Quickstart - Docs by LangChain

This quickstart takes you from a simple setup to a fully functional AI agent in just a few minutes.

Requirements

For these examples, you will need to:

Install the LangChain package
Set up a Claude (Anthropic) account and get an API key
Set the ANTHROPIC_API_KEY environment variable in your terminal

Although these examples use Claude, you can use any supported model by changing the model name in the code and setting up the appropriate API key.

Build a basic agent

Start by creating a simple agent that can answer questions and call tools. The agent will use Claude Sonnet 4.6 as its language model, a basic weather function as a tool, and a simple prompt to guide its behavior.

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="claude-sonnet-4-6",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)

# Run the agent
agent.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)

Build a real-world agent

Next, build a practical weather forecasting agent that demonstrates key production concepts:

Detailed system prompts for better agent behavior
Create tools that integrate with external data
Model configuration for consistent responses
Structured output for predictable results
Conversational memory for chat-like interactions
Create and run the agent to test the fully functional agent

Let’s walk through each step:

Show Full example code

from dataclasses import dataclass

from langchain.agents import create_agent
from langchain.chat_models import init_chat_model
from langchain.tools import tool, ToolRuntime
from langgraph.checkpoint.memory import InMemorySaver
from langchain.agents.structured_output import ToolStrategy


# Define system prompt
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."""

# Define context schema
@dataclass
class Context:
    """Custom runtime context schema."""
    user_id: str

# Define tools
@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"

# Configure model
model = init_chat_model(
    "claude-sonnet-4-6",
    temperature=0
)

# Define response format
@dataclass
class ResponseFormat:
    """Response schema for the agent."""
    # A punny response (always required)
    punny_response: str
    # Any interesting information about the weather if available
    weather_conditions: str | None = None

# Set up memory
checkpointer = InMemorySaver()

# Create agent
agent = create_agent(
    model=model,
    system_prompt=SYSTEM_PROMPT,
    tools=[get_user_location, get_weather_for_location],
    context_schema=Context,
    response_format=ToolStrategy(ResponseFormat),
    checkpointer=checkpointer
)

# Run agent
# `thread_id` is a unique identifier for a given conversation.
config = {"configurable": {"thread_id": "1"}}

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

print(response['structured_response'])
# ResponseFormat(
#     punny_response="Florida is still having a 'sun-derful' day! The sunshine is playing 'ray-dio' hits all day long! I'd say it's the perfect weather for some 'solar-bration'! If you were hoping for rain, I'm afraid that idea is all 'washed up' - the forecast remains 'clear-ly' brilliant!",
#     weather_conditions="It's always sunny in Florida!"
# )


# Note that we can continue the conversation using the same `thread_id`.
response = agent.invoke(
    {"messages": [{"role": "user", "content": "thank you!"}]},
    config=config,
    context=Context(user_id="1")
)

print(response['structured_response'])
# ResponseFormat(
#     punny_response="You're 'thund-erfully' welcome! It's always a 'breeze' to help you stay 'current' with the weather. I'm just 'cloud'-ing around waiting to 'shower' you with more forecasts whenever you need them. Have a 'sun-sational' day in the Florida sunshine!",
#     weather_conditions=None
# )

Congratulations! You now have an 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