Rescuetime Python API Docs | dltHub

RescueTime is a personal and team time-tracking and productivity analytics platform that exposes an Analytic Data API (and feed/resource APIs) to query activity logs, productivity metrics, daily summaries, alerts, highlights, and focus‑session events. The REST API base URL is https://www.rescuetime.com/anapi and API key (or OAuth2 access_token) required on all requests..

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 Rescuetime data in under 10 minutes.

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What data can I load from Rescuetime?

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

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How do I authenticate with the Rescuetime API?

Requests must include either the user's API key (query parameter key=YOUR_API_KEY) for personal/personal‑app access, or an OAuth2 access_token (query parameter access_token=YOUR_TOKEN) for OAuth connections.

1. Get your credentials

1. Log in to your RescueTime account at https://www.rescuetime.com/.
2. Navigate to the developer / key management page ("API Keys" or "Key Management" in account settings or https://www.rescuetime.com/rtx/developers).
3. Create a new API key; copy the generated key string.
4. Use the key value as the query parameter key=YOUR_API_KEY on API requests, or create an OAuth2 application with RescueTime for access tokens if building a web integration.

2. Add them to .dlt/secrets.toml

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[sources.rescuetime_source]
api_key = "your_rescuetime_api_key_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 Rescuetime 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 rescuetime_pipeline.py

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

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Pipeline rescuetime_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset rescuetime_data
The duckdb destination used duckdb:/rescuetime.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 rescuetime_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 data and daily_summary_feed from the Rescuetime 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 rescuetime_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.rescuetime.com/anapi",
            "auth": {
                "type": "api_key",
                "key": api_key,
            },
        },
        "resources": [
            {"name": "analytic_data", "endpoint": {"path": "data", "data_selector": "rows"}},
            {"name": "daily_summary_feed", "endpoint": {"path": "daily_summary_feed"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="rescuetime_pipeline",
        destination="duckdb",
        dataset_name="rescuetime_data",
    )
    load_info = pipeline.run(rescuetime_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("rescuetime_pipeline").dataset()
sessions_df = data.analytic_data.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM rescuetime_data.analytic_data LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("rescuetime_pipeline").dataset()
data.analytic_data.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 Rescuetime 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 responses return HTTP 401 or empty results: verify you passed key=YOUR_API_KEY (for API key access) or access_token=YOUR_OAUTH_TOKEN for OAuth endpoints. API keys can be revoked; regenerate in the account key management page.

Rate limits and data sync delays

RescueTime syncs activity data on intervals (premium: every ~3 minutes; free: ~30 minutes). If newly‑recorded desktop activity doesn't appear, wait for the next sync. The public docs do not specify strict numeric rate limits—treat queries responsibly and paginate/timebox large queries.

Pagination and response shape

The Analytic Data API (data endpoint) returns a JSON envelope with keys notes, row_headers, and rows; rows is the array of data (data_selector: rows). Other feed endpoints return top‑level arrays of objects. For CSV format use format=csv instead of json.

Common API errors:

• 400 Bad Request: malformed parameters or missing required fields (e.g., POST highlights_post missing description).
• 401 Unauthorized: invalid or missing API key/access_token.
• 403 Forbidden: request requires premium feature or scope (e.g., highlights or alerts for Lite plan returns zero results or forbidden behavior).
• 429 Too Many Requests: inferred if excessive querying; back off and retry.

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