Generating Text

This page adapts the original AI SDK documentation: Generating and Streaming Text.

Large language models (LLMs) can generate text in response to a prompt, which can contain instructions and information to process. For example, you can ask a model to come up with a recipe, draft an email, or summarize a document.

The AI SDK Core provides two functions to generate text and stream it from LLMs:

• generateText: Generates text for a given prompt and model.
• streamText: Streams text from a given prompt and model.

Advanced LLM features such as tool calling and structured data generation are built on top of text generation.

generateText

You can generate text using the generateText function. This function is ideal for non-interactive use cases where you need to write text (e.g. drafting email or summarizing web pages) and for agents that use tools.

import SwiftAISDK
import OpenAIProvider

let result = try await generateText(
  model: openai("gpt-4o"),
  prompt: "Write a vegetarian lasagna recipe for 4 people."
)
print(result.text)

You can use more advanced prompts to generate text with more complex instructions and content:

import SwiftAISDK
import OpenAIProvider

let result = try await generateText(
  model: openai("gpt-4o"),
  system: "You are a professional writer. You write simple, clear, and concise content.",
  prompt: "Summarize the following article in 3-5 sentences: \(article)"
)
print(result.text)

The result object of generateText contains several promises that resolve when all required data is available:

• result.content: The content that was generated in the last step.
• result.text: The generated text.
• result.reasoning: The full reasoning that the model has generated in the last step.
• result.reasoningText: The reasoning text of the model (only available for some models).
• result.files: The files that were generated in the last step.
• result.sources: Sources that have been used as references in the last step (only available for some models).
• result.toolCalls: The tool calls that were made in the last step.
• result.toolResults: The results of the tool calls from the last step.
• result.finishReason: The reason the model finished generating text.
• result.usage: The usage of the model during the final step of text generation.
• result.totalUsage: The total usage across all steps (for multi-step generations).
• result.warnings: Warnings from the model provider (e.g. unsupported settings).
• result.request: Additional request information.
• result.response: Additional response information, including response messages and body.
• result.providerMetadata: Additional provider-specific metadata.
• result.steps: Details for all steps, useful for getting information about intermediate steps.
• result.experimental_output: The generated structured output using the experimental_output specification.

Inspecting the full result

Need to debug the entire payload (same as JSON.stringify in TypeScript)? Call jsonString() or jsonValue() directly on the result.

let result = try await generateText(
  model: openai("gpt-4o"),
  prompt: "Invent a new holiday and describe its traditions."
)
let fullJSON = try result.jsonString()
print(fullJSON)

Both helpers mirror the upstream object shape, so you can log or persist the response without manually traversing the structure.

Accessing response headers & body

Sometimes you need access to the full response from the model provider, e.g. to access some provider-specific headers or body content.

You can access the raw response headers and body using the response property:

let result = try await generateText(
  model: openai("gpt-4o"),
  prompt: "..."
)
print(String(describing: result.response.headers))
print(String(describing: result.response.body))

onFinish callback

When using generateText, you can provide an onFinish callback that is triggered after the last step is finished (

). It contains the text, usage information, finish reason, messages, steps, total usage, and more:

let _ = try await generateText(
  model: openai("gpt-4o"),
  prompt: "Invent a new holiday and describe its traditions.",
  onFinish: { event in
    let messages = event.response.messages
    _ = messages
  }
)

streamText

Depending on your model and prompt, it can take a large language model (LLM) up to a minute to finish generating its response. This delay can be unacceptable for interactive use cases such as chatbots or real-time applications, where users expect immediate responses.

AI SDK Core provides the streamText function which simplifies streaming text from LLMs:

import SwiftAISDK
import OpenAIProvider

let result = try streamText(
  model: openai("gpt-4o"),
  prompt: "Invent a new holiday and describe its traditions."
)

for try await textPart in result.textStream {
  print(textPart)
}

Note In Swift, textStream is an AsyncThrowingStream<String, Error> that you iterate with for try await.

Warning streamText starts streaming immediately. Use onError to log errors.

