# My Actor (`citric_kimberlite/my-actor`) Actor

- **URL**: https://apify.com/citric_kimberlite/my-actor.md
- **Developed by:** [zikun](https://apify.com/citric_kimberlite) (community)
- **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.10 / company hiring signal

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

### Python LangGraph template

A template for [LangGraph](https://www.langchain.com/langgraph) projects in Python for building AI agents with [Apify Actors](https://apify.com/actors). The template provides a basic structure and an example [LangGraph](https://www.langchain.com/langgraph) [ReAct agent](https://react-lm.github.io/) that calls [Actors](https://apify.com/actors) as tools in a workflow.

### How it works

A [ReAct agent](https://react-lm.github.io/) 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](https://apify.com/apify/instagram-scraper) 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 [LangChain agents documentation](https://docs.langchain.com/oss/python/langchain/agents) and the [LangChain tools documentation](https://python.langchain.com/docs/concepts/tools/).

For a more advanced multi-agent example, see the [Finance Monitoring Agent actor](https://github.com/apify/actor-finance-monitoring-agent) or visit the [LangGraph documentation](https://langchain-ai.github.io/langgraph/concepts/multi_agent/).

##### Pay Per Event

This template uses the [Pay Per Event (PPE)](https://docs.apify.com/platform/actors/publishing/monetize#pay-per-event-pricing-model) 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](.actor/pay_per_event.json).

### Included features

- **[Apify SDK](https://docs.apify.com/sdk/python/)** for Python - a toolkit for building Apify [Actors](https://apify.com/actors) and scrapers in Python
- **[Input schema](https://docs.apify.com/platform/actors/development/input-schema)** - define and easily validate a schema for your Actor's input
- **[Dataset](https://docs.apify.com/sdk/python/docs/concepts/storages#working-with-datasets)** - store structured data where each object stored has the same attributes
- **[Key-value store](https://docs.apify.com/platform/storage/key-value-store)** - store any kind of data, such as JSON documents, images, or text files

### Resources

- [What are AI agents?](https://blog.apify.com/what-are-ai-agents/)
- [Python tutorials in Academy](https://docs.apify.com/academy/python)
- [Apify Python SDK documentation](https://docs.apify.com/sdk/python/)
- [LangChain documentation](https://python.langchain.com/docs/introduction/)
- [LangGraph documentation](https://langchain-ai.github.io/langgraph/tutorials/introduction/)
- [Integration with Make, GitHub, Zapier, Google Drive, and other apps](https://apify.com/integrations)

### Getting started

For complete information [see this article](https://docs.apify.com/platform/actors/development#build-actor-at-apify-console). 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](https://docs.apify.com/sdk/js)
- [Apify SDK for Python documentation](https://docs.apify.com/sdk/python)
- [Apify Platform documentation](https://docs.apify.com/platform)
- [Join our developer community on Discord](https://discord.com/invite/jyEM2PRvMU)

# Actor input Schema

## `query` (type: `string`):

Query for the agent.

## `modelName` (type: `string`):

The OpenAI model to use. Currently supported models are gpt-4o and gpt-4o-mini, and the reasoning models o1 and o3-mini.

## `debug` (type: `boolean`):

If enabled, the Actor will run in debug mode and produce more output.

## Actor input object example

```json
{
  "query": "What is the total number of likes and the total number of comments for the latest 10 posts on the @openai Instagram account? From the 10 latest posts, show me the most popular one.",
  "modelName": "gpt-4o-mini",
  "debug": false
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "query": "What is the total number of likes and the total number of comments for the latest 10 posts on the @openai Instagram account? From the 10 latest posts, show me the most popular one.",
    "modelName": "gpt-4o-mini"
};

// Run the Actor and wait for it to finish
const run = await client.actor("citric_kimberlite/my-actor").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "query": "What is the total number of likes and the total number of comments for the latest 10 posts on the @openai Instagram account? From the 10 latest posts, show me the most popular one.",
    "modelName": "gpt-4o-mini",
}

# Run the Actor and wait for it to finish
run = client.actor("citric_kimberlite/my-actor").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "query": "What is the total number of likes and the total number of comments for the latest 10 posts on the @openai Instagram account? From the 10 latest posts, show me the most popular one.",
  "modelName": "gpt-4o-mini"
}' |
apify call citric_kimberlite/my-actor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,citric_kimberlite/my-actor"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/VE0obLPtphegeZfgy/builds/tWcxB4rsFpFf5bQlq/openapi.json