streamUI
AI SDK RSC is currently experimental. We recommend using AI SDK UI for production. For guidance on migrating from RSC to UI, see our migration guide.
A helper function to create a streamable UI from LLM providers. This function is similar to AI SDK Core APIs and supports the same model interfaces.
To see streamUI in action, check out these examples.
Import
import { streamUI } from "ai/rsc"Parameters
model:
LanguageModel
The language model to use. Example: openai("gpt-4-turbo")
initial?:
ReactNode
The initial UI to render.
system:
string
The system prompt to use that specifies the behavior of the model.
prompt:
string
The input prompt to generate the text from.
messages:
Array<CoreSystemMessage | CoreUserMessage | CoreAssistantMessage | CoreToolMessage> | Array<UIMessage>
A list of messages that represent a conversation. Automatically converts UI messages from the useChat hook.
CoreSystemMessage
role:
'system'
The role for the system message.
content:
string
The content of the message.
CoreUserMessage
role:
'user'
The role for the user message.
content:
string | Array<TextPart | ImagePart | FilePart>
The content of the message.
TextPart
type:
'text'
The type of the message part.
text:
string
The text content of the message part.
ImagePart
type:
'image'
The type of the message part.
image:
string | Uint8Array | Buffer | ArrayBuffer | URL
The image content of the message part. String are either base64 encoded content, base64 data URLs, or http(s) URLs.
mimeType?:
string
The mime type of the image. Optional.
FilePart
type:
'file'
The type of the message part.
data:
string | Uint8Array | Buffer | ArrayBuffer | URL
The file content of the message part. String are either base64 encoded content, base64 data URLs, or http(s) URLs.
mimeType:
string
The mime type of the file.
CoreAssistantMessage
role:
'assistant'
The role for the assistant message.
content:
string | Array<TextPart | ToolCallPart>
The content of the message.
TextPart
type:
'text'
The type of the message part.
text:
string
The text content of the message part.
ToolCallPart
type:
'tool-call'
The type of the message part.
toolCallId:
string
The id of the tool call.
toolName:
string
The name of the tool, which typically would be the name of the function.
args:
object based on zod schema
Parameters generated by the model to be used by the tool.
CoreToolMessage
role:
'tool'
The role for the assistant message.
content:
Array<ToolResultPart>
The content of the message.
ToolResultPart
type:
'tool-result'
The type of the message part.
toolCallId:
string
The id of the tool call the result corresponds to.
toolName:
string
The name of the tool the result corresponds to.
result:
unknown
The result returned by the tool after execution.
isError?:
boolean
Whether the result is an error or an error message.
maxTokens?:
number
Maximum number of tokens to generate.
temperature?:
number
Temperature setting. The value is passed through to the provider. The range depends on the provider and model. It is recommended to set either `temperature` or `topP`, but not both.
topP?:
number
Nucleus sampling. The value is passed through to the provider. The range depends on the provider and model. It is recommended to set either `temperature` or `topP`, but not both.
topK?:
number
Only sample from the top K options for each subsequent token. Used to remove "long tail" low probability responses. Recommended for advanced use cases only. You usually only need to use temperature.
presencePenalty?:
number
Presence penalty setting. It affects the likelihood of the model to repeat information that is already in the prompt. The value is passed through to the provider. The range depends on the provider and model.
frequencyPenalty?:
number
Frequency penalty setting. It affects the likelihood of the model to repeatedly use the same words or phrases. The value is passed through to the provider. The range depends on the provider and model.
stopSequences?:
string[]
Sequences that will stop the generation of the text. If the model generates any of these sequences, it will stop generating further text.
seed?:
number
The seed (integer) to use for random sampling. If set and supported by the model, calls will generate deterministic results.
maxRetries?:
number
Maximum number of retries. Set to 0 to disable retries. Default: 2.
abortSignal?:
AbortSignal
An optional abort signal that can be used to cancel the call.
headers?:
Record<string, string>
Additional HTTP headers to be sent with the request. Only applicable for HTTP-based providers.
tools:
Record<string, Tool>
Tools that are accessible to and can be called by the model.
Tool
description?:
string
Information about the purpose of the tool including details on how and when it can be used by the model.
parameters:
zod schema
The typed schema that describes the parameters of the tool that can also be used to validation and error handling.
generate?:
(async (parameters) => ReactNode) | AsyncGenerator<ReactNode, ReactNode, void>
A function or a generator function that is called with the arguments from the tool call and yields React nodes as the UI.
toolChoice?:
"auto" | "none" | "required" | { "type": "tool", "toolName": string }
The tool choice setting. It specifies how tools are selected for execution. The default is "auto". "none" disables tool execution. "required" requires tools to be executed. { "type": "tool", "toolName": string } specifies a specific tool to execute.
text?:
(Text) => ReactNode
Callback to handle the generated tokens from the model.
Text
content:
string
The full content of the completion.
delta:
string
The delta.
done:
boolean
Is it done?
experimental_providerMetadata?:
Record<string,Record<string,JSONValue>> | undefined
Optional metadata from the provider. The outer key is the provider name. The inner values are the metadata. Details depend on the provider.
onFinish?:
(result: OnFinishResult) => void
Callback that is called when the LLM response and all request tool executions (for tools that have a `generate` function) are finished.
OnFinishResult
usage:
TokenUsage
The token usage of the generated text.
TokenUsage
promptTokens:
number
The total number of tokens in the prompt.
completionTokens:
number
The total number of tokens in the completion.
totalTokens:
number
The total number of tokens generated.
value:
ReactNode
The final ui node that was generated.
warnings:
Warning[] | undefined
Warnings from the model provider (e.g. unsupported settings).
rawResponse:
RawResponse
Optional raw response data.
RawResponse
headers?:
Record<string, string>
Response headers.
Returns
value:
ReactNode
The user interface based on the stream output.
rawResponse?:
RawResponse
Optional raw response data.
RawResponse
headers?:
Record<string, string>
Response headers.
warnings:
Warning[] | undefined
Warnings from the model provider (e.g. unsupported settings).
stream:
AsyncIterable<StreamPart> & ReadableStream<StreamPart>
A stream with all events, including text deltas, tool calls, tool results, and errors. You can use it as either an AsyncIterable or a ReadableStream. When an error occurs, the stream will throw the error.
StreamPart
type:
'text-delta'
The type to identify the object as text delta.
textDelta:
string
The text delta.
StreamPart
type:
'tool-call'
The type to identify the object as tool call.
toolCallId:
string
The id of the tool call.
toolName:
string
The name of the tool, which typically would be the name of the function.
args:
object based on zod schema
Parameters generated by the model to be used by the tool.
StreamPart
type:
'error'
The type to identify the object as error.
error:
Error
Describes the error that may have occurred during execution.
StreamPart
type:
'finish'
The type to identify the object as finish.
finishReason:
'stop' | 'length' | 'content-filter' | 'tool-calls' | 'error' | 'other' | 'unknown'
The reason the model finished generating the text.
usage:
TokenUsage
The token usage of the generated text.
TokenUsage
promptTokens:
number
The total number of tokens in the prompt.
completionTokens:
number
The total number of tokens in the completion.
totalTokens:
number
The total number of tokens generated.