You can use streamText on its own or integrate it with your Swift HTTP framework. The result object provides helpers for server responses:

• result.toUIMessageStreamResponse(...): Creates a UI Message stream HTTP response (includes tool calls) as a typed response value.
• result.pipeUIMessageStreamToResponse(...): Pipes UI Message stream deltas to any response conforming to StreamTextResponseWriter (e.g., a Vapor response adapter).
• result.toTextStreamResponse(...): Creates a simple text stream HTTP response.
• result.pipeTextStreamToResponse(...): Pipes text deltas to any StreamTextResponseWriter.

Note streamText uses backpressure and generates tokens as requested. You must consume the stream for it to finish.

It also provides several promises that resolve when the stream is finished:

• result.content: The content that was generated in the last step.
• result.text: The generated text.
• result.reasoning: The full reasoning that the model has generated.
• result.reasoningText: The reasoning text of the model (only available for some models).
• result.files: Files that have been generated by the model in the last step.
• result.sources: Sources that have been used as references in the last step (only available for some models).
• result.toolCalls: The tool calls that have been executed in the last step.
• result.toolResults: The tool results that have been generated in the last step.
• result.finishReason: The reason the model finished generating text.
• result.usage: The usage of the model during the final step of text generation.
• result.totalUsage: The total usage across all steps (for multi-step generations).
• result.warnings: Warnings from the model provider (e.g. unsupported settings).
• result.steps: Details for all steps, useful for getting information about intermediate steps.
• result.request: Additional request information from the last step.
• result.response: Additional response information from the last step.
• result.providerMetadata: Additional provider-specific metadata from the last step.

onError callback

streamText immediately starts streaming to enable sending data without waiting for the model. Errors become part of the stream and are not thrown to prevent e.g. servers from crashing.

To log errors, you can provide an onError callback that is triggered when an error occurs.

let _ = try streamText(
  model: openai("gpt-4o"),
  prompt: "Invent a new holiday and describe its traditions.",
  onError: { error in
    print("Stream error: \(error)")
  }
)

onChunk callback

When using streamText, you can provide an onChunk callback that is triggered for each chunk of the stream.

It receives the following chunk types:

• text
• reasoning
• source
• tool-call
• tool-input-start
• tool-input-delta
• tool-result
• raw

let _ = try streamText(
  model: openai("gpt-4o"),
  prompt: "Invent a new holiday and describe its traditions.",
  onChunk: { part in
    if case .textDelta(_, let text, _) = part { print(text) }
  }
)

onFinish callback

When using streamText, you can provide an onFinish callback that is triggered when the stream is finished (

). It contains the text, usage information, finish reason, messages, steps, total usage, and more:

let _ = try streamText(
  model: openai("gpt-4o"),
  prompt: "Invent a new holiday and describe its traditions.",
  onFinish: { finalStep, steps, totalUsage, finishReason in
    let messages = finalStep.response.messages
    _ = messages
  }
)

fullStream property

You can read a stream with all events using the fullStream property. This can be useful if you want to implement your own UI or handle the stream in a different way. Here is an example of how to use the fullStream property:

import SwiftAISDK
import OpenAIProvider

struct CityQuery: Codable, Sendable { let city: String }
struct Attractions: Codable, Sendable { let attractions: [String] }

let cityAttractions = tool(
  description: "List attractions for a city",
  inputSchema: CityQuery.self
) { query, _ in
  Attractions(attractions: [
    "Golden Gate Bridge",
    "Ferry Building",
    "Golden Gate Park"
  ])
}

let result = try streamText(
  model: openai("gpt-4.1"),
  tools: ["cityAttractions": cityAttractions.eraseToTool()],
  prompt: "What are some San Francisco tourist attractions?"
)

for try await part in result.fullStream {
  switch part {
  case .start:
    // handle start of stream
    break
  case .startStep:
    // handle start of step
    break
  case .textStart:
    // handle text start
    break
  case .textDelta(_, let text, _):
    // handle text delta here
    print(text)
  case .textEnd:
    // handle text end
    break
  case .reasoningStart, .reasoningDelta, .reasoningEnd:
    // handle reasoning parts
    break
  case .source(let src):
    // handle source
    print(src)
  case .file(let file):
    // handle file
    print(file)
  case .toolCall(let call, _):
    if call.toolName == "cityAttractions" {
      // handle tool call
    }
  case .toolInputStart, .toolInputDelta, .toolInputEnd:
    // handle tool input phases
    break
  case .toolResult(let result, _):
    // handle tool result
    print(result)
  case .toolError(let err):
    // handle tool error
    print(err)
  case .finishStep:
    // handle finish step
    break
  case .finish:
    // handle finish
    break
  case .error(let error):
    // handle stream error
    print(error)
  case .raw:
    // handle raw
    break
  }
}

Stream transformation

You can use the experimental_transform option to transform the stream. This is useful for e.g. filtering, changing, or smoothing the text stream.

