React demo

Wed, 05 Aug 2026 00:00:00 +0000
import json
import os
from typing import Annotated, Sequence, TypedDict
from dotenv import load_dotenv
from langchain_core.messages import BaseMessage, ToolMessage, SystemMessage
from langchain_core.runnables import RunnableConfig
from langgraph.constants import END
from langgraph.graph import StateGraph
from langgraph.graph.message import add_messages
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool

load_dotenv()

# 图状态
class AgentState(TypedDict):
 messages: Annotated[Sequence[BaseMessage], add_messages]

llm = ChatOpenAI(
 model=os.getenv("MODEL_NAME"),
 base_url=os.getenv("BASE_URL"),
 api_key=os.getenv("API_KEY"),
 timeout=30)

@tool
def get_weather(location: str) -> str:
 """Call to get the weather from a specific location."""
 if any([city in location.lower() for city in ['sf', 'san francisco']]):
 return "It's sunny in San Francisco, but you better look out if you're a a Gemini 😈."
 else:
 return f"I am not sure what the weather is in {location}."

tools = [get_weather]

model = llm.bind_tools(tools)

tools_by_name = {tool.name: tool for tool in tools}

#Define the tool node
def tool_node(state: AgentState):
 outputs = []
 for tool_call in state["messages"][-1].tool_calls:
 tool_result = tools_by_name[tool_call["name"]].invoke(tool_call["args"])
 outputs.append(
 ToolMessage(
 content=json.dumps(tool_result),
 name=tool_call["name"],
 tool_call_id=tool_call["id"],
 )
 )
 return {"messages": outputs}

# Define the node that calls the model
def call_model(state: AgentState, config: RunnableConfig):
 system_prompt = SystemMessage(
 "You are a helpful AI assistant, please respond to the users query to the best of your ability.",
 )
 response = model.invoke([system_prompt] + state["messages"], config)
 return {"messages": [response]}

# Define the conditional edge that determines whether to continue or not
def should_continue(state: AgentState):
 messages = state["messages"]
 last_message = messages[-1]
 if not last_message.tool_calls:
 return "end"
 else:
 return "continue"

# Define a new graph
workflow = StateGraph(AgentState)

# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)

# Set the entrypoint as 'agent'
workflow.set_entry_point("agent")

# Add a conditional edge
workflow.add_conditional_edges(
 "agent",
 should_continue,
 {
 "continue": "tools",
 "end": END,
 },
)

# Add a normal edge from 'tools' to 'agent'
workflow.add_edge("tools", "agent")
graph = workflow.compile()

print(graph.get_graph().draw_mermaid())

def print_stream(stream):
 for s in stream:
 message = s["messages"][-1]
 if isinstance(message, tuple):
 print(message)
 else:
 message.pretty_print()

while True:
 user_megs = input("\nuser: ").strip()
 if user_megs == "quit":
 break
 inputs = {"messages": [("user", user_megs)]}
 print_stream(graph.stream(inputs, stream_mode="values"))
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React demo
