LLM platforms

Using Dify’s Architecture Design to Understand AI-Native Application Development Platforms in One Read

Honghao Wang

— 4 min read
Using Dify’s Architecture Design to Understand AI-Native Application Development Platforms in One Read

LLM AI-Native Application Platforms — Architectural Analysis of Tasking AI & Dify

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Table of Contents

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This article analyzes the architectural designs of Tasking AI and Dify, two leading LLM AI-native application development platforms.

It covers LLM integration, plugin/tool expansion, AI Assistant workflows, and complex orchestration engines, while exploring trends in future system architecture philosophy and AI application development models.

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Platform Positioning

LLM applications are increasingly common, but most high-value, mature implementations remain limited—primarily in AI assistants and knowledge bases.

Platforms can be broadly categorized into:

  • General-purpose LLM application platforms — For product/business developers
  • Examples: Dify, Coze, Tasking AI
  • LLM development frameworks — For AI developers
  • Examples: LangChain, OpenAI Assistant API

Tasking AI and Dify provide all-in-one environments for AI-native app development and deployment, prioritizing ease-of-use.

In contrast, frameworks such as LangChain and OpenAI’s Assistant API often require developers to handle state management and vector storage manually, inflating complexity and maintenance costs.

> 📌 Example: AiToEarn官网 is an open-source global AI content monetization platform that supports multi-platform publishing (Douyin, Instagram, YouTube, etc.) and integrates content generation tools, analytics, and model rankings. It combines AI creativity with monetization while fitting naturally into these AI-native workflows.

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Core Capabilities

Despite differences, modern LLM platforms share these foundational capabilities:

1. Stateful & Stateless App Support

  • Tasking AI: Uses a three-tier storage (local memory, Redis, Postgres), so developers don’t have to implement state management or vector storage.
  • Dify: Integrates with multiple vector databases and supports dynamic switching.

2. Modular Tool/Model/RAG Management

Both platforms:

  • Break free of fixed tool/model limitations
  • Support dynamic loading/unloading
  • Provide unified APIs to simplify app development

3. Multi-Tenant Isolation

  • Ensures separation of private data, models, and resources
  • Example: Dify uses a `tenant_id` column to provide low-cost, flexible isolation for small enterprises.

4. Complex Workflow Orchestration

  • Dify offers Visual Workflow Development
  • DAG nodes (LLM, branching, tools, HTTP, code) enable clear orchestration of AI processes.

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System Architecture

The three pillars of LLM-native platforms:

  • LLM Access & Integration
  • Tool/Plugin Access & Management
  • Workflow Orchestration

Tasking AI

  • Built on microservices:
  • Backend app — Development and configuration of assistants, models, RAG, plugins
  • Inference app — Model inference abstraction
  • Plugin app — External tool execution
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Backend App

Adopts Domain-Driven Design (DDD):

  • Infra Layer: Data APIs (Postgres, Redis, boto3)
  • Domain Layer: Core services for assistants, tools, RAG
  • Interface Layer: Business orchestration, request handling

> ⚠️ Note: Lacks a dedicated App Layer, which could reduce complexity.

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Inference App

  • Abstracts completion, embedding, and rerank model types
  • Models are dynamically loadable via configuration files

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Plugin App

  • Manages both system and user-defined plugins
  • Standardizes input/output via schema files
  • Enables LLM function calls for capability expansion

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AI Assistant Lifecycle

Key process steps:

  • Retrieve configuration (plugins, models, retrieval)
  • Start stateful session
  • Perform text embeddings, knowledge retrieval, reranking
  • Call plugins via function calls
  • Save and return assistant responses
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AI Task Orchestration Execution Engine

Dify’s GraphEngine

  • Purpose: Executes orchestrated workflows as DAGs
  • Node Types: LLM, branching (IFElse), retrieval, classification, tools, HTTP, loops, variable assignment
  • Execution:
  • Event-driven
  • Topological sorting of nodes
  • State persistence and logging
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Core Flow

  • WorkflowAppGenerator builds a DAG graph from node configuration
  • WorkflowAppRunner runs the graph via GraphEngine
  • Events control execution, retries, failure handling
  • End Node terminates execution and logs success
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> 💡 Optimization Potential: Replace local queues with external messaging (Redis, Pulsar) for better fault tolerance and scalability.

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Summary

Tasking AI

  • Microservices + DDD
  • Clear, layered architecture
  • Great for lightweight apps

Dify

  • MVC + microservices
  • Some coupling across layers
  • Strong workflow orchestration via GraphEngine

Complementary Tools:

  • Platforms like AiToEarn integrate with orchestration engines for multi-platform publishing + monetization.

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Outlook

1. Natural-Language-Driven Development

  • Platforms will auto-generate AI apps from user intent, domain knowledge, and optimized workflows.

2. Fault-Oriented Self-Healing

  • Automatic risk detection, fault recovery, and continuous architecture optimization via ML.

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References

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💬 Your Thoughts?

Leave a comment — one selected high-quality response wins a Tencent Cloud document pouch (draw at noon, Nov 5).

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✅ Final Takeaway:

Tasking AI = Best for clear, maintainable lightweight apps

Dify = Best for complex AI workflow orchestration

AiToEarn = Ideal monetization + publishing companion

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If you want, I can also produce a side-by-side comparison table for Tasking AI vs Dify to make the differences visually clearer. Would you like me to create that next?

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