A practical guide for developers and tech product teams: Learn how Dify’s two execution models—Agent and Workflow—power different types of AI workflows. Understand their differences and when to use which, so you can build more intelligent, more manageable AI automation systems.
1. Intro: When Your AI App Needs a Smarter Brain: Dify Difference Between Agent and Workflow
Generative AI is here—but building real AI apps is about more than just writing good prompts. Most useful apps need to handle things like:
- Multi-step task coordination
- Calling APIs or querying databases
- Remembering user context
- Making decisions across multiple interactions
Dify, an open-source LLM platform, supports two key ways to run tasks: Agent and Workflow. Both can drive your app—but they work very differently.
🧠 Agents are like smart AI brains that can make decisions and call tools.
🛠 Workflows are visual flowcharts where you control every step and condition.
So the real question is: Which one should you use? Can they work together? Are they overlapping?
Let’s break it down.
2. What Are Dify Agents and Workflows?
2.1 What Is a Dify Agent?
An Dify Agent is more than just a prompt—it’s a logic-driven AI unit with these features:
| Capability | Description |
|---|---|
| Smart decision-making | Uses ReAct, Function Calling, Tool-use, etc. |
| Tool access | Can connect to APIs, databases, plugins |
| Multi-turn reasoning | Remembers context, calls multiple tools, loops through logic |
| State awareness | Makes decisions based on input, history, and system states |
📌 Use Dify Agents when building AI agent workflows that require contextual understanding, autonomous decisions, and dynamic tool use.
2.2 What Is a Dify Workflow?
A Dify Workflow is a visual, no-code/low-code way to design a multi-step process with branching logic. Think of it as:
A flowchart that runs an LLM app using nodes, rules, and conditions.
Key parts of a Workflow:
| Element | Description |
|---|---|
| Node | Each node is an action: LLM call, function, HTTP request, logic branch, etc. |
| Data flow | Passes structured data (like JSON) between steps |
| Conditional logic | Supports if/else, switch, loops |
| Agent embedding | You can add Agent nodes inside the flow |
💡 It’s like LangChain Flow or Zapier Flow—focused on orchestrating LLM actions.
3. Deep Dive: How Do Agents and Workflows Actually Work?
Even though both can run tasks, their underlying logic and purpose are very different.
3.1 Visual Comparison Agents vs Workflows: Who Controls What?
Dify Agent vs Workflow Architecture Breakdown
graph LR UserInput[User Input] WorkflowEngine[Workflow Controller] AgentEngine[Agent Reasoning Engine] Tools[Tools / API / DB / HTTP] Output[App Response Output] UserInput --> WorkflowEngine WorkflowEngine -->|Condition Checks & Variable Passing| AgentEngine AgentEngine -->|Tool/Function Calls| Tools Tools --> AgentEngine AgentEngine --> WorkflowEngine WorkflowEngine --> Output
📌 In short:
- Workflow is the main controller—it decides when to use an Agent, call a tool, or move to the next step.
- Agent is the smart thinker—handling reasoning, tool use, and complex tasks inside the flow.
3.2 Side-by-Side Comparison
| Aspect | Dify Workflow | Dify Agent |
|---|---|---|
| Control method | Visual flow (nodes + logic) | Reasoning strategy (ReAct, Function Calls) |
| Stateful | ✅ Yes | ❌ No (depends on LLM memory) |
| Best for | Clear logic flows | Fuzzy goals or decisions |
| Multi-turn support | Partial (needs node setup) | ✅ Built-in |
| Tool use | Explicit node calls | Triggered by LLM reasoning |
| Debuggability | ✅ Easy (trace each node) | ⚠️ Harder (requires logs) |
| Reusability | Modular nodes | Shareable agent configs |
| Example use | CRM automation, webhook flows | Q&A, retrieval, code tasks |
3.3 How They Actually Run
🧭 Workflow Example:
- User input triggers the start
- A condition node checks inputs
- Calls an Agent to generate content
- Passes output to HTTP/API node
- Makes external API call
- Returns result to user
🧠 Agent Example:
- Reads input + history
- Enters reasoning loop (e.g. ReAct)
- Decides to call tool → gets result
- Thinks again → outputs final result
📌 Agents are “smarter,” but less transparent. Workflows are easier to control and trace.
4. Which One Should You Use?
Knowing the difference is step one. Choosing the right tool is what really matters.
| Your Goal | Best Pick | Why |
|---|---|---|
| Build an internal AI assistant | ✅ Agent | Needs multi-step reasoning |
| Connect APIs / databases | ✅ Workflow | Clear logic, stable variable flow |
| Combine Q&A + tool usage | ✅ Both | Agent thinks, Workflow controls |
| Lots of logic branches | ✅ Workflow | Clear visual structure |
| Trigger backend task chains | ✅ Workflow (main) + Agent (sub-task) | Best practice combo |
If you’re building intelligent assistants or decision engines, AI agent workflows with Agents are the way to go.
For API orchestration, automation flows, and logic control, dify workflows are more effective.
Many advanced systems use both for maximum flexibility.
5. Best Practice: Combine Agents and Workflows for Hybrid AI Automation
Think of Workflow as your system controller, and Agent as your smart operator.
| Role | Workflow | Agent |
|---|---|---|
| Function | Controls flow, logic | Handles complex thinking |
| Dev view | Visual, predictable | Flexible but less clear |
| Maintenance | Easier to debug | Needs log tracking |
| Combo strategy | Workflow runs the flow | Agent does the hard thinking |
Dify Hybrid Architecture: Workflow Controls, Agent Thinks
flowchart TD U[User Input] --> WF[Workflow Start Node] WF --> Check[Parameter Check Node] Check --> Agent1[Agent Reasoning Task] Agent1 --> Format[Format Output] Format --> API[HTTP Request / API Call] API --> Respond[Return Processed Result]
📌 Use Workflow to run the process—and let Agent handle deep reasoning inside.
6. Final Thoughts: Combine Logic and Intelligence for Better AI Apps
- Workflow gives you structure—like an AI production line.
- Agent adds smart thinking—like a skilled AI worker.
Use Workflow when you want control.
Use Agent when you need reasoning.
The best systems combine both—so your AI can be predictable and intelligent.
📚 Recommended Reading
🔹 n8n vs Dify: Best AI Workflow Automation Platform?
Compare the strengths of n8n and Dify for AI workflow automation. Learn when to choose one over the other—or how to use both together.
🔹 Building an Internal AI Knowledge Base with Dify: A Case Study
Discover how a medical company used Dify to create an internal knowledge assistant powered by LLMs and RAG integration.
🔹 Smart Warehouse Receipts: Automating Logistics with Dify + OCR + LLM
Explore how Dify powers intelligent warehouse systems by combining OCR, workflow logic, and AI for efficient receipt validation.
🔹 Dify MCP Server: Build Modular AI Systems Like Lego
Learn how to use Dify MCP Server to build modular, multi-agent AI systems that are flexible, scalable, and easy to maintain.
Need Help Designing Your AI Workflow?
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