> ## Documentation Index
> Fetch the complete documentation index at: https://newsletter-ai-agent.pratikdani.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Actor API

> Technical details of the Newsletter AI Agent Apify Actor API

# Newsletter AI Agent Actor API

This document provides technical details about the Newsletter AI Agent Apify Actor API, including input schema, output format, and usage examples.

## Actor Input Schema

The Newsletter AI Agent Actor accepts the following input parameters:

```json theme={null}
{
  "topic": "string"
}
```

### Parameters

| Parameter | Type   | Description                  | Required | Default                                                                                                                       |
| --------- | ------ | ---------------------------- | -------- | ----------------------------------------------------------------------------------------------------------------------------- |
| `topic`   | string | The topic for the newsletter | No       | "I want to know everything about AI agents – current news, AI agentic platforms and frameworks, and companies in this field." |

## Actor Output Schema

The actor outputs the generated newsletter in the following format:

```json theme={null}
{
  "topic": "string",
  "content": "string",
  "status": "string",
  "timestamp": "string"
}
```

### Output Fields

| Field       | Type   | Description                                                       |
| ----------- | ------ | ----------------------------------------------------------------- |
| `topic`     | string | The topic that was used to generate the newsletter                |
| `content`   | string | The generated newsletter content in Markdown format               |
| `status`    | string | The status of the generation process ("success" or "error")       |
| `timestamp` | string | ISO 8601 formatted timestamp of when the newsletter was generated |

## Error Output Schema

If an error occurs during newsletter generation, the actor will output:

```json theme={null}
{
  "topic": "string",
  "error": "string",
  "status": "error",
  "timestamp": "string"
}
```

### Error Fields

| Field       | Type   | Description                                             |
| ----------- | ------ | ------------------------------------------------------- |
| `topic`     | string | The topic that was used (if available)                  |
| `error`     | string | Error message describing what went wrong                |
| `status`    | string | Always "error" for error outputs                        |
| `timestamp` | string | ISO 8601 formatted timestamp of when the error occurred |

## Usage Examples

### Running the Actor via Apify Console

1. Navigate to the Newsletter AI Agent on the Apify platform
2. Set the `topic` input parameter to your desired topic
3. Click "Run" to start the actor
4. Wait for the actor to complete
5. View the results in the "Dataset" tab

### Running the Actor via Apify API Client (Python)

```python theme={null}
import os
from apify_client import ApifyClient

# Initialize the ApifyClient with your API token
client = ApifyClient(os.getenv('APIFY_API_KEY'))

# Start the actor and wait for it to finish
run = client.actor('your-username/newsletter-ai-agent').call({
    'topic': 'Latest advancements in quantum computing'
})

# Fetch the actor's output
output = client.dataset(run['defaultDatasetId']).list_items().items[0]
newsletter_content = output['content']

# Do something with the newsletter content
print(newsletter_content)
```

### Running the Actor via Apify API Client (JavaScript)

```javascript theme={null}
const { ApifyClient } = require('apify-client');

// Initialize the ApifyClient with your API token
const client = new ApifyClient({
    token: process.env.APIFY_API_KEY,
});

// Start the actor and wait for it to finish
const run = await client.actor('your-username/newsletter-ai-agent').call({
    topic: 'Latest advancements in quantum computing',
});

// Fetch the actor's output
const { items } = await client.dataset(run.defaultDatasetId).listItems();
const newsletterContent = items[0].content;

// Do something with the newsletter content
console.log(newsletterContent);
```

## Actor Lifecycle

1. The actor initializes and loads environment variables
2. It processes the input parameters (using default if none provided)
3. It creates a NewsletterCrew instance with the necessary agents
4. The Researcher Agent gathers information about the topic
5. The Writer Agent transforms the research into a newsletter draft
6. The Editor Agent reviews and improves the draft
7. The final newsletter is returned as output

## Resource Usage

The Newsletter AI Agent performs web scraping and uses large language models, which can be resource-intensive. The actor typically takes 3-5 minutes to generate a newsletter, depending on the complexity of the topic and the amount of information available.

## Next Steps

* Learn about the [agents](/agents/overview) that power the Newsletter AI Agent
* Explore the [tools](/tools/overview) used by the agents
* See how the Newsletter AI Agent [generates newsletters](/features/newsletter-generation)