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LLM streaming

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).

Ready-to-run example​

import os
import sys
from typing import Literal

from pydantic import SecretStr

from faheemcode.sdk import (
    Conversation,
    get_logger,
)
from faheemcode.sdk.llm import LLM
from faheemcode.sdk.llm.streaming import ModelResponseStream
from faheemcode.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.

Bring your own provider key
export LLM_API_KEY="your-api-key"
export LLM_MODEL="anthropic/claude-sonnet-4-5-20250929"  # or openai/gpt-4o, etc.
cd software-agent-sdk
uv run python examples/01_standalone_sdk/29_llm_streaming.py
Faheem Code Cloud key
# https://app.faheemcode.ai/settings/api-keys
export LLM_API_KEY="example-user-api-key"
export LLM_MODEL="faheemcode/claude-sonnet-4-5-20250929"
cd software-agent-sdk
uv run python examples/01_standalone_sdk/29_llm_streaming.py

Next steps​