LLM Streaming

Streaming is supported by both Chat Completions (completion() / acompletion()) and Responses (responses() / aresponses()).

A ready-to-run example is available here!

Enable real-time display of LLM responses as they’re generated, token by token. This guide demonstrates how to use streaming callbacks to process and display tokens as they arrive from the language model.

How It Works

Streaming allows you to display LLM responses progressively as the model generates them, rather than waiting for the complete response. This creates a more responsive user experience, especially for long-form content generation.

Enable Streaming on LLM

Configure the LLM with streaming enabled:

llm = LLM(
    model="anthropic/claude-sonnet-4-5-20250929",
    api_key=SecretStr(api_key),
    base_url=base_url,
    usage_id="stream-demo",
    stream=True,  # Enable streaming
)

Define Token Callback

Create a callback function that processes streaming chunks as they arrive:

def on_token(chunk: ModelResponseStream) -> None:
    """Process each streaming chunk as it arrives."""
    choices = chunk.choices
    for choice in choices:
        delta = choice.delta
        if delta is not None:
            content = getattr(delta, "content", None)
            if isinstance(content, str):
                sys.stdout.write(content)
                sys.stdout.flush()

The callback receives a ModelResponseStream object containing:

choices: List of response choices from the model
delta: Incremental content changes for each choice
content: The actual text tokens being streamed

Register Callback with Conversation

Pass your token callback to the conversation:

conversation = Conversation(
    agent=agent,
    token_callbacks=[on_token],  # Register streaming callback
    workspace=os.getcwd(),
)

The token_callbacks parameter accepts a list of callbacks, allowing you to register multiple handlers if needed (e.g., one for display, another for logging).

Responses Streaming

For direct LLM calls, pass stream=True and an on_token callback. The call returns a complete LLMResponse after the stream finishes; callbacks receive ModelResponseStream chunks while it is being read.

from openhands.sdk.llm import Message, TextContent
from openhands.sdk.llm.streaming import ModelResponseStream

def show_text(chunk: ModelResponseStream) -> None:
    for choice in chunk.choices:
        if choice.delta.content:
            print(choice.delta.content, end="", flush=True)

response = llm.responses(
    [
        Message(role="system", content=[TextContent(text="You are a helpful assistant.")]),
        Message(role="user", content=[TextContent(text="Say hello")]),
    ],
    stream=True,
    on_token=show_text,
)

Use await llm.aresponses(...) for the asynchronous equivalent. It accepts synchronous or asynchronous callbacks. Without a callback, the SDK normally falls back to a non-streaming request. Endpoints that require streaming, such as ChatGPT subscription endpoints, still drain the stream and return the complete response without a callback. Instrumentation wrappers do not need to inherit from LiteLLM’s stream classes. The SDK accepts synchronous iterables and asynchronous iterables on the async path, including synchronous wrappers returned to an async caller. A completed Responses event yielded by the stream remains valid if the wrapper’s completed_response attribute is absent or None. A non-null wrapper completion takes precedence after the stream has been read. A stream with no completion raises LLMNoResponseError and follows the configured retry policy.

Ready-to-run Example

This example is available on GitHub: examples/01_standalone_sdk/29_llm_streaming.py

examples/01_standalone_sdk/29_llm_streaming.py

import os
import sys
from typing import Literal

from pydantic import SecretStr

from openhands.sdk import (
    Conversation,
    get_logger,
)
from openhands.sdk.llm import LLM
from openhands.sdk.llm.streaming import ModelResponseStream
from openhands.tools.preset.default import get_default_agent


logger = get_logger(__name__)


api_key = os.getenv("LLM_API_KEY") or os.getenv("OPENAI_API_KEY")
if not api_key:
    raise RuntimeError("Set LLM_API_KEY or OPENAI_API_KEY in your environment.")

model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929")
base_url = os.getenv("LLM_BASE_URL")
llm = LLM(
    model=model,
    api_key=SecretStr(api_key),
    base_url=base_url,
    usage_id="stream-demo",
    stream=True,
)

agent = get_default_agent(llm=llm, cli_mode=True)


