TypeScript SDK - Stream Method - Edgee documentation

The stream() method is used to make streaming chat completion requests to the Edgee AI Gateway. It returns an AsyncGenerator<StreamChunk> that yields response chunks as they arrive from the API.

Arguments

The stream() method accepts two arguments:

Parameter	Type	Description
`model`	`string`	The model identifier to use (e.g., `"openai/gpt-5.2"`)
`input`	`string \| InputObject`	The input for the completion. Can be a simple string or a structured `InputObject`

Input Types

String Input

When input is a string, it’s automatically converted to a user message:

for await (const chunk of edgee.stream('gpt-5.2', 'Tell me a story')) {
  if (chunk.text) {
    process.stdout.write(chunk.text);
  }
  
  if (chunk.finishReason) {
    console.log(`\nFinished: ${chunk.finishReason}`);
  }
}
// Equivalent to: input: { messages: [{ role: 'user', content: 'Tell me a story' }] }

InputObject

When input is an InputObject, you have full control over the conversation:

Property	Type	Description
`messages`	`Message[]`	Array of conversation messages
`tools`	`Tool[]`	Array of function tools available to the model
`tool_choice`	`ToolChoice`	Controls which tool (if any) the model should call. See Tools documentation for details
`tags`	`string[]`	Optional tags to categorize and label the request for analytics and filtering. Can also be sent via the `x-edgee-tags` header (comma-separated)
`compression_model`	`string`	Compression model for this request: `"claude"`, `"codex"`, `"opencode"`, `"cursor"`. Each model is a bundle of compression strategies. Overrides API key settings when present.

For details about Message type, see the Send Method documentation. For details about Tool and ToolChoice types, see the Tools documentation. Example - Streaming with Messages:

for await (const chunk of edgee.stream('gpt-5.2', {
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Write a poem about coding' }
  ]
})) {
  if (chunk.text) {
    process.stdout.write(chunk.text);
  }
}

Return Value

The stream() method returns an AsyncGenerator<StreamChunk>. Each chunk contains incremental updates to the response.

StreamChunk Object

Each chunk yielded by the generator has the following structure:

Property	Type	Description
`choices`	`StreamChoice[]`	Array of streaming choices (typically one)
`compression`	`Compression \| null`	Token compression metrics (if compression was applied)

StreamChoice Object

Each choice in the choices array contains:

Property	Type	Description
`index`	`number`	The index of this choice in the array
`delta`	`StreamDelta`	The incremental update to the message
`finish_reason`	`string \| null \| undefined`	Reason why the generation stopped. Only present in the final chunk. Possible values: `"stop"`, `"length"`, `"tool_calls"`, `"content_filter"`, or `null`

Example - Handling Multiple Choices:

for await (const chunk of edgee.stream('gpt-5.2', 'Give me creative ideas')) {
  chunk.choices.forEach((choice, index) => {
    if (choice.delta.content) {
      console.log(`Choice ${index}: ${choice.delta.content}`);
    }
  });
}

StreamDelta Object

The delta object contains incremental updates:

Property	Type	Description
`role`	`string \| undefined`	The role of the message (typically `"assistant"`). Only present in the first chunk
`content`	`string \| undefined`	Incremental text content. Each chunk contains a portion of the full response
`tool_calls`	`ToolCall[] \| undefined`	Array of tool calls (if any). See Tools documentation for details

Convenience Properties

The StreamChunk class provides convenience getters for easier access:

Property	Type	Description
`text`	`string \| null`	Shortcut to `choices[0].delta.content` - the incremental text content
`role`	`string \| null`	Shortcut to `choices[0].delta.role` - the message role (first chunk only)
`finishReason`	`string \| null`	Shortcut to `choices[0].finish_reason` - the finish reason (final chunk only)

Example - Using Convenience Properties:

for await (const chunk of edgee.stream('gpt-5.2', 'Explain quantum computing')) {
  // Content chunks
  if (chunk.text) {
    process.stdout.write(chunk.text);
  }

  // First chunk contains the role
  if (chunk.role) {
    console.log(`Role: ${chunk.role}`);
  }

  // Last chunk contains finish reason
  if (chunk.finishReason) {
    console.log(`\nFinish reason: ${chunk.finishReason}`);
  }
}

Understanding Streaming Behavior

Chunk Structure

First chunk: Contains role (typically "assistant") and may contain initial content
Content chunks: Contain incremental content updates
Final chunk: Contains finish_reason indicating why generation stopped

Example - Collecting Full Response:

let fullText = '';

for await (const chunk of edgee.stream('gpt-5.2', 'Tell me a story')) {
  if (chunk.text) {
    fullText += chunk.text;
    process.stdout.write(chunk.text); // Also display as it streams
  }
}

console.log(`\n\nFull response (${fullText.length} characters):`);
console.log(fullText);

Finish Reasons

Value	Description
`"stop"`	Model generated a complete response and stopped naturally
`"length"`	Response was cut off due to token limit
`"tool_calls"`	Model requested tool/function calls
`"content_filter"`	Content was filtered by safety systems
`null`	Generation is still in progress (not the final chunk)

Empty Chunks

Some chunks may not contain content. This is normal and can happen when:

The chunk only contains metadata (role, finish_reason)
The chunk is part of tool call processing
Network buffering creates empty chunks

Always check for chunk.text before using it:

for await (const chunk of edgee.stream('gpt-5.2', 'Hello')) {
  if (chunk.text) {  // ✅ Good: Check before using
    console.log(chunk.text);
  }
  // ❌ Bad: console.log(chunk.text) - may log null
}

Error Handling

The stream() method can throw errors:

try {
  for await (const chunk of edgee.stream('gpt-5.2', 'Hello!')) {
    if (chunk.text) {
      process.stdout.write(chunk.text);
    }
  }
} catch (error) {
  if (error instanceof Error) {
    // API errors: "API error {status}: {message}"
    // Network errors: Standard fetch errors
    console.error('Stream failed:', error.message);
  }
}

​Arguments

​Input Types

​String Input

​InputObject

​Return Value

​StreamChunk Object

​StreamChoice Object

​StreamDelta Object

​Convenience Properties

​Understanding Streaming Behavior

​Chunk Structure

​Finish Reasons

​Empty Chunks

​Error Handling