Agent model React

import os
from langchain_deepseek import ChatDeepSeek
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.memory import MemorySaver
from langgraph.prebuilt import create_react_agent

os.environ['DEEPSEEK_API_KEY']='sk-'
os.environ['TAVILY_API_KEY']='tvly-dev-'

## create llm
model=ChatDeepSeek(model='deepseek-chat',temperature=0,streaming=True)

## create search tool
search=TavilySearchResults(max_results=3)
tools=[search]

## create memory
memory=MemorySaver()

## create React agent
agent_executor=create_react_agent(model,tools,checkpointer=memory)

##设置会话配置
config={"configurable":{"thread_id":"example_session"}}

def ask_agent(question):
    for step in agent_executor.stream(
        {"messages":[HumanMessage(content=question)]},
        config=config,
        stream_mode="values",
    ):
        step['messages'][-1].pretty_print()

if __name__=="__main__":
    ask_agent("what is the capital of China?")