API Changelog Python API Docs | dltHub

API Changelog is a Substack publication that curates and documents API changes and commentary. The REST API base URL is https://apichangelog.substack.com and No API key or bearer auth — public read-only content accessible via HTML and RSS/Atom; no official REST API..

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 API Changelog data in under 10 minutes.

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What data can I load from API Changelog?

Here are some of the endpoints you can load from API Changelog:

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How do I authenticate with the API Changelog API?

API Changelog does not provide a documented REST API or authentication flow. Public pages and RSS/Atom feeds are accessible without credentials. If you scrape pages, respect robots.txt and rate limits.

1. Get your credentials

Not applicable — the site does not provide API credentials or a developer dashboard.

2. Add them to .dlt/secrets.toml

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[sources.api_changelog_source]

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 API Changelog 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 api_changelog_pipeline.py

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

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Pipeline api_changelog_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset api_changelog_data
The duckdb destination used duckdb:/api_changelog.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 api_changelog_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 posts and sitemap from the API Changelog 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 api_changelog_source(=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://apichangelog.substack.com",
            "auth": {
                "type": "none",
                "": ,
            },
        },
        "resources": [
            {"name": "posts", "endpoint": {"path": "p/{post-slug}"}},
            {"name": "sitemap", "endpoint": {"path": "sitemap/2026"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="api_changelog_pipeline",
        destination="duckdb",
        dataset_name="api_changelog_data",
    )
    load_info = pipeline.run(api_changelog_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("api_changelog_pipeline").dataset()
sessions_df = data.posts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM api_changelog_data.posts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("api_changelog_pipeline").dataset()
data.posts.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 API Changelog 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

API Changelog does not offer an API requiring authentication. If you attempt to access private Substack APIs (undocumented), you may encounter 401/403; avoid relying on undisclosed endpoints.

Rate limiting and scraping

Substack may impose rate limits or anti‑bot protections. When scraping HTML or fetching RSS, throttle requests (e.g., 1 request/sec), obey robots.txt, and use caching. Repeated requests may result in 429 responses or temporary IP blocks.

Content format and selectors

Pages are HTML; there is no consistent JSON list key. Use the RSS/Atom feed (/feed) to obtain structured items (XML elements). If scraping HTML, parse article lists by their DOM structure (post links in main content or archive pages) — selectors will vary and must be confirmed at runtime.

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