Load ActivityWatch data to DuckDB

ActivityWatch is a privacy-focused open-source automated time tracker that uses a REST API for communication between the server and clients. Everything needed to build a working ActivityWatch → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your ActivityWatch to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

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Run uvx dlthub-init@latest to build a pipeline from ActivityWatch to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the ActivityWatch API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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ActivityWatch API at a glance

These values come from the ActivityWatch API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the ActivityWatch API?

When the server is configured with token authentication, requests must include an 'Authorization' header with the value 'Bearer '. Authentication is optional and opt-in by the user in the server configuration.

1. Get your credentials

1. Locate your ActivityWatch configuration file, typically named config.toml. On Linux, this is often found in ~/.config/activitywatch/aw-server/config.toml (or similar XDG-compliant paths). On other platforms, check the ActivityWatch data directory.\n2. Open the file in a text editor.\n3. Add an [auth] section if it does not already exist.\n4. Define an api_key field under the [auth] section with your desired secure string (e.g., 'api_key = "your-secret-key-here"').\n5. Save the file and restart the ActivityWatch server to apply the changes. This key will now be required for all authenticated requests via the Authorization: Bearer <api_key> header.

2. Add them to .dlt/secrets.toml

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[sources.activitywatch_source]
auth_token = "REPLACE_ME"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What ActivityWatch data can I load into DuckDB?

These are the ActivityWatch endpoints dlt can load into DuckDB:

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How do I load only new ActivityWatch records?

The ActivityWatch API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "buckets", "endpoint": {
    "path": "api/0/buckets/",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated ActivityWatch pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/0/info and /api/0/buckets from the ActivityWatch API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def activitywatch_source(auth_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:5600/api",
            "auth": {"type": "bearer", "token": auth_token},
        },
        "resources": [
            {"name": "buckets", "endpoint": {"path": "api/0/buckets/"}},
            {"name": "events", "endpoint": {"path": "api/0/buckets/{bucket_id}/events"}}
        ],
    }
    yield from rest_api_resources(config)


def load_activitywatch_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="activitywatch_pipeline",
        destination="duckdb",
        dataset_name="activitywatch_data",
    )
    load_info = pipeline.run(activitywatch_source())
    print(load_info)


if __name__ == "__main__":
    load_activitywatch_to_duckdb()

Run it with python activitywatch_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query ActivityWatch data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("activitywatch_pipeline").dataset()
df = data.buckets.df()
print(df.head())

SQL:

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SELECT * FROM activitywatch_data.buckets LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the ActivityWatch to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw ActivityWatch loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load ActivityWatch data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack