Inoreader Python API Docs | dltHub

Inoreader is a web feed reader platform and API for accessing users' subscriptions, feeds, and article streams. The REST API base URL is https://www.inoreader.com/reader/api/0 and Requests require AppId/AppKey; user‑scoped endpoints also require an OAuth 2.0 Bearer token..

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 Inoreader data in under 10 minutes.

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What data can I load from Inoreader?

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

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How do I authenticate with the Inoreader API?

API access requires application credentials (App ID and App Key) sent on every request, typically via HTTP headers AppId and AppKey. User‑scoped operations also require an OAuth 2.0 bearer token (Authorization: Bearer ) or legacy GoogleLogin token.

1. Get your credentials

1. Sign in to the Inoreader developer portal and create a new application.
2. After creation you will be shown an App ID and an App Key; copy them.
3. For user‑level access, implement the OAuth 2.0 flow: direct the user to the Inoreader authorisation URL, obtain an authorization code, exchange the code for an access token, and use the token as a Bearer token in the Authorization header for user‑scoped calls.

2. Add them to .dlt/secrets.toml

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[sources.inoreader_source]
app_id = "your_app_id"
app_key = "your_app_key"

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 Inoreader 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 inoreader_pipeline.py

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

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Pipeline inoreader_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset inoreader_data
The duckdb destination used duckdb:/inoreader.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 inoreader_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 stream_contents and subscription_list from the Inoreader 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 inoreader_source(app_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.inoreader.com/reader/api/0",
            "auth": {
                "type": "api_key",
                "app_key": app_key,
            },
        },
        "resources": [
            {"name": "stream_contents", "endpoint": {"path": "stream/contents/[streamId]", "data_selector": "items"}},
            {"name": "subscription_list", "endpoint": {"path": "subscription/list", "data_selector": "subscriptions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="inoreader_pipeline",
        destination="duckdb",
        dataset_name="inoreader_data",
    )
    load_info = pipeline.run(inoreader_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("inoreader_pipeline").dataset()
sessions_df = data.stream_contents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM inoreader_data.stream_contents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("inoreader_pipeline").dataset()
data.stream_contents.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 Inoreader 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

• Symptom: 403 response when sending AppId/AppKey → Cause: invalid AppId/AppKey or missing credentials. Ensure AppId and AppKey are sent either as headers (AppId, AppKey) or as query parameters. For user‑scoped endpoints also ensure a valid OAuth2 Bearer token is sent in the Authorization header.

Rate limits

• Symptom: 429 Too Many Requests → Cause: exceeded rate limits for your zone or app. Retry after a backoff. App‑level rate limits apply; ensure you batch requests and respect continuation pagination.

Pagination / continuation

• Stream endpoints return a continuation string in the JSON response when more items are available — include that in the continuation parameter for the next request. If continuation is missing, the stream end has been reached.

Common HTTP errors

• 401 Unauthorized: missing/invalid OAuth2 token for user‑scoped endpoints.
• 403 Forbidden: invalid App credentials or app not allowed (free users cannot access API).
• 429 Too Many Requests: rate limiting.
• 400 Bad Request: malformed parameters (encode streamId; streamId must be URL‑encoded).

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