Bitly Python API Docs | dltHub

Bitly is a link management platform that provides services for shortening URLs, tracking link performance, and managing branded links. The REST API base URL is https://api-ssl.bitly.com/v4 and All requests require a Bearer token for authentication, which can be a Generic Access Token or obtained via OAuth 2.0..

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 Bitly data in under 10 minutes.

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What data can I load from Bitly?

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

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How do I authenticate with the Bitly API?

Authentication is done using a Bearer token. All requests must include an 'Authorization' header with the format 'Authorization: Bearer {token}'.

1. Get your credentials

To obtain API credentials, log in to your Bitly account, navigate to the developer or API settings section, and generate a Generic Access Token. This token will be used in the Authorization header for your API requests.

2. Add them to .dlt/secrets.toml

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[sources.bitly_source]
access_token = "your_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 Bitly 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 bitly_pipeline.py

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

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Pipeline bitly_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bitly_data
The duckdb destination used duckdb:/bitly.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 bitly_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 groups and group_bitlinks from the Bitly 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 bitly_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api-ssl.bitly.com/v4",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "groups", "endpoint": {"path": "groups", "data_selector": "groups"}},
            {"name": "group_bitlinks", "endpoint": {"path": "groups/{group_guid}/bitlinks", "data_selector": "links"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bitly_pipeline",
        destination="duckdb",
        dataset_name="bitly_data",
    )
    load_info = pipeline.run(bitly_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("bitly_pipeline").dataset()
sessions_df = data.groups.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bitly_data.groups LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bitly_pipeline").dataset()
data.groups.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 Bitly 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 Errors

• 401 Unauthorized: This error indicates that your API request lacks valid authentication credentials. Ensure your Generic Access Token is correctly included in the Authorization: Bearer {token} header.
• 403 Forbidden: This error suggests that your authenticated user does not have the necessary permissions to access the requested resource or perform the action. Verify the scope of your access token or user permissions.

Resource Not Found

• 404 Not Found: This error occurs when the requested resource (e.g., a bitlink, group, or user) does not exist or the URL path is incorrect. Double-check the resource identifiers and endpoint paths.

Rate Limiting and Usage Limits

• 429 Too Many Requests: Bitly enforces rate limits and monthly usage limits. If you exceed these limits, you will receive a 429 error. Review your API usage and consider implementing exponential backoff for retries.

Server Errors

• 500/503 Server Errors: These errors indicate an issue on the Bitly server side. These are typically temporary and can often be resolved by retrying the request after a short delay.

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