AgentQL Python API Docs | dltHub

AgentQL is a web scraping and data extraction API that queries web pages and documents (HTML, PDFs, images) and returns structured JSON data. The REST API base URL is https://api.agentql.com/v1 and all requests require an X-API-Key header for authentication.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading AgentQL data in under 10 minutes.

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What data can I load from AgentQL?

Here are some of the endpoints you can load from AgentQL:

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How do I authenticate with the AgentQL API?

AgentQL uses an API key provided in the X-API-Key HTTP header on every request. Include Content-Type: application/json for JSON requests and multipart/form-data for file uploads.

1. Get your credentials

1. Go to the Dev Portal at https://dev.agentql.com/. 2) Sign in or create an account. 3) Navigate to API Keys / Credentials. 4) Create a new API key and copy the key. 5) Store the key securely; use it in the X-API-Key header.

2. Add them to .dlt/secrets.toml

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[sources.agentql_scraping_data_api_source]
api_key = "your_agentql_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the AgentQL API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python agentql_scraping_data_api_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline agentql_scraping_data_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset agentql_scraping_data_api_data
The duckdb destination used duckdb:/agentql_scraping_data_api.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline agentql_scraping_data_api_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads query_data and query_document from the AgentQL API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def agentql_scraping_data_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.agentql.com/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "query_data", "endpoint": {"path": "query-data", "data_selector": "data"}},
            {"name": "query_document", "endpoint": {"path": "query-document", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="agentql_scraping_data_api_pipeline",
        destination="duckdb",
        dataset_name="agentql_scraping_data_api_data",
    )
    load_info = pipeline.run(agentql_scraping_data_api_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("agentql_scraping_data_api_pipeline").dataset()
sessions_df = data.query_data.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM agentql_scraping_data_api_data.query_data LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("agentql_scraping_data_api_pipeline").dataset()
data.query_data.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load AgentQL data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401/403 responses, ensure your X-API-Key header is set to a valid API key. Generate or rotate keys at https://dev.agentql.com/ and update the X-API-Key header.

Rate limits and request errors

The docs reference per-call consumption for document pages (1 API call per image or per PDF page). If you receive 429 Too Many Requests, back off and retry with exponential backoff. Monitor request volume when querying multi-page PDFs.

Pagination and large responses

AgentQL returns extracted data in the data object matching your query shape. For very large responses or multi-page documents, consider splitting requests (per-page document queries) or using faster extraction mode (params.mode = "fast") to reduce runtime/cost.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack