langgraph/examples/streaming-tokens-without-langchain.ipynb

How to stream LLM tokens (without LangChain LLMs)

In this example we will stream tokens from the language model powering an agent. We'll be using OpenAI client library directly, without using LangChain chat models. We will also use a ReAct agent as an example.

Setup

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%%capture --no-stderr
%pip install -U langgraph openai

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import getpass
import os


def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")


_set_env("OPENAI_API_KEY")

OPENAI_API_KEY:  ········

Define model, tools and graph

Define a node that will call OpenAI API

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from openai import AsyncOpenAI
from langchain_core.language_models.chat_models import ChatGenerationChunk
from langchain_core.messages import AIMessageChunk
from langchain_core.runnables.config import ensure_config, get_callback_manager_for_config

openai_client = AsyncOpenAI()
# define tool schema for openai tool calling

tool = {
  "type": "function",
  "function": {
    "name": "get_items",
    "description": "Use this tool to look up which items are in the given place.",
    "parameters": {
      "type": "object",
      "properties": {
        "place": {
          "type": "string"
        }
      },
      "required": [
        "place"
      ]
    }
  }
}
async def call_model(state, config=None):
    config = ensure_config(config | {"tags": ["agent_llm"]})
    callback_manager = get_callback_manager_for_config(config)
    messages = state["messages"]
    
    llm_run_manager = callback_manager.on_chat_model_start({}, [messages])[0]
    response = await openai_client.chat.completions.create(
        messages=messages,
        model="gpt-3.5-turbo",
        tools=[tool],
        stream=True
    )

    response_content = ""
    role = None

    tool_call_id = None
    tool_call_function_name = None
    tool_call_function_arguments = ""
    async for chunk in response:
        delta = chunk.choices[0].delta
        if delta.role is not None:
            role = delta.role

        if delta.content:
            response_content += delta.content
            llm_run_manager.on_llm_new_token(delta.content)

        if delta.tool_calls:
            # note: for simplicity we're only handling a single tool call here
            if delta.tool_calls[0].function.name is not None:
                tool_call_function_name = delta.tool_calls[0].function.name
                tool_call_id = delta.tool_calls[0].id

            # note: we're wrapping the tools calls in ChatGenerationChunk so that the events from .astream_events in the graph can render tool calls correctly
            tool_call_chunk = ChatGenerationChunk(
                message=AIMessageChunk(content="", additional_kwargs={"tool_calls": [delta.tool_calls[0].dict()]})
            )
            llm_run_manager.on_llm_new_token("", chunk=tool_call_chunk)
            tool_call_function_arguments += delta.tool_calls[0].function.arguments

    if tool_call_function_name is not None:
        tool_calls = [
            {
                "id": tool_call_id,
                "function": {"name": tool_call_function_name, "arguments": tool_call_function_arguments},
                "type": "function"
            }
        ]
    else:
        tool_calls = None

    response_message = {
        "role": role,
        "content": response_content,
        "tool_calls": tool_calls
    }
    return {"messages": [response_message]}

Define our tools and a tool-calling node

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import json

async def get_items(place: str) -> str:
    """Use this tool to look up which items are in the given place."""
    if "bed" in place:  # For under the bed
        return "socks, shoes and dust bunnies"
    if "shelf" in place:  # For 'shelf'
        return "books, penciles and pictures"
    else:  # if the agent decides to ask about a different place
        return "cat snacks"

# define mapping to look up functions when running tools
function_name_to_function = {
    "get_items": get_items
}

async def call_tools(state):
    messages = state["messages"]

    tool_call = messages[-1]["tool_calls"][0]
    function_name = tool_call["function"]["name"]
    function_arguments = tool_call["function"]["arguments"]
    arguments = json.loads(function_arguments)
    
    function_response = await function_name_to_function[function_name](**arguments)    
    tool_message = {
        "tool_call_id": tool_call["id"],
        "role": "tool",
        "name": function_name,
        "content": function_response,
    }
    return {
        "messages": [tool_message]
    }

Define our graph

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import operator
from typing import Annotated, TypedDict, Literal

from langgraph.graph import StateGraph, END

class State(TypedDict):
    messages: Annotated[list, operator.add]

def should_continue(state) -> Literal["tools", END]:
    messages = state['messages']
    last_message = messages[-1]
    if last_message["tool_calls"]:
        return "tools"
    return END

workflow = StateGraph(State)
workflow.set_entry_point("model")
workflow.add_node("model", call_model)  # i.e. our "agent"
workflow.add_node("tools", call_tools)
workflow.add_conditional_edges("model", should_continue)
workflow.add_edge("tools", "model")
graph = workflow.compile()

Stream tokens

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async for event in graph.astream_events({"messages": [{"role": "user", "content": "what's in the bedroom"}]}, version="v2"):
    tags = event.get("tags", [])
    if event["event"] == "on_chat_model_stream" and "agent_llm" in tags:
        print("LLM token", event["data"]["chunk"].dict())

/Users/vadymbarda/.virtualenvs/langgraph/lib/python3.11/site-packages/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: This API is in beta and may change in the future.
  warn_beta(

LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': 'call_xUcx3IPa8GREPOpjHVj5k9Wx', 'function': {'arguments': '', 'name': 'get_items'}, 'type': 'function'}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': 'get_items', 'args': '', 'id': 'call_xUcx3IPa8GREPOpjHVj5k9Wx', 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': 'get_items', 'args': '', 'id': 'call_xUcx3IPa8GREPOpjHVj5k9Wx', 'index': 0}]}
LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '{"', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [{'name': '', 'args': {}, 'id': None}], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '{"', 'id': None, 'index': 0}]}
LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': 'place', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': 'place', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': 'place', 'id': None, 'index': 0}]}
LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '":"', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': '":"', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '":"', 'id': None, 'index': 0}]}
LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': 'bed', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': 'bed', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': 'bed', 'id': None, 'index': 0}]}
LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': 'room', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': 'room', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': 'room', 'id': None, 'index': 0}]}
LLM token {'content': '', 'additional_kwargs': {'tool_calls': [{'index': 0, 'id': None, 'function': {'arguments': '"}', 'name': None}, 'type': None}]}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [{'name': None, 'args': '"}', 'id': None, 'error': None}], 'usage_metadata': None, 'tool_call_chunks': [{'name': None, 'args': '"}', 'id': None, 'index': 0}]}
LLM token {'content': 'In', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' the', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' bedroom', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ',', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' you', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' have', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' socks', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ',', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' shoes', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ',', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' and', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' some', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' dust', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' b', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': 'unn', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': 'ies', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': '.', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' Is', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' there', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' anything', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' else', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' you', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' would', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' like', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' to', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': ' know', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}
LLM token {'content': '?', 'additional_kwargs': {}, 'response_metadata': {}, 'type': 'AIMessageChunk', 'name': None, 'id': None, 'example': False, 'tool_calls': [], 'invalid_tool_calls': [], 'usage_metadata': None, 'tool_call_chunks': []}

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