Instapaper Python API Docs | dltHub

Instapaper is a service providing a full REST API for programmatic management of bookmarks, folders, highlights, and account credentials using OAuth 1.0a. The REST API base URL is https://www.instapaper.com/api/1 and all requests require OAuth 1.0a Authorization header signing.

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 add "dlt[hub]" and start loading Instapaper data in under 10 minutes.

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What data can I load from Instapaper?

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

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How do I authenticate with the Instapaper API?

The Instapaper Full API requires OAuth 1.0a with HMAC-SHA1 signature method, passing OAuth parameters via the Authorization header. Access tokens are obtained using xAuth and are provided as a query-string-like key-value pair format.

1. Get your credentials

1. Navigate to the Instapaper API developer portal at https://www.instapaper.com/developers.
2. Register your application via the link to request an OAuth consumer token (or visit https://instapaper.com/main/request_oauth_consumer_token directly).
3. Once your application is registered and approved, you will receive a Consumer Key and a Consumer Secret from the Instapaper developer dashboard.
4. To access user-specific data, use these consumer credentials along with the user's Instapaper username and password to perform an xAuth request to the /api/1/oauth/access_token endpoint. This will return the user-specific oauth_token and oauth_token_secret.

2. Add them to .dlt/secrets.toml

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[sources.instapaper_source]
consumer_key = "your_consumer_key_here"
consumer_secret = "your_consumer_secret_here"
oauth_token = "your_user_access_token_here"
oauth_token_secret = "your_user_access_token_secret_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Instapaper 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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uv run python instapaper_pipeline.py

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

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Pipeline instapaper_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset instapaper_data
The duckdb destination used duckdb:/instapaper.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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uv run dlthub 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 oauth/access_token and bookmarks/list from the Instapaper 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 instapaper_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.instapaper.com/api/1",
            "auth": {"type": "api_key", "api_key": access_token, "name": "Authorization", "location": "header"},
        },
        "resources": [
            {"name": "bookmarks", "endpoint": {"path": "api/1/bookmarks/list"}},
            {"name": "folders", "endpoint": {"path": "api/1/folders/list"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="instapaper_pipeline",
        destination="duckdb",
        dataset_name="instapaper_data",
    )
    load_info = pipeline.run(instapaper_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("instapaper_pipeline").dataset()
sessions_df = data.bookmarks.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM instapaper_data.bookmarks LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("instapaper_pipeline").dataset()
data.bookmarks.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 Instapaper 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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Next steps

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

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

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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