Dify AI Application Portfolio: 9 Practical Projects Demonstrating Agent Collaboration and Workflow Orchestration

This portfolio showcases 9 AI applications built on the Dify platform, covering scenarios such as AIGC manga production, customer service capability assessment, and enterprise efficiency tools, with a core focus on the practical application of multi-agent collaboration, workflow orchestration, and prompt engineering.

• Original Author/Maintainer: baoguom276-maker
• Source Platform: GitHub
• Release Date: June 4, 2026
• Original Link: https://github.com/baoguom276-maker/mh-portfolio

Dify is an open-source LLM application development platform, with core values including:

1. Visual orchestration: Drag-and-drop to build complex workflows
2. Multi-model support: Compatible with OpenAI, Anthropic, local models, etc.
3. Native RAG: Built-in retrieval-augmented generation capabilities
4. Agent framework: Supports multi-agent collaboration and tool calling
5. Prompt management: Versioned prompt management

As AI applications move from proof-of-concept to production deployment, low-code/no-code platforms like Dify have become key infrastructure for AI engineering.

Multi-Agent Collaboration

• Role division: Different agents take on roles such as screenwriter, artist, reviewer, etc.
• Task flow: Agents delegate tasks and pass results between each other
• Collaborative work: Complete complex tasks in parallel or serially

Workflow Orchestration

• Process design: Break down business logic into orchestratable steps
• Conditional branching: Dynamically adjust paths based on intermediate results
• Loop iteration: Support multi-round processing scenarios

Prompt Engineering

• Role setting: Design professional prompts for agents
• Output specification: Define structured formats for downstream processing
• Quality control: Improve output quality through prompt optimization

Scenario 1: AIGC Manga Production Pipeline

• Workflow: Script generation → Character design → Storyboard drawing → Post-production orchestration
• Technical highlights: End-to-end automation, style consistency, human-machine collaboration

Scenario 2: Customer Service Capability Assessment

• Features: Simulated dialogue generation, intelligent scoring, capability analysis report
• Value: Reduce training costs, standardized evaluation, continuous improvement

Scenario 3: Enterprise Efficiency Tools

• Features: Document processing (contract review, meeting minutes), data analysis, knowledge management
• Technology: RAG architecture, tool calling, permission control

Typical Workflow Structure

User input layer → Intent recognition node → Agent collaboration layer → Knowledge retrieval layer → Output generation layer

Prompt Design Strategies

1. Systematic role definition: Clarify role, skills, tasks, and output format
2. Few-shot examples: Provide input-output samples to guide the model
3. Chain-of-thought guidance: Require the model to show reasoning processes to improve accuracy

Learning Value

• Practical reference: Real business scenarios and complete workflows
• Best practices: Tips for prompts, agent collaboration, and workflow orchestration
• Inspiration: Discover new AI application scenarios

Limitations

• Model dependency: Output quality is limited by the underlying LLM's capabilities
• Cost and latency: Complex workflows lead to high API costs and slow responses
• Error accumulation: Early errors in multi-step processes affect subsequent steps

Improvement Directions

Cache optimization, degradation strategies, human intervention, A/B testing to compare effects

This portfolio is a valuable learning resource, demonstrating how to build production-grade AI applications based on Dify, covering popular AI directions. It is recommended that developers deeply study the examples, understand the design ideas, and try to build their own AI applications. As AI platforms mature, more excellent works will promote the implementation of AI technology across various industries.