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LangGraph.js agent template

LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows.

This template provides a basic structure and an example of a LangGraph ReAct agent that answers questions using web search.

How it works

The LangGraph agent follows these steps:

  1. Determines whether to answer questions using internal knowledge or by searching the web.
  2. If a web search is needed, it uses the RAG Web Browser to gather relevant data from websites.
  3. Utilizes the gathered data to generate an answer using the OpenAI model.

In LangGraph.js, agents use tools, which are functions designed to perform specific tasks. This agent has one tool, webSearchTool, defined in src/tools.js, which allows it to search the web for relevant data.

Sample query

  • How to build a LangGraph agent on the Apify platform?

Before you start

To run this template locally or on the Apify platform, you need:

When running the agent locally, set the OpenAI API key as an environment variable:

export OPENAI_API_KEY=your-openai-api-key

When running the agent on the Apify platform, set the OpenAI API key in the environment variables of the Actor. To do this, go to Actor settings → Source → Code, then scroll down to the Environment variables tab and add a new variable named OPENAI_API_KEY with your OpenAI API key.

Monetization

This template uses the Pay Per Event (PPE) monetization model, which provides flexible pricing based on defined events.

To charge users, define events in JSON format and save them on the Apify platform. Here is an example of pay_per_event.json with the task-completed event:

[
    {
        "task-completed": {
            "eventTitle": "Task completed",
            "eventDescription": "Cost per query answered.",
            "eventPriceUsd": 0.1
        }
    }
]

In the Actor, trigger the event with:

await Actor.charge({ eventName: 'task-completed' });

This approach allows you to programmatically charge users directly from your Actor, covering the costs of execution and related services, such as LLM input/output tokens.

Resources

Useful resources to help you get started: