documentation: https://docs.n8n.io 
  • Official Courses: Video and text tutorials for different skill levels
  • Community Forum: Exchange experiences with other users
  • Example Workflows: Learn and reuse workflow templates shared by the community

Practice Tips

  1. Start with simple automation tasks (like scheduled notifications)
  2. Gradually increase complexity and try integrating multiple apps
  3. Use official examples and templates for learning
  4. Join the community to share and learn from others’ experiences
  5. Regularly check the official blog for new features and best practices

Dify Learning Path

What is Dify?

Dify is an open-source LLM application development platform that combines Backend-as-a-Service and LLMOps, designed to streamline the development of generative AI solutions.

Core Concepts

1. Platform Features

  • LLM Support: Support for mainstream large language models (OpenAI, Claude, local models, etc.)
  • Prompt Orchestration: Intuitive prompt management and optimization
  • RAG Engine: High-quality Retrieval-Augmented Generation capabilities
  • AI Agent: Flexible agent framework
  • Low-Code Workflow: Visual application development process
  • API-First: Easy-to-integrate API interfaces

2. Application Types

  • Chat Assistant: Build conversational AI applications
  • Text Generation: Content creation and text processing
  • Agent Applications: Intelligent agents with tool-calling capabilities
  • Workflow Applications: Complex multi-step AI processes

3. Core Components

  • Knowledge Base: Manage and retrieve document data
  • Prompt Templates: Reusable prompt designs
  • Model Management: Unified model access and configuration
  • Datasets: Training and testing data management
  • Logs & Annotations: Application monitoring and data optimization

Learning Path

Phase 1: Quick Start

  1. Environment Setup

    • Choose cloud version or self-hosting
    • Complete account registration and configuration
    • Understand interface layout and basic concepts

  2. Create Your First Application

    • Select application type (chat/text generation)
    • Configure LLM model
    • Design basic prompts
    • Test and debug the application

  3. Model Integration

    • Configure API keys
    • Understand characteristics of different models
    • Select appropriate model parameters

Phase 2: Core Functions

  1. Knowledge Base Applications

    • Create and manage knowledge bases
    • Upload and process documents
    • Configure retrieval strategies
    • Build RAG applications

  2. Prompt Engineering

    • Learn prompt design principles
    • Use variables and context
    • Optimize prompt effectiveness
    • Manage prompt versions

  3. Workflow Orchestration

    • Understand workflow node types
    • Design multi-step processes
    • Conditional branches and loops
    • Integrate external tools and APIs

Phase 3: Advanced Applications

  1. Agent Development

    • Configure tools and function calling
    • Design agent reasoning processes
    • Handle complex task chains
    • Optimize agent performance

  2. Application Integration

    • Integrate into existing systems using APIs
    • Configure webhooks
    • SSO and permission management
    • Multi-tenant deployment

  3. Production Optimization

    • Monitoring and log analysis
    • Cost optimization strategies
    • Caching and performance tuning
    • Data security and privacy protection

Use Cases

Startups

  • Rapidly build AI product prototypes
  • Lower barriers to AI application development
  • Save infrastructure costs

Enterprise Users

  • Add LLM capabilities to existing applications
  • Build internal AI assistants
  • Unified LLM gateway and management

AI Enthusiasts

  • Learn and experiment with LLM applications
  • Understand AI product development processes
  • Participate in open-source community contributions
  • Official Documentation: https://docs.dify.ai 
  • GitHub Repository: View source code and contribute
  • Community Forum: Connect with 180,000+ developers
  • Application Templates: Learn and reuse community application examples

Practice Tips

  1. Start with simple chat applications to understand the basic workflow
  2. Try connecting different LLM models and compare results
  3. Build your own knowledge base application to master RAG technology
  4. Learn workflow orchestration to implement complex business logic
  5. Follow community updates for latest features and best practices
  6. Consider data security and choose appropriate deployment methods

Dify vs n8n

Key Differences

  • n8n: General-purpose workflow automation tool focused on app integration and process automation
  • Dify: LLM application development platform focused on building and managing AI applications

Complementary Usage

You can combine n8n and Dify:

  • Use Dify to build AI capabilities (e.g., Q&A, content generation)
  • Use n8n to orchestrate business processes and system integrations
  • Connect both platforms via APIs and webhooks
  • Achieve end-to-end intelligent automation solutions
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