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Welcome to LangChain! This quickstart will take you from zero to a fully functional AI agent in just a few minutes. We’ll start simple and gradually build up to something more sophisticated.

​
Super quick start

Let’s begin with the absolute basics - creating a simple agent that can answer questions and use tools: 
from langchain.agents import create_agent

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

agent = create_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_weather],
    prompt="You are a helpful assistant",
)

# Run the agent
agent.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)
What just happened? We created an agent with:
  • A language model (Claude 3.7 Sonnet)
  • A simple tool (weather function)
  • A basic prompt
  • The ability to invoke it with messages

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Building a real-world agent

Now let’s create something more practical. We’ll build a weather forecasting agent that demonstrates the key concepts you’ll use in production:
  1. Detailed system prompts for better agent behavior
  2. Real-world tools that integrate with external data
  3. Model configuration for consistent responses
  4. Structured output for predictable results
  5. Conversational memory for chat-like interactions
Let’s walk through each step:
1

Define the system prompt

The system prompt is your agent’s personality and instructions. Make it specific and actionable:
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 whereever they are, use the get_user_location tool to find their location."""
2

Create tools

Tools are functions your agent can call. They should be well-documented. Oftentimes, tools will want to connect to external systems, and will rely on runtime configuration to do so. Notice here how the get_user_location tool does exactly that:
from langchain_core.tools import tool

def get_weather_for_location(city: str) -> str:  # (1)!
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"

from langchain_core.runnables import RunnableConfig

USER_LOCATION = {
    "1": "Florida",
    "2": "SF"
}

@tool
def get_user_location(config: RunnableConfig) -> str:
    """Retrieve user information based on user ID."""
    user_id = config["context"].get("user_id")
    return USER_LOCATION[user_id]
3

Configure your model

Set up your language model with the right parameters for your use case:
from langchain.chat_models import init_chat_model

model = init_chat_model(
    "anthropic:claude-3-7-sonnet-latest",
    temperature=0
)
4

Define response format

Structured outputs ensure your agent returns data in a predictable format. Here, we use Python’s DataClass dictionary.
from dataclasses import dataclass

@dataclass
class WeatherResponse:
    conditions: str
    punny_response: str
5

Add memory

Enable your agent to remember conversation history:
from langgraph.checkpoint.memory import InMemorySaver

checkpointer = InMemorySaver()
6

Bring it all together

Now assemble your agent with all the components:
agent = create_agent(
    model=model,
    prompt=system_prompt,
    tools=[get_user_location, get_weather_for_location],
    response_format=WeatherResponse,
    checkpointer=checkpointer
)

config = {"configurable": {"thread_id": "1"}}
context = {"user_id": "1"}
response = agent.invoke(
    {"messages": [{"role": "user", "content": "what is the weather outside?"}]},
    config=config,
    context=context
)

response['structured_response']

response = agent.invoke(
    {"messages": [{"role": "user", "content": "thank you!"}]},
    config=config,
    context=context
)

response['structured_response']

​
What you’ve built

Congratulations! You now have a sophisticated AI agent that can:
  • Understand context and remember conversations
  • Use multiple tools intelligently
  • Provide structured responses in a consistent format
  • Handle user-specific information through context
  • Maintain conversation state across interactions