The transformations are applied before the callbacks are invoked and the promises are resolved. If you e.g. have a transformation that changes all text to uppercase, the onFinish callback will receive the transformed text.

Smoothing streams

The AI SDK Core provides a smoothStream function that can be used to smooth out text streaming.

Note A smoothing helper is coming to the Swift port. You can build custom transforms today (see below).

Custom transformations

You can also implement your own custom transformations. The transformation function receives the tools that are available to the model, and returns a function that is used to transform the stream. Tools can either be generic or limited to the tools that you are using.

Here is an example of how to implement a custom transformation that converts all text to uppercase:

let upperCaseTransform: StreamTextTransform = { stream, _ in
  createAsyncIterableStream(source: AsyncThrowingStream<TextStreamPart, Error> { continuation in
    let task = Task {
      do {
        var it = stream.makeAsyncIterator()
        while let part = try await it.next() {
          if case .textDelta(let id, let text, let meta) = part {
            continuation.yield(.textDelta(id: id, text: text.uppercased(), providerMetadata: meta))
          } else {
            continuation.yield(part)
          }
        }
        continuation.finish()
      } catch { continuation.finish(throwing: error) }
    }
    continuation.onTermination = { _ in task.cancel() }
  })
}

You can also stop the stream using the stopStream function. This is e.g. useful if you want to stop the stream when model guardrails are violated, e.g. by generating inappropriate content.

When you invoke stopStream, it is important to simulate the step-finish and finish events to guarantee that a well-formed stream is returned and all callbacks are invoked.

let stopWordTransform: StreamTextTransform = { stream, opts in
  createAsyncIterableStream(source: AsyncThrowingStream<TextStreamPart, Error> { continuation in
    let task = Task {
      do {
        var it = stream.makeAsyncIterator()
        while let part = try await it.next() {
          if case .textDelta(_, let text, _) = part, text.contains("STOP") {
            opts.stopStream()
            break
          }
          continuation.yield(part)
        }
        continuation.finish()
      } catch { continuation.finish(throwing: error) }
    }
    continuation.onTermination = { _ in task.cancel() }
  })
}

Multiple transformations

You can also provide multiple transformations. They are applied in the order they are provided.

let result = try streamText(
  model: openai("gpt-4o"),
  prompt: "...",
  experimentalTransform: [upperCaseTransform, stopWordTransform]
)

Sources

Some providers such as Perplexity and Google Generative AI include sources in the response.

Currently sources are limited to web pages that ground the response. You can access them using the sources property of the result.

Each url source contains the following properties:

• id: The ID of the source.
• url: The URL of the source.
• title: The optional title of the source.
• providerMetadata: Provider metadata for the source.

When you use generateText, you can access the sources using the sources property:

let g = try await generateText(
  model: openai("gpt-4o"),
  prompt: "List the top 5 San Francisco news from the past week."
)
for source in g.sources { print(source) }

When you use streamText, you can access the sources using the fullStream property:

let s = try streamText(
  model: openai("gpt-4o"),
  prompt: "List the top 5 San Francisco news from the past week."
)
for try await part in s.fullStream {
  if case .source(let src) = part { print(src) }
}

The sources are also available in the result.sources promise.

Examples

You can see generateText and streamText in action using various frameworks in the following examples:

generateText

Examples for Swift will be added in a dedicated Quickstarts section (iOS/macOS, Vapor, CLI). Node/Next.js links are omitted in the Swift port.

streamText

Examples for Swift will be added in a dedicated Quickstarts section (iOS/macOS, Vapor, CLI). Node/Next.js links are omitted in the Swift port.