> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1782332329-96d87c7.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# AgentQLLoader integration

> Integrate with the AgentQLLoader document loader using LangChain Python.

[AgentQL](https://www.agentql.com/)'s document loader provides structured data extraction from any web page using an [AgentQL query](https://docs.agentql.com/agentql-query). AgentQL can be used across multiple languages and web pages without breaking over time and change.

## Overview

`AgentQLLoader` requires the following two parameters:

* `url`: The URL of the web page you want to extract data from.
* `query`: The AgentQL query to execute. Learn more about [how to write an AgentQL query in the docs](https://docs.agentql.com/agentql-query) or test one out in the [AgentQL Playground](https://dev.agentql.com/playground).

Setting the following parameters are optional:

* `api_key`: Your AgentQL API key from [dev.agentql.com](https://dev.agentql.com). **`Optional`.**
* `timeout`: The number of seconds to wait for a request before timing out. **Defaults to `900`.**
* `is_stealth_mode_enabled`: Whether to enable experimental anti-bot evasion strategies. This feature may not work for all websites at all times. Data extraction may take longer to complete with this mode enabled. **Defaults to `False`.**
* `wait_for`: The number of seconds to wait for the page to load before extracting data. **Defaults to `0`.**
* `is_scroll_to_bottom_enabled`: Whether to scroll to bottom of the page before extracting data. **Defaults to `False`.**
* `mode`: `"standard"` uses deep data analysis, while `"fast"` trades some depth of analysis for speed and is adequate for most usecases. [Learn more about the modes in this guide.](https://docs.agentql.com/accuracy/standard-mode) **Defaults to `"fast"`.**
* `is_screenshot_enabled`: Whether to take a screenshot before extracting data. Returned in 'metadata' as a Base64 string. **Defaults to `False`.**

AgentQLLoader is implemented with AgentQL's [REST API](https://docs.agentql.com/rest-api/api-reference)

### Integration details

| Class           | Package           | Local | Serializable | JS support |
| :-------------- | :---------------- | :---: | :----------: | :--------: |
| `AgentQLLoader` | langchain-agentql |   ✅   |       ❌      |      ❌     |

### Loader features

|      Source     | Document Lazy Loading | Native Async Support |
| :-------------: | :-------------------: | :------------------: |
| `AgentQLLoader` |           ✅           |           ❌          |

## Setup

To use the AgentQL Document Loader, you will need to configure the `AGENTQL_API_KEY` environment variable, or use the `api_key` parameter. You can acquire an API key from our [Dev Portal](https://dev.agentql.com).

### Installation

Install **langchain-agentql**.

```python theme={null}
pip install -qU langchain-agentql
```

### Set credentials

```python theme={null}
import os

os.environ["AGENTQL_API_KEY"] = "YOUR_AGENTQL_API_KEY"
```

## Initialization

Next instantiate your model object:

```python theme={null}
from langchain_agentql.document_loaders import AgentQLLoader

loader = AgentQLLoader(
    url="https://www.agentql.com/blog",
    query="""
    {
        posts[] {
            title
            url
            date
            author
        }
    }
    """,
    is_scroll_to_bottom_enabled=True,
)
```

## Load

```python theme={null}
docs = loader.load()
docs[0]
```

```text theme={null}
Document(metadata={'request_id': 'bdb9dbe7-8a7f-427f-bc16-839ccc02cae6', 'generated_query': None, 'screenshot': None}, page_content="{'posts': [{'title': 'Launch Week Recap—make the web AI-ready', 'url': 'https://www.agentql.com/blog/2024-launch-week-recap', 'date': 'Nov 18, 2024', 'author': 'Rachel-Lee Nabors'}, {'title': 'Accurate data extraction from PDFs and images with AgentQL', 'url': 'https://www.agentql.com/blog/accurate-data-extraction-pdfs-images', 'date': 'Feb 1, 2025', 'author': 'Rachel-Lee Nabors'}, {'title': 'Introducing Scheduled Scraping Workflows', 'url': 'https://www.agentql.com/blog/scheduling', 'date': 'Dec 2, 2024', 'author': 'Rachel-Lee Nabors'}, {'title': 'Updates to Our Pricing Model', 'url': 'https://www.agentql.com/blog/2024-pricing-update', 'date': 'Nov 19, 2024', 'author': 'Rachel-Lee Nabors'}, {'title': 'Get data from any page: AgentQL’s REST API Endpoint—Launch week day 5', 'url': 'https://www.agentql.com/blog/data-rest-api', 'date': 'Nov 15, 2024', 'author': 'Rachel-Lee Nabors'}]}")
```

```python theme={null}
print(docs[0].metadata)
```

```python theme={null}
{'request_id': 'bdb9dbe7-8a7f-427f-bc16-839ccc02cae6', 'generated_query': None, 'screenshot': None}
```

## Lazy load

`AgentQLLoader` currently only loads one [`Document`](https://reference.langchain.com/python/langchain-core/documents/base/Document) at a time. Therefore, `load()` and `lazy_load()` behave the same:

```python theme={null}
pages = [doc for doc in loader.lazy_load()]
pages
```

```text theme={null}
[Document(metadata={'request_id': '06273abd-b2ef-4e15-b0ec-901cba7b4825', 'generated_query': None, 'screenshot': None}, page_content="{'posts': [{'title': 'Launch Week Recap—make the web AI-ready', 'url': 'https://www.agentql.com/blog/2024-launch-week-recap', 'date': 'Nov 18, 2024', 'author': 'Rachel-Lee Nabors'}, {'title': 'Accurate data extraction from PDFs and images with AgentQL', 'url': 'https://www.agentql.com/blog/accurate-data-extraction-pdfs-images', 'date': 'Feb 1, 2025', 'author': 'Rachel-Lee Nabors'}, {'title': 'Introducing Scheduled Scraping Workflows', 'url': 'https://www.agentql.com/blog/scheduling', 'date': 'Dec 2, 2024', 'author': 'Rachel-Lee Nabors'}, {'title': 'Updates to Our Pricing Model', 'url': 'https://www.agentql.com/blog/2024-pricing-update', 'date': 'Nov 19, 2024', 'author': 'Rachel-Lee Nabors'}, {'title': 'Get data from any page: AgentQL’s REST API Endpoint—Launch week day 5', 'url': 'https://www.agentql.com/blog/data-rest-api', 'date': 'Nov 15, 2024', 'author': 'Rachel-Lee Nabors'}]}")]
```

***

## API reference

For more information on how to use this integration, please refer to the [git repo](https://github.com/tinyfish-io/agentql-integrations/tree/main/langchain) or the [langchain integration documentation](https://docs.agentql.com/integrations/langchain)

***

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