This guide shows you how to set up and use LangGraph’s prebuilt, reusable components, which are designed to help you construct agentic systems quickly and reliably.

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Prerequisites

Before you start this tutorial, ensure you have the following:

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1. Install dependencies

If you haven’t already, install LangGraph and LangChain: 
pip install -U langgraph "langchain[anthropic]"
LangChain is installed so the agent can call the model.

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2. Create an agent

To create an agent, use create_react_agent: 
from langgraph.prebuilt import create_react_agent

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

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",  # (2)!
    tools=[get_weather],  # (3)!
    prompt="You are a helpful assistant"  # (4)!
)

# Run the agent
agent.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)
  1. Define a tool for the agent to use. Tools can be defined as vanilla Python functions. For more advanced tool usage and customization, check the tools page.
  2. Provide a language model for the agent to use. To learn more about configuring language models for the agents, check the models page.
  3. Provide a list of tools for the model to use.
  4. Provide a system prompt (instructions) to the language model used by the agent.

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3. Configure an LLM

To configure an LLM with specific parameters, such as temperature, use init_chat_model: 
from langchain.chat_models import init_chat_model
from langgraph.prebuilt import create_react_agent

# highlight-next-line
model = init_chat_model(
    "anthropic:claude-3-7-sonnet-latest",
    # highlight-next-line
    temperature=0
)

agent = create_react_agent(
    # highlight-next-line
    model=model,
    tools=[get_weather],
)
For more information on how to configure LLMs, see Models.

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4. Add a custom prompt

Prompts instruct the LLM how to behave. Add one of the following types of prompts:
  • Static: A string is interpreted as a system message.
  • Dynamic: A list of messages generated at runtime, based on input or configuration.
Define a fixed prompt string or list of messages:
from langgraph.prebuilt import create_react_agent

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_weather],
    # A static prompt that never changes
    # highlight-next-line
    prompt="Never answer questions about the weather."
)

agent.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]}
)
For more information, see Context.

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5. Add memory

To allow multi-turn conversations with an agent, you need to enable persistence by providing a checkpointer when creating an agent. At runtime, you need to provide a config containing thread_id — a unique identifier for the conversation (session): 
from langgraph.prebuilt import create_react_agent
from langgraph.checkpoint.memory import InMemorySaver

# highlight-next-line
checkpointer = InMemorySaver()

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_weather],
    # highlight-next-line
    checkpointer=checkpointer  # (1)!
)

# Run the agent
# highlight-next-line
config = {"configurable": {"thread_id": "1"}}
sf_response = agent.invoke(
    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
    # highlight-next-line
    config  # (2)!
)
ny_response = agent.invoke(
    {"messages": [{"role": "user", "content": "what about new york?"}]},
    # highlight-next-line
    config
)
  1. checkpointer allows the agent to store its state at every step in the tool calling loop. This enables short-term memory and human-in-the-loop capabilities.
  2. Pass configuration with thread_id to be able to resume the same conversation on future agent invocations.
When you enable the checkpointer, it stores agent state at every step in the provided checkpointer database (or in memory, if using InMemorySaver). Note that in the above example, when the agent is invoked the second time with the same thread_id, the original message history from the first conversation is automatically included, together with the new user input. For more information, see Memory.

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6. Configure structured output

To produce structured responses conforming to a schema, use the response_format parameter. The schema can be defined with a Pydantic model or TypedDict. The result will be accessible via the structured_response field. 
from pydantic import BaseModel
from langgraph.prebuilt import create_react_agent

class WeatherResponse(BaseModel):
    conditions: str

agent = create_react_agent(
    model="anthropic:claude-3-7-sonnet-latest",
    tools=[get_weather],
    # highlight-next-line
    response_format=WeatherResponse  # (1)!
)

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

# highlight-next-line
response["structured_response"]
  1. When response_format is provided, a separate step is added at the end of the agent loop: agent message history is passed to an LLM with structured output to generate a structured response. To provide a system prompt to this LLM, use a tuple (prompt, schema), e.g., response_format=(prompt, WeatherResponse).
LLM post-processing Structured output requires an additional call to the LLM to format the response according to the schema.

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Next steps