Move your team from Dify to Flowise.
Each person gets a short interview, then a personalized step-by-step guide showing exactly where their saved work, prompts, and projects land in Flowise — and the few things that need a manual step, walked through. You send one invite; nobody becomes the help desk, and nothing in Dify is changed or cancelled. Want to see where you’re paying for both first? Run the free audit.
Both platforms support autonomous agents that select and invoke tools at runtime without hardcoded branching. To migrate a Dify Agent Node to Flowise: document the LLM model, system prompt, and list of tools configured in the Dify Agent Node; in Flowise Agentflow V2, create a Worker agent node with the same LLM and system prompt, then attach the equivalent Tool nodes. For multi-agent patterns, add a Supervisor agent in Flowise to replicate Dify's parent workflow that delegates to the Agent Node. To migrate in the reverse direction: take a Flowise Supervisor + Worker graph and recreate it as a Dify workflow with one Agent Node per Worker, wired via Dify's branching and iteration logic. In both directions, manually re-enter tool API keys — credentials are not exportable.
- Warning: Dify's Agent Node runs a single reasoning loop within a broader workflow; Flowise Agentflow V2 can run multiple agents in parallel with explicit looping via a Loop Node — migrating a Flowise multi-agent fan-out to Dify requires structuring it as iteration over agent calls, which may increase latency.
- Warning: Flowise's Loop Node exposes a configurable max-loop count to prevent runaway execution; Dify does not publish a per-run step cap — verify runtime behavior under load before production cutover.
- Warning: Tool availability differs: Dify's marketplace lists 800+ plugins; Flowise offers built-in tool nodes but requires custom code nodes for tools not in its catalogue — verify each required tool has an equivalent before migrating.
Both products support pausing an automated workflow for a human to review, approve, or correct an agent action before execution continues. In Dify, this is implemented via the Human Input Node (introduced in v1.13.0), added to the canvas between the agent step and the downstream action node. In Flowise, human-in-the-loop is a first-class node in Agentflow V2 that saves execution state so the workflow can resume after a restart. To migrate from Dify to Flowise: identify each Human Input Node in your Dify canvas, note the prompt text and the downstream action it guards, then insert a Human-in-the-Loop node at the same position in the Flowise Agentflow and configure the review prompt. To migrate from Flowise to Dify: add a Human Input Node in the Dify workflow canvas at the equivalent checkpoint position. In both directions, update any notification logic (webhook, email) that fires when the pause triggers, since each platform routes the review to a different URL or UI panel.
- Warning: Flowise persists the paused execution state across application restarts; Dify's Human Input Node behavior during a server restart was not explicitly documented — test restart behavior before relying on it in production workflows.
- Warning: The reviewer UI differs: Flowise surfaces the review prompt inside the Agentflow canvas or via API; Dify presents the Human Input Node prompt inline in the chatbot or workflow test panel — downstream processes waiting on approval must target the correct endpoint on the new platform.
Both products let you upload documents, embed them into a vector store, and wire retrieval into an agent or chatbot. To migrate from Dify to Flowise: export documents from your Dify Knowledge Base (the UI allows bulk download), then re-ingest them in Flowise by selecting a Document Loader node, choosing a vector store (Flowise supports 100+ options including Pinecone, Qdrant, and Weaviate), and attaching the retrieval node to your Chatflow or Agentflow. Chunking and embedding parameters must be re-entered manually — note your current Dify settings (chunk size, overlap, embedding model) before migrating. To migrate from Flowise to Dify: upload the same source files via the Dify Knowledge Base UI; select the matching embedding model and configure chunk settings to replicate retrieval quality. Validate end-to-end retrieval quality with a set of reference questions on both platforms before cutover.
- Warning: Dify Sandbox plan caps Knowledge Bases at 50 documents and 50 MB; Professional allows 500 documents / 5 GB; Team allows 1,000 documents / 20 GB — large corpora require at least a Professional plan.
- Warning: Flowise supports Graph RAG and Reranker for advanced retrieval; Dify's built-in retrieval pipeline does not expose a native reranker node — workflows that depend on reranking accuracy must compensate with post-retrieval logic.
- Warning: Vector index formats are not portable between the two platforms; embeddings must be regenerated after migration, which incurs API costs for large document sets.
Both platforms log every agent run and surface traces for debugging and quality review. To migrate from Dify to Flowise: export conversation logs from the Dify Logs & Monitoring panel (JSON or CSV download) for historical records before cutover; configure Flowise's Prometheus or OpenTelemetry integration to pipe execution traces to your existing observability stack (Datadog, Grafana, etc.). In Flowise, the visual debugger lets you replay individual steps — use it to validate that migrated flows behave identically to Dify runs on the same inputs. To migrate from Flowise to Dify: export run logs from Flowise's log-streaming endpoint, then archive them; in Dify, use the Logs & Monitoring panel per-app and configure annotation queues to flag and review poor outputs. Set up equivalent token-usage tracking in the new platform — both expose per-run token counts.
- Warning: Dify's annotation review quota is tier-gated: 10 annotations on Sandbox, 2,000 on Professional, 5,000 on Team — teams that heavily annotate responses for quality improvement will hit the Sandbox cap immediately.
- Warning: Flowise exports traces via Prometheus and OpenTelemetry; Dify does not advertise native Prometheus or OpenTelemetry export — teams that feed agent traces into an existing APM stack must verify Dify's integration options before migrating away from Flowise.
- Warning: Flowise's evaluation datasets and evaluator frameworks provide structured quality measurement over time; Dify's equivalent is the annotation queue, which is more manual — the workflow for regression testing after prompt changes differs significantly between the two platforms.
Both products center on a browser-based drag-and-drop canvas. When migrating from Dify to Flowise, map your Dify node types to Flowise equivalents: simple instruction-following chatbots become an Assistant; single-agent chatbots with retrieval become a Chatflow; multi-step pipelines with branching or parallel execution become an Agentflow V2. Export your Dify workflow as JSON from Settings > Export, document your node configuration (LLM model, system prompts, branching conditions), then recreate the canvas in Flowise by adding the corresponding nodes. When migrating from Flowise to Dify, open the Flowise Agentflow JSON and recreate each node on the Dify canvas; Dify's Iteration node covers Flowise's Loop Node, and Dify's parallel branches cover Flowise's branching patterns. Test each canvas inline before exposing it via API or embed.
- Warning: Dify offers Iteration and Parameter Extractor nodes with no direct Flowise equivalent — logic in those nodes must be rewritten as custom JavaScript code nodes in Flowise's Agentflow V2.
- Warning: Flowise's three builder types (Assistant, Chatflow, Agentflow) are distinct products with separate canvases; Dify has a single unified canvas with different node types — plan which builder mode matches each Dify workflow before starting the migration.
- Warning: Dify's per-plan trigger cap was unconfirmed on the pricing page; Flowise self-hosted imposes no trigger cap, while Flowise Cloud plan limits vary — verify against current pricing before committing.