Stack Exchange Python API Docs | dltHub

Stack Exchange API is a RESTful API that provides programmatic access to Q&A data across the Stack Exchange network (questions, answers, users, tags, sites, etc.). The REST API base URL is https://api.stackexchange.com/2.3 and API key and optional OAuth access_token (will accept Bearer in Authorization header).

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 Stack Exchange data in under 10 minutes.

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What data can I load from Stack Exchange?

Here are some of the endpoints you can load from Stack Exchange:

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How do I authenticate with the Stack Exchange API?

The API accepts an application key (query parameter "key") to raise quota and an OAuth 2.0 access_token for user-authenticated requests. As of 2026 the platform is transitioning to passing the key or access token via the Authorization: Bearer {token} header instead of query parameters.

1. Get your credentials

1. Register your application on Stack Apps: https://stackapps.com/apps/oauth/register. 2) Note the returned client_id and client_secret (and the app key). 3) For app-level increased quota use the app key. 4) To act on behalf of users implement OAuth 2.0 using the documented flow to obtain an access_token. 5) Use the key or access_token in requests (prefer Authorization: Bearer {token}).

2. Add them to .dlt/secrets.toml

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[sources.stack_exchange_source]
access_token = "your_oauth_access_token_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 Stack Exchange 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 stack_exchange_pipeline.py

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

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Pipeline stack_exchange_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset stack_exchange_data
The duckdb destination used duckdb:/stack_exchange.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 stack_exchange_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 questions and users from the Stack Exchange 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 stack_exchange_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.stackexchange.com/2.3",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "questions", "endpoint": {"path": "questions", "data_selector": "items"}},
            {"name": "users", "endpoint": {"path": "users", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="stack_exchange_pipeline",
        destination="duckdb",
        dataset_name="stack_exchange_data",
    )
    load_info = pipeline.run(stack_exchange_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("stack_exchange_pipeline").dataset()
sessions_df = data.questions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM stack_exchange_data.questions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("stack_exchange_pipeline").dataset()
data.questions.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 Stack Exchange 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 or error responses indicating authentication required, ensure you registered an app on Stack Apps and obtained an app key or completed OAuth to get an access_token. As of 2026, pass the key or access_token in the Authorization header as: Authorization: Bearer {KEY_OR_ACCESS_TOKEN} rather than as a query parameter.

Rate limits and throttles

The API enforces per-app and per-ip throttles; anonymous requests have lower limits and maximum page size is 100. Using an app key increases daily quota. Watch for error responses indicating quota exhausted.

Pagination and limits

Responses are paged. Use page and pagesize parameters (max pagesize 100). Most methods cap page count for anonymous access. Check the wrapper fields (has_more, quota_remaining) to drive pagination.

Common error fields

API error responses include fields such as error_id, error_message, and error_name in the JSON wrapper — inspect these to determine cause (throttle, access_denied, etc.).

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