Competitor Intelligence

Python LangGraph template

A template for LangGraph projects in Python for building AI agents with Apify Actors. The template provides a basic structure and an example LangGraph ReAct agent that calls Actors as tools in a workflow.

How it works

A ReAct agent is created and given a set of tools to accomplish a task. The agent receives a query from the user and decides which tools to use and in what order to complete the task. In this case, the agent is provided with an Instagram Scraper Actor to scrape Instagram profile posts and a calculator tool to sum a list of numbers to calculate the total number of likes and comments. The agent is configured to also output structured data, which is pushed to the dataset, while textual output is stored in the key-value store as a response.txt file.

How to use

Add or modify the agent tools in the src/tools.py file, and make sure to include new tools in the agent tools list in src/main.py. Additionally, you can update the agent system prompt in src/main.py. For more information, refer to the LangGraph ReAct agent documentation and the LangChain tools documentation.

For a more advanced multi-agent example, see the Finance Monitoring Agent actor or visit the LangGraph documentation.

Pay Per Event

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 schema 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(event_name='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.

To set up the PPE model for this Actor:

• Configure the OpenAI API key environment variable: provide your OpenAI API key to the OPENAI_API_KEY in the Actor's Environment variables.
• Configure Pay Per Event: establish the Pay Per Event pricing schema in the Actor's Monetization settings. First, set the Pricing model to Pay per event and add the schema. An example schema can be found in .actor/pay_per_event.json.

Included features

• Apify SDK for Python - a toolkit for building Apify Actors and scrapers in Python
• Input schema - define and easily validate a schema for your Actor's input
• Dataset - store structured data where each object stored has the same attributes
• Key-value store - store any kind of data, such as JSON documents, images, or text files

Resources

• What are AI agents?
• Python tutorials in Academy
• Apify Python SDK documentation
• LangChain documentation
• LangGraph documentation
• Integration with Make, GitHub, Zapier, Google Drive, and other apps

Getting started

For complete information see this article. To run the Actor use the following command:

$apify run

Deploy to Apify

Connect Git repository to Apify

If you've created a Git repository for the project, you can easily connect to Apify:

1. Go to Actor creation page
2. Click on Link Git Repository button

Push project on your local machine to Apify

You can also deploy the project on your local machine to Apify without the need for the Git repository.

1. Log in to Apify. You will need to provide your Apify API Token to complete this action.

   $apify login
   

2. Deploy your Actor. This command will deploy and build the Actor on the Apify Platform. You can find your newly created Actor under Actors -> My Actors.

   $apify push
   

Documentation reference

To learn more about Apify and Actors, take a look at the following resources:

• Apify SDK for JavaScript documentation
• Apify SDK for Python documentation
• Apify Platform documentation
• Join our developer community on Discord