LinkedIn Agent

Python LlamaIndex Agent Template

Create a new AI Agent with LlamaIndex using this template. It provides a basic structure for the Agent with the Apify SDK and allows you to easily add your own functionality.

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.
• LlamaIndex - a framework for building LLM-powered agents using your data.
• Dataset - a storage solution for structured data where each object stored shares the same attributes.

How it works

The Agent has two main tools:

1. call_contact_details_scraper - Calls the Contact Details Scraper to scrape contact details from websites.
2. summarize_contact_information - Summarizes the collected contact details.

Given a user query with a URL, the Agent uses the Contact Details Scraper to retrieve the contact information and optionally summarizes the data. The Agent can decide how to handle the data—whether to process it further or skip summarization if it's not necessary.

Sample queries:

• Find contact details for apify.com and provide raw results.
• Find contact details for apify.com and summarize them.

Before you start

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

• An Apify account and an Apify API token.
• An OpenAI account and 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 .actor/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:

• Apify Actors
• LlamaIndex agent
• Building a basic agent
• What are AI agents?
• 11 AI agent use cases on Apify

Additional material: Web Scraping Data for Generative AI

Getting started

For complete information see this article. In short, you will:

1. Build the Actor
2. Run the Actor

Pull the Actor for local development

If you would like to develop locally, you can pull the existing Actor from Apify console using Apify CLI:

1. Install apify-cli

   Using Homebrew

   $brew install apify-cli
   

   Using NPM

   $npm -g install apify-cli
   

2. Pull the Actor by its unique <ActorId>, which is one of the following:

   • unique name of the Actor to pull (e.g. "apify/hello-world")
   • or ID of the Actor to pull (e.g. "E2jjCZBezvAZnX8Rb")

   You can find both by clicking on the Actor title at the top of the page, which will open a modal containing both Actor unique name and Actor ID.

   This command will copy the Actor into the current directory on your local machine.

   $apify pull <ActorId>
   

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