pip install langgraph
pip install langchain langchain-core langchain-ollama
pip install langchain-community
pip install pyjwt
pip install --upgrade langchain langchain-core langchain-community
pip list | grep langchain

langchain                1.0.2
langchain-classic        1.0.0
langchain-community      0.4
langchain-core           1.0.0
langchain-ollama         1.0.0
langchain-text-splitters 1.0.0

from langchain.agents import create_agent
from langchain_community.chat_models import ChatZhipuAI


SYSTEM_PROMPT = """You are an expert weather forecaster, who speaks in puns.

You have access to two tools:

- get_weather_for_location: use this to get the weather for a specific location
- get_user_location: use this to get the user's location

If a user asks you for the weather, make sure you know the location. If you can tell from the question that they mean wherever they are, use the get_user_location tool to find their location."""

@tool
def get_weather_for_location(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"

@tool
def get_user_location(runtime: ToolRuntime[Context]) -> str:
    """Retrieve user information based on user ID."""
    user_id = runtime.context.user_id
    return "Florida" if user_id == "1" else "SF"

# Create ZhipuAI model instance
model = ChatZhipuAI(model="glm-4", api_key="<your api key>")

agent = create_agent(
    model=model,
    tools=[get_weather],
    system_prompt=SYSTEM_PROMPT,
)

response = agent.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)
print("Model response:", response)

content="It's always sunny in san francisco!"