Dify Workflow vs Chatflow 2026: Which One Should You Build?
Chatflow and Workflow in Dify look like the same node graph but behave differently: one remembers the conversation, one starts fresh every run. Choosing wrong means rebuilding the graph, not flipping a switch.
📜 Table of Contents
Dify Workflow vs Chatflow 2026: Which One Should You Build?
Both are node graphs, but one carries conversation state and the other does not. Dify cannot convert between them, so spend thirty seconds on one question: will the user follow up?
The difference is state
| Chatflow | Workflow | |
|---|---|---|
| Trigger | User sends a message | Single call (API, button, schedule) |
| Memory | Yes, across turns | None |
| Variables | Conversation variables persist | Destroyed at run end |
| Output | Streaming answer, multiple blocks | Structured output variables |
| Fits | Support, assistants, multi-turn tasks | Summaries, classification, extraction |
| Debugging | Harder, state interferes | Easier, reproducible |
| Token cost | History resent every turn | Single pass, predictable |
Chatflow: when users follow up
"Check this contract for risks" then "expand on clause three" only works if the app remembers. Chatflow brings conversation variables, a memory window, answer nodes and follow-up buttons.
Build a Chatflow when users refine, correct or add details; when the flow must ask before acting; when parameters arrive over several turns.
The hidden cost is tokens: every turn resends history, so turn ten can cost several times turn one. Limit the memory window, keep stable instructions in the system prompt, and put long documents in the knowledge base rather than the history.
Workflow: one in, one out
Input a PDF, output a summary and three clauses. Input a support message, output a label and a routing target. The caller is a program or a button, not a person chatting.
Workflow nodes are engineering-oriented: start node declares inputs, end node declares outputs, HTTP request calls external services, code nodes transform data, conditional branches route, iteration nodes handle arrays.
The gain is testability: same input, comparable output, automatable test cases, failures traceable to a node.
Decision rule
Answer three questions; any "yes" means Chatflow:
- Will the user refer back to the previous message?
- Does the flow need to confirm or request parameters?
- Must output stream to the user as it is generated?
Three "no" answers means Workflow. Note the common misjudgement: "support bot" sounds like automation but is almost always Chatflow, because customers follow up.
Differences that bite
| Point | Chatflow | Workflow |
|---|---|---|
| Variable scope | Conversation variables persist | Run-scoped only |
| Direct answer node | Available, streams in blocks | Not available; end node only |
| Iteration | Works but context grows | Better for batch work |
| Concurrency | Per-session resources | Single runs, scales out |
| Release | Changes affect live sessions | Canary and rollback friendly |
Hybrid: when you need both
Let Chatflow receive the user, clarify and collect parameters, then call a Workflow app to do the deterministic work and present the result conversationally. Flexibility stays in the chat layer, determinism in the workflow, and each side is testable alone.
FAQ
Can I convert later? No — rebuild the graph. Prototype logic in Workflow, then port it and add a conversation layer.
Which is cheaper? Workflow. Chatflow resends history each turn.
Can Workflow do multi-turn? Not natively. External state passed in as input is session management done outside Dify.
Which suits RAG? Both support knowledge retrieval. Follow-up-heavy Q&A wants Chatflow; system integration wants Workflow.
Local models? Yes, via Ollama or any OpenAI-compatible endpoint. Chatflow demands stronger instruction following.
API shape? Chatflow is conversational with a conversation_id; Workflow is request/response, easier to integrate.
Note: node sets and API shapes change by version; check current official docs.
Related
❓ FAQ
Can I convert between them?
No direct conversion - you rebuild the graph. Pick right the first time.
Which is cheaper?
Workflow - single-shot means no history re-sends.
Can a Workflow call a Chatflow?
Yes - Dify allows cross-app calls, so automation can invoke conversational apps.
Which is better for RAG?
Both support knowledge bases; Chatflow keeps the conversational follow-ups that RAG chat usually needs.
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