How to stream state updates of your graph¶
LangGraph supports multiple streaming modes. The main ones are:
values
: This streaming mode streams back values of the graph. This is the full state of the graph after each node is called.updates
: This streaming mode streams back updates to the graph. This is the update to the state of the graph after each node is called.
This guide covers stream_mode="updates"
.
Setup¶
We'll be using a simple ReAct agent for this guide.
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%%capture --no-stderr
%pip install -U langgraph langchain-openai
%%capture --no-stderr
%pip install -U langgraph langchain-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")
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")
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from typing import Literal
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.runnables import ConfigurableField
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
model = ChatOpenAI(model_name="gpt-4o", temperature=0)
graph = create_react_agent(model, tools)
from typing import Literal
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.runnables import ConfigurableField
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
@tool
def get_weather(city: Literal["nyc", "sf"]):
"""Use this to get weather information."""
if city == "nyc":
return "It might be cloudy in nyc"
elif city == "sf":
return "It's always sunny in sf"
else:
raise AssertionError("Unknown city")
tools = [get_weather]
model = ChatOpenAI(model_name="gpt-4o", temperature=0)
graph = create_react_agent(model, tools)
Stream updates¶
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inputs = {"messages": [("human", "what's the weather in sf")]}
async for chunk in graph.astream(inputs, stream_mode="updates"):
for node, values in chunk.items():
print(f"Receiving update from node: '{node}'")
print(values)
print("\n\n")
inputs = {"messages": [("human", "what's the weather in sf")]}
async for chunk in graph.astream(inputs, stream_mode="updates"):
for node, values in chunk.items():
print(f"Receiving update from node: '{node}'")
print(values)
print("\n\n")
Receiving update from node: 'agent' {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_kc6cvcEkTAUGRlSHrP4PK9fn', 'function': {'arguments': '{"city":"sf"}', 'name': 'get_weather'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 14, 'prompt_tokens': 57, 'total_tokens': 71}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3e7d703517', 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-cd68b3a0-86c3-4afa-9649-1b962a0dd062-0', tool_calls=[{'name': 'get_weather', 'args': {'city': 'sf'}, 'id': 'call_kc6cvcEkTAUGRlSHrP4PK9fn'}], usage_metadata={'input_tokens': 57, 'output_tokens': 14, 'total_tokens': 71})]} Receiving update from node: 'tools' {'messages': [ToolMessage(content="It's always sunny in sf", name='get_weather', tool_call_id='call_kc6cvcEkTAUGRlSHrP4PK9fn')]} Receiving update from node: 'agent' {'messages': [AIMessage(content='The weather in San Francisco is currently sunny.', response_metadata={'token_usage': {'completion_tokens': 10, 'prompt_tokens': 84, 'total_tokens': 94}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_3e7d703517', 'finish_reason': 'stop', 'logprobs': None}, id='run-009d83c4-b874-4acc-9494-20aba43132b9-0', usage_metadata={'input_tokens': 84, 'output_tokens': 10, 'total_tokens': 94})]}
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