Pivotal Tracker Python API Docs | dltHub

Pivotal Tracker is a project management and agile tracking platform exposing a REST API to create, read, update, and delete projects, stories, epics, iterations, memberships, activity, labels and related resources. The REST API base URL is https://www.pivotaltracker.com/services/v5 and all requests require a user API token (X-TrackerToken header or token query param).

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 Pivotal Tracker data in under 10 minutes.

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What data can I load from Pivotal Tracker?

Here are some of the endpoints you can load from Pivotal Tracker:

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How do I authenticate with the Pivotal Tracker API?

Authenticate by passing the user's API token in the HTTP header X-TrackerToken: (or as token= query parameter). You can also obtain the token by calling GET /me using HTTP Basic auth (username:password) which returns the token in the response.

1. Get your credentials

1. Sign in to Pivotal Tracker (web UI).
2. Open your Profile (usually via avatar → Profile or Account settings).
3. Locate the API token value on your Profile page and copy it.
4. Use this token in the X-TrackerToken header for API requests (or token query param).

2. Add them to .dlt/secrets.toml

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[sources.pivotal_tracker_source]
api_token = "your_pivotal_tracker_api_token"

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 Pivotal Tracker 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 pivotal_tracker_pipeline.py

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

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Pipeline pivotal_tracker_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pivotal_tracker_data
The duckdb destination used duckdb:/pivotal_tracker.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 pivotal_tracker_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 projects and stories from the Pivotal Tracker 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 pivotal_tracker_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.pivotaltracker.com/services/v5",
            "auth": {
                "type": "api_key",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "projects"}},
            {"name": "stories", "endpoint": {"path": "projects/{project_id}/stories"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pivotal_tracker_pipeline",
        destination="duckdb",
        dataset_name="pivotal_tracker_data",
    )
    load_info = pipeline.run(pivotal_tracker_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("pivotal_tracker_pipeline").dataset()
sessions_df = data.stories.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pivotal_tracker_data.stories LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pivotal_tracker_pipeline").dataset()
data.stories.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 Pivotal Tracker 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 see HTTP 401 (unauthenticated) the request lacked credentials. Ensure X-TrackerToken header is present and valid or include token query param. Calling GET /me with Basic auth can be used to exchange username/password for a token; invalid basic auth returns 403.

Authorization errors (403)

HTTP 403 with code unauthorized_operation indicates the token is valid but the user lacks permission for the requested resource (e.g., not a member of the project or project API access disabled). Check project membership and roles.

Not found (404)

A 404 returns code unfound_resource and means the referenced resource id does not exist or is not visible to the authenticated user.

Validation / bad request (400)

Bad inputs return 400 with code invalid_parameter and may include validation_errors array with per-field issues.

Pagination and envelope behavior

List endpoints return pagination via response headers: X-Tracker-Pagination-Limit, -Offset, -Total, -Returned. If you pass envelope=true the response body will include a top-level envelope with data (the actual records) and a pagination object. By default collection responses are top-level arrays (data selector is the top-level array).

Rate limiting and retries

The public docs do not document a strict rate‑limit header; implement exponential backoff on 429/5xx and respect 4xx errors. Monitor HTTP response headers for any provider‑specific guidance (project-version, downtime, deprecation warnings).

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