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

# Parallel

> Chat with websites and enrich data using Parallel's web browsing and data enrichment capabilities

<div style={{ display: 'flex', justifyContent: 'center', margin: '2rem 0' }}>
  <div className="video-card">
    <video src="https://lindy-docs-content.nyc3.digitaloceanspaces.com/Human%20in%20Loop.mp4" width="600" autoPlay muted loop playsInline style={{ display: 'block', width: '100%', borderRadius: '16px' }} />
  </div>
</div>

## Overview

Parallel provides powerful web browsing and data enrichment capabilities for your agents. Perfect for:

* **Web research** — ask questions and get answers from web content
* **Data enrichment** — automatically enhance your data with web information
* **Entity research** — gather detailed information about companies, people, or topics

## Chat with Web Action

* **What it does**: Ask questions and get answers by browsing and analyzing web content in real-time.

<Frame>
  <img src="https://mintcdn.com/lindyai/QWdJNkViLsD1veyH/lindy-brand-assets/parallel-web-chat.png?fit=max&auto=format&n=QWdJNkViLsD1veyH&q=85&s=e3edf7d1afa6f227b836515ab0d495be" alt="Chat with Web action configuration" width="2026" height="1156" data-path="lindy-brand-assets/parallel-web-chat.png" />
</Frame>

### Inputs

* **Question**: The query or question you want answered
* **Processor**: Choose the model strength for your research needs

### Outputs

* **Response**: The response based on web research

## Web Enrichment Action

* **What it does**: Enriches your data by automatically gathering additional information about entities from the web.

<Frame>
  <img src="https://mintcdn.com/lindyai/QWdJNkViLsD1veyH/lindy-brand-assets/parallel-web-enrichment.png?fit=max&auto=format&n=QWdJNkViLsD1veyH&q=85&s=e7ed8e0ed1c5031b441897cea451792b" alt="Web Enrichment action configuration" width="2062" height="1368" data-path="lindy-brand-assets/parallel-web-enrichment.png" />
</Frame>

### Inputs

* **Entity**: The thing you want to enrich (company name, person, topic, etc.)
* **Enrichment Columns**: Choose automatic enrichment or specify custom columns
* **Processor**: Choose the model strength for processing

### Example Enrichment Columns

| Entity Type          | Common Columns                                                         |
| -------------------- | ---------------------------------------------------------------------- |
| **Companies**        | Industry, Employee Count, Revenue, Website, Headquarters, Founded Year |
| **People**           | Job Title, Company, LinkedIn Profile, Education, Location              |
| **General Entities** | Description, Category, Related Links, Key Facts                        |

### Outputs

* **Enriched Data**: Enhanced information about your entity

## Processor Selection

Both actions use the same processor options:

| Processor | Description                               | Strength |
| --------- | ----------------------------------------- | -------- |
| **Lite**  | Basic web browsing and research           | ⭐        |
| **Base**  | Standard processing with better analysis  | ⭐⭐       |
| **Core**  | Enhanced capabilities for complex tasks   | ⭐⭐⭐      |
| **Pro**   | Advanced processing with detailed results | ⭐⭐⭐⭐     |
| **Ultra** | Maximum capability for thorough research  | ⭐⭐⭐⭐⭐    |

<Tip>
  Core processor provides good balance of speed and detail for most research tasks.
</Tip>

## Best Practices

<AccordionGroup>
  <Accordion title="Question Optimization">
    * Be specific about what information you need
    * Include context when asking about entities
    * Use clear, direct questions for better results
    * Specify timeframes when relevant
  </Accordion>

  <Accordion title="Enrichment Strategy">
    * Start with automatic enrichment to see available data
    * Use specific columns when you know what you need
    * Choose appropriate processor based on entity complexity
    * Test with sample entities before processing large datasets
  </Accordion>

  <Accordion title="Rate Limit Management">
    * When looping, set concurrency limits to prevent hitting rate limits
    * Reduce parallel requests when processing large datasets
    * Monitor for timeout errors and adjust concurrency accordingly
  </Accordion>
</AccordionGroup>

## Next Steps

<CardGroup cols={2}>
  <Card title="Perplexity Search" href="/skills/web-browsing/perplexity" icon="browser">
    Alternative web search with AI-powered research
  </Card>

  <Card title="Context Management" icon="database" href="/skills/lindy-utilities/context">
    Store research findings for your agent's memory
  </Card>

  <Card title="Set Variables" icon="database" href="/skills/lindy-utilities/set-variables">
    Save enriched data for use in other workflow steps
  </Card>

  <Card title="Apify Web Scraping" icon="square-code" href="/skills/web-browsing/apify">
    Extract specific data from websites
  </Card>
</CardGroup>