Python SDK - Stream Method - Edgee documentation

The stream() method is used to make streaming chat completion requests to the Edgee AI Gateway. It returns a generator that yields StreamChunk objects as they arrive from the API.

Arguments

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

Input Types

String Input

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

for chunk in edgee.stream("gpt-5.2", "Tell me a story"):
    if chunk.text:
        print(chunk.text, end="", flush=True)
    
    if chunk.finish_reason:
        print(f"\nFinished: {chunk.finish_reason}")
# Equivalent to: input={"messages": [{"role": "user", "content": "Tell me a story"}]}

InputObject or Dictionary

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

Property	Type	Description
`messages`	`list[dict]`	Array of conversation messages
`tools`	`list[dict] \| None`	Array of function tools available to the model
`tool_choice`	`str \| dict \| None`	Controls which tool (if any) the model should call. See Tools documentation for details
`tags`	`list[str] \| None`	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`	`str`	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 chunk in 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:
        print(chunk.text, end="", flush=True)

Return Value

The stream() method returns a generator that yields StreamChunk objects. Each chunk contains incremental updates to the response.

StreamChunk Object

Each chunk yielded by the generator has the following structure:

Property	Type	Description
`choices`	`list[StreamChoice]`	Array of streaming choices (typically one)
`compression`	`Compression \| None`	Token compression metrics (if compression was applied)

StreamChoice Object

Each choice in the choices array contains:

Property	Type	Description
`index`	`int`	The index of this choice in the array
`delta`	`StreamDelta`	The incremental update to the message
`finish_reason`	`str \| None`	Reason why the generation stopped. Only present in the final chunk. Possible values: `"stop"`, `"length"`, `"tool_calls"`, `"content_filter"`, or `None`

Example - Handling Multiple Choices:

for chunk in edgee.stream("gpt-5.2", "Give me creative ideas"):
    for choice in chunk.choices:
        if choice.delta.content:
            print(f"Choice {choice.index}: {choice.delta.content}")

StreamDelta Object

The delta object contains incremental updates:

Property	Type	Description
`role`	`str \| None`	The role of the message (typically `"assistant"`). Only present in the first chunk
`content`	`str \| None`	Incremental text content. Each chunk contains a portion of the full response
`tool_calls`	`list[dict] \| None`	Array of tool calls (if any). See Tools documentation for details

Convenience Properties

The StreamChunk class provides convenience properties for easier access:

Property	Type	Description
`text`	`str \| None`	Shortcut to `choices[0].delta.content` - the incremental text content
`role`	`str \| None`	Shortcut to `choices[0].delta.role` - the message role (first chunk only)
`finish_reason`	`str \| None`	Shortcut to `choices[0].finish_reason` - the finish reason (final chunk only)

Example - Using Convenience Properties:

for chunk in edgee.stream("gpt-5.2", "Explain quantum computing"):
    # Content chunks
    if chunk.text:
        print(chunk.text, end="", flush=True)

    # First chunk contains the role
    if chunk.role:
        print(f"\nRole: {chunk.role}")

    # Last chunk contains finish reason
    if chunk.finish_reason:
        print(f"\nFinish reason: {chunk.finish_reason}")

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:

full_text = ""

for chunk in edgee.stream("gpt-5.2", "Tell me a story"):
    if chunk.text:
        full_text += chunk.text
        print(chunk.text, end="", flush=True)  # Also display as it streams

print(f"\n\nFull response ({len(full_text)} characters):")
print(full_text)

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
`None`	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 chunk in edgee.stream("gpt-5.2", "Hello"):
    if chunk.text:  # ✅ Good: Check before using
        print(chunk.text)
    # ❌ Bad: print(chunk.text) - may print None

Alternative: Using send() with stream=True

You can also use the send() method with stream=True to get streaming responses:

for chunk in edgee.send("gpt-5.2", "Tell me a story", stream=True):
    if chunk.text:
        print(chunk.text, end="", flush=True)

The stream() method is a convenience wrapper that calls send() with stream=True.

Error Handling

The stream() method can raise exceptions:

try:
    for chunk in edgee.stream("gpt-5.2", "Hello!"):
        if chunk.text:
            print(chunk.text, end="", flush=True)
except RuntimeError as error:
    # API errors: "API error {status}: {message}"
    # Network errors: Standard HTTP errors
    print(f"Stream failed: {error}")

​Arguments

​Input Types

​String Input

​InputObject or Dictionary

​Return Value

​StreamChunk Object

​StreamChoice Object

​StreamDelta Object

​Convenience Properties

​Understanding Streaming Behavior

​Chunk Structure

​Finish Reasons

​Empty Chunks

​Alternative: Using send() with stream=True

​Error Handling