Use Stores with LangGraph Agent

In this quickstart, we will be creating a simple agent that can get the top posts on Hacker News.

While large language models can generate text, they need additional tools to perform actions like fetching data from Hacker News. Using Stores, we will add tools for querying the Hacker News API.

Hacker News agent

from langchain_core.messages import HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.prebuilt import create_react_agent
import stores

# Load the Hacker News tool index
index = stores.Index(["silanthro/hackernews"])

# Initialize the LangGraph agent
agent_model = ChatGoogleGenerativeAI(model="gemini-2.0-flash-001")
agent_executor = create_react_agent(agent_model, index.tools)

# Get the response from the agent. The LangGraph agent will automatically execute
# tool calls and generate a response.
response = agent_executor.invoke(
    {
        "messages": [
            HumanMessage(content="What are the top 10 posts on Hacker News today?")
        ]
    }
)
print(f"Assistant response: {response['messages'][-1].content}")

Agent script walkthrough

1. Load the tools

First, we will load the Hacker News tools from the silanthro/hackernews tool index.

index = stores.Index(["silanthro/hackernews"])

You can also load a tool index from a public GitHub repository or load your own custom tools from your repository. Learn more about what a tool index is here.

The Hacker News API doesn't require any API key. If a tool requires an API key, you can pass it via the env_var parameter.

2. Create a ReAct agent with the tools

Remember to add your Gemini API key (GOOGLE_API_KEY) to your .env file.

index.tools is a list of functions loaded in the index. It can be used directly in the agent initialization because LangChain will automatically create the required function declaration JSON schema for us.

agent_model = ChatGoogleGenerativeAI(model="gemini-2.0-flash-001")
agent_executor = create_react_agent(agent_model, index.tools)

3. Invoke the agent

The LangGraph agent will automatically execute any functions required by the input task and generate a response with the tool call result.

response = agent_executor.invoke(
    {
        "messages": [
            HumanMessage(content="What are the top 10 posts on Hacker News today?")
        ]
    }
)

Next steps