Openproject Python API Docs | dltHub

OpenProject is a project management and collaboration platform exposing a RESTful API (APIv3) providing access to projects, work packages, users, documents and related resources. The REST API base URL is https://<your-openproject-host>/api/v3 and All requests requiring authentication typically use Basic auth with username 'apikey' and the API key as password; OAuth2 and Bearer JWT are also supported..

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 Openproject data in under 10 minutes.

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What data can I load from Openproject?

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

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How do I authenticate with the Openproject API?

OpenProject supports API key via HTTP Basic auth (username 'apikey' and the API key as password), OAuth2, session‑based authentication, and JWT bearer tokens via the Authorization header.

1. Get your credentials

1. Log into your OpenProject web UI.
2. Open your user account → My account page.
3. Locate the API access or API key section and generate a new API key.
4. Copy the key and store it in your secrets.toml as api_key.
5. Use Basic auth with username 'apikey' and the key as password, or configure OAuth2/OIDC and use a Bearer token if preferred.

2. Add them to .dlt/secrets.toml

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[sources.openproject_source]
api_key = "your_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 Openproject 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 openproject_pipeline.py

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

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Pipeline openproject_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset openproject_data
The duckdb destination used duckdb:/openproject.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 openproject_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 work_packages from the Openproject 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 openproject_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your-openproject-host>/api/v3",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "projects", "data_selector": "_embedded.elements"}},
            {"name": "work_packages", "endpoint": {"path": "work_packages", "data_selector": "_embedded.elements"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="openproject_pipeline",
        destination="duckdb",
        dataset_name="openproject_data",
    )
    load_info = pipeline.run(openproject_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("openproject_pipeline").dataset()
sessions_df = data.projects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM openproject_data.projects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("openproject_pipeline").dataset()
data.projects.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 Openproject 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, ensure you use Basic auth with username apikey and the API key as password (e.g., curl -u apikey:YOUR_KEY https://host/api/v3/projects) or provide a valid Authorization: Bearer {jwt} header for OIDC/OAuth2. Session‑based requests also require the X-Requested-With: XMLHttpRequest header.

Pagination quirks

List endpoints are paginated; results appear in _embedded.elements. Use query parameters page and per_page or follow the _links.next URL to retrieve subsequent pages.

Rate limits and errors

OpenProject may enforce rate limiting (HTTP 429). Other common error codes include 401 (Unauthorized), 403 (Forbidden), 404 (Not Found), and 422 (Unprocessable Entity). Inspect the response body for an error or message field for details.

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