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Don’t pass Dify’s workflow stream straight to useChat and expect it to work. Dify emits its own workflow-event protocol; Vercel AI SDK expects a text stream or an AI SDK message stream. Put Dify behind an application server, parse its events there, and either translate them into an AI SDK-supported format or use a custom client transport for an application-defined stream. Keep the Dify API key on the server in either design.
Can useChat consume Dify SSE directly?
Not as a drop-in connection. Dify’s SSE describes workflow events, while the AI SDK documents separate text-stream and UI-message/data-stream protocols. A Dify event containing JSON is not automatically an AI SDK message chunk. For an AI SDK data stream, a custom backend must follow the documented format and include the x-vercel-ai-ui-message-stream: v1 header. See AI SDK stream protocols.
The integration point is an endpoint owned by your application. It can consume Dify’s stream and return an AI SDK-compatible stream, or expose an application-defined event stream that a custom transport or hook understands. The right choice depends on whether the interface needs only the final answer or also workflow-specific progress and metadata.
Keep the Dify key on your server
The browser should call your application, not Dify with a secret key. Dify explicitly warns that a key embedded in frontend code or a client app can be extracted and abused, and recommends calling the API from a backend. Dify Cloud documents https://api.dify.ai/v1 as its API base URL; a self-hosted instance uses its own base URL. See Dify’s API getting-started guide.
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Your server should authenticate the application user, authorize the requested workflow inputs, and then make the Dify request using the server-side key. Include a per-end-user user value in the Dify request. Validate or derive that identity on your server; do not trust a browser-supplied identity string without checking it. Dify uses the field to distinguish end users, but your application must define its own authorization policy.
For a workflow run, the request selects streaming with response_mode: "streaming". The relevant request fields include the workflow inputs, the user identity, and the response mode. Keep the API key in the server’s authorization header, not in the request body sent by the browser. Dify documents streaming as the option for user-facing or long-running workflow runs; its alternative is a blocking JSON response. The exact endpoint and other request details depend on the Dify API and deployment you are using. See the Dify Workflow API.
Parse Dify’s SSE framing before interpreting events
Dify documents data events as data: lines containing JSON and separated by a blank line. A keep-alive can arrive as a bare event: ping without a data payload, so do not try to parse every frame as JSON. Workflow streams can begin with a ping; Dify identifies the workflow_started data event—not that first ping—as confirmation that the run was accepted. During a run, pings arrive roughly every 10 seconds, so the server’s read timeout should be comfortably longer than that interval. These details are in Dify’s streaming guide.
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Use a standards-aware SSE parser where possible. Network reads can split or combine lines and events, so a transport chunk is not necessarily a complete SSE event. Assemble a complete event and its data before parsing the JSON, then dispatch based on the event name. Ignore blank separators and frames without data where appropriate.
- Ping: Treat it as a keep-alive, not as run acceptance or workflow output.
workflow_started: Record that Dify accepted the run.- Other data events: Parse the JSON and handle the event according to the application’s explicit mapping.
Capture task_id and workflow_run_id when they become available. They have different purposes and should remain separate in application state.
Choose how the server stream reaches the React UI
| Approach | Best fit | What your application must do |
|---|---|---|
| Server adapter to an AI SDK stream | A chat-like interface that mainly displays text or a deliberately selected set of message parts. | Consume Dify SSE on the server, select the workflow output or progress worth showing, translate it into the chosen AI SDK protocol, and emit the required protocol framing. A text stream is limited to basic text; a UI-message/data stream supports richer message parts under the AI SDK contract. |
| Custom transport or dedicated hook | An interface that needs Dify node progress, workflow-specific fields, or event detail that does not map cleanly to ordinary chat messages. | Expose an application-defined stream from your server and parse it in the client transport or hook. Manage message state, lifecycle transitions, and event-to-UI mapping yourself. |
The AI SDK’s useChat supports transports that control request and response processing. Its default transport sends HTTP POST requests to /api/chat; custom transport configuration can change the endpoint, headers, credentials, and request body. That makes an application-owned adapter endpoint a natural seam. See AI SDK transport documentation.
Choose a plain text stream only if the interface needs basic text. It does not carry tool calls, usage, finish reasons, or arbitrary Dify node-event semantics. For richer message parts, use the AI SDK’s documented UI-message/data-stream contract rather than forwarding Dify JSON unchanged. The AI SDK describes this distinction in its stream protocol documentation.
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Represent completion and errors explicitly
For a Workflow app, workflow_finished is the terminal event. A failure may be visible in node_finished and workflow_finished with a failed status; other failures can arrive through an error event with status, code, and message. A stream can fail after the HTTP response has opened, leaving the HTTP status at 200. Therefore, the adapter must inspect event contents and set the UI’s success or error state from the workflow events, not from the HTTP status alone. Dify documents these cases in its streaming guide and errors and rate limits guide.
AI SDK useChat exposes UI statuses including submitted, streaming, ready, and error, along with stop and resume-related methods. Those statuses and methods do not automatically supply Dify’s task or run identifiers, nor do they implement Dify’s cancellation and reconnection semantics. Connect them through your adapter or custom transport. See AI SDK’s useChat reference.
Use the right Dify identifier for stop and reconnect
task_id: The control handle for a live task, including stopping it. If the user cancels, make sure your server-side cancellation path reaches Dify; merely aborting the browser’s connection may only stop delivery to that browser.workflow_run_id: Identifies the persisted workflow run and is used to reconnect to its event stream or check its result.
For a reconnect, use the same user value that started the run. Dify documents a 404 response when the identifier and user do not match. If the workflow is still running after reconnecting, confirm its final state with Get Workflow Run Detail rather than relying only on the reconnected stream to deliver a final event. The identifiers and reconnection behavior are covered by the Workflow API and streaming guide.
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Retry only errors that can recover
Classify failures before retrying. Dify identifies too_many_requests as concurrency pressure, for which backing off and retrying may help. A Cloud-plan rate_limit_error indicates quota exhaustion and will not be resolved by retrying. Fix validation, authorization, and quota problems instead of resubmitting unchanged requests. Network errors and server 500 responses are candidates for bounded backoff. Consult Dify’s errors and rate limits guidance for the applicable error details.
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What React 19 changes—and what it does not
React 19 is stable, but its release does not make Dify’s API protocol compatible with the AI SDK stream protocol. React’s rendering streams concern HTML rendering; Dify’s SSE is runtime workflow data that your client or server adapter must consume. React’s static prerender APIs wait for data rather than progressively emitting content as it loads. See the React 19 announcement.
For a simple answer display, translate Dify’s selected output into an AI SDK text stream or other supported message stream. For a workflow-aware interface, preserve the events you need in an application-defined contract and build the client state around those events. In both cases, keep credentials, event interpretation, and Dify task control behind your application’s server boundary.
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