Feedlyboard Python API Docs | dltHub

Feedly is a cloud‑based RSS/news aggregation platform and API that provides programmatic access to feeds, articles, user profile, subscriptions, streams and team boards. The REST API base URL is https://api.feedly.com/v3 and All requests require an OAuth 2.0 Bearer token in the 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 Feedlyboard data in under 10 minutes.

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What data can I load from Feedlyboard?

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

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How do I authenticate with the Feedlyboard API?

Feedly uses OAuth2 access tokens. Include header: Authorization: OAuth <access_token> (or Authorization: Bearer where supported).

1. Get your credentials

1. Sign in to your Feedly account.
2. Go to the Feedly developer / API access / self‑service token page (Feedly account settings or developers.feedly.com Authorization docs).
3. Create or copy your generated API access token (or follow the OAuth flow to obtain authorization_code and exchange it at POST /v3/auth/token).
4. Store the access token securely and use it in the Authorization header for API calls.

2. Add them to .dlt/secrets.toml

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[sources.feedlyboard_source]
access_token = "your_feedly_oauth_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 Feedlyboard 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 feedlyboard_pipeline.py

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

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Pipeline feedlyboard_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset feedlyboard_data
The duckdb destination used duckdb:/feedlyboard.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 feedlyboard_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 streams_contents and subscriptions from the Feedlyboard 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 feedlyboard_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.feedly.com/v3",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "streams_contents", "endpoint": {"path": "v3/streams/contents?streamId={streamId}", "data_selector": "items"}},
            {"name": "subscriptions", "endpoint": {"path": "v3/subscriptions"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="feedlyboard_pipeline",
        destination="duckdb",
        dataset_name="feedlyboard_data",
    )
    load_info = pipeline.run(feedlyboard_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("feedlyboard_pipeline").dataset()
sessions_df = data.streams_contents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM feedlyboard_data.streams_contents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("feedlyboard_pipeline").dataset()
data.streams_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 Feedlyboard 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 Unauthorized or 403 Forbidden ensure the Authorization header is present and the token is valid and not expired. Feedly expects OAuth tokens generated via the Feedly self‑service page or via the OAuth /v3/auth/token exchange. Use "Authorization: OAuth " or "Authorization: Bearer " as required.

Rate limiting

Feedly enforces request limits (examples and tooling indicate limits such as ~1000 requests/hour for some products). If you receive 429 Too Many Requests, back off and retry after a pause. Implement exponential backoff and respect continuation‑based pagination to reduce requests.

Pagination and continuation

List endpoints that support pagination (streams/contents) return a continuation token in the response (field: continuation) and the items array under the items key. Use the continuation query parameter to fetch the next page.

Common error responses

• 400 Bad Request — invalid parameters
• 401/403 — missing/invalid token
• 404 Not Found — invalid resource id (feed or entry not found)
• 429 Too Many Requests — rate limit exceeded

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