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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: json [ { "task-completed": { "eventTitle": "Task completed", "eventDescription": "Cost per query answered.", "eventPriceUsd": 0.1 } } ] In the Actor, trigger the event with: python 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 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. 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 bash brew install apify-cli Using NPM bash 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. bash 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