# Define streaming states
StreamingState = Literal["thinking", "content", "tool_name", "tool_args"]
# Track state across on_token calls for boundary detection
_current_state: StreamingState | None = None


def on_token(chunk: ModelResponseStream) -> None:
    """
    Handle all types of streaming tokens including content,
    tool calls, and thinking blocks with dynamic boundary detection.
    """
    global _current_state

    choices = chunk.choices
    for choice in choices:
        delta = choice.delta
        if delta is not None:
            # Handle thinking blocks (reasoning content)
            reasoning_content = getattr(delta, "reasoning_content", None)
            if isinstance(reasoning_content, str) and reasoning_content:
                if _current_state != "thinking":
                    if _current_state is not None:
                        sys.stdout.write("\n")
                    sys.stdout.write("THINKING: ")
                    _current_state = "thinking"
                sys.stdout.write(reasoning_content)
                sys.stdout.flush()

            # Handle regular content
            content = getattr(delta, "content", None)
            if isinstance(content, str) and content:
                if _current_state != "content":
                    if _current_state is not None:
                        sys.stdout.write("\n")
                    sys.stdout.write("CONTENT: ")
                    _current_state = "content"
                sys.stdout.write(content)
                sys.stdout.flush()

            # Handle tool calls
            tool_calls = getattr(delta, "tool_calls", None)
            if tool_calls:
                for tool_call in tool_calls:
                    tool_name = (
                        tool_call.function.name if tool_call.function.name else ""
                    )
                    tool_args = (
                        tool_call.function.arguments
                        if tool_call.function.arguments
                        else ""
                    )
                    if tool_name:
                        if _current_state != "tool_name":
                            if _current_state is not None:
                                sys.stdout.write("\n")
                            sys.stdout.write("TOOL NAME: ")
                            _current_state = "tool_name"
                        sys.stdout.write(tool_name)
                        sys.stdout.flush()
                    if tool_args:
                        if _current_state != "tool_args":
                            if _current_state is not None:
                                sys.stdout.write("\n")
                            sys.stdout.write("TOOL ARGS: ")
                            _current_state = "tool_args"
                        sys.stdout.write(tool_args)
                        sys.stdout.flush()


conversation = Conversation(
    agent=agent,
    workspace=os.getcwd(),
    token_callbacks=[on_token],
)

story_prompt = (
    "Tell me a long story about LLM streaming, write it a file, "
    "make sure it has multiple paragraphs. "
)
conversation.send_message(story_prompt)
print("Token Streaming:")
print("-" * 100 + "\n")
conversation.run()

cleanup_prompt = (
    "Thank you. Please delete the streaming story file now that I've read it, "
    "then confirm the deletion."
)
conversation.send_message(cleanup_prompt)
print("Token Streaming:")
print("-" * 100 + "\n")
conversation.run()

# Report cost
cost = llm.metrics.accumulated_cost
print(f"EXAMPLE_COST: {cost}")

You can run the example code as-is.

The model name should follow the LiteLLM convention: provider/model_name (e.g., anthropic/claude-sonnet-4-5-20250929, openai/gpt-4o). The LLM_API_KEY should be the API key for your chosen provider.

ChatGPT Plus/Pro subscribers: You can use LLM.subscription_login() to authenticate with your ChatGPT account and access Codex models without consuming API credits. See the LLM Subscriptions guide for details.

Next Steps

LLM Error Handling - Handle streaming errors gracefully
Custom Visualizer - Build custom UI for streaming
Interactive Terminal - Display streams in terminal UI

Guides

Architecture

API Reference

How It Works

Enable Streaming on LLM

Define Token Callback

Register Callback with Conversation

Responses Streaming

Ready-to-run Example

Next Steps

​How It Works

​Enable Streaming on LLM

​Define Token Callback

​Register Callback with Conversation

​Responses Streaming

​Ready-to-run Example

​Next Steps

How It Works

Enable Streaming on LLM

Define Token Callback

Register Callback with Conversation

Responses Streaming

Ready-to-run Example

Next Steps