Wrike Python API Docs | dltHub

Wrike is a collaborative work management platform that provides a REST API for accessing projects, tasks, folders, and other resources. The REST API base URL is https://www.wrike.com/api/v4 and All requests require an OAuth 2.0 Bearer token in the Authorization header or as an access_token query parameter..

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 Wrike data in under 10 minutes.

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What data can I load from Wrike?

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

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How do I authenticate with the Wrike API?

Wrike uses OAuth 2.0; include the header Authorization: Bearer <access_token> on each request, or alternatively supply access_token=<access_token> as a query parameter.

1. Get your credentials

1. Log in to your Wrike account.
2. Open the Wrike Developer Console (https://www.wrike.com/developerconsole).
3. Create a new application – provide a name, description, and a redirect URI.
4. After creation, note the Client ID and Client Secret.
5. Implement the OAuth 2.0 Authorization Code flow: direct the user to the authorization endpoint with your client_id and redirect URI to obtain an authorization code.
6. Exchange the authorization code for an access token by POSTing to the token endpoint (https://www.wrike.com/oauth2/token) with client_id, client_secret, grant_type=authorization_code, and the code.
7. Use the returned access_token in API calls.

2. Add them to .dlt/secrets.toml

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[sources.wrike_project_management_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 Wrike 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 wrike_project_management_pipeline.py

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

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Pipeline wrike_project_management_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset wrike_project_management_data
The duckdb destination used duckdb:/wrike_project_management.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 wrike_project_management_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 folders and tasks from the Wrike 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 wrike_project_management_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.wrike.com/api/v4",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "folders", "endpoint": {"path": "folders", "data_selector": "data"}},
            {"name": "tasks", "endpoint": {"path": "tasks", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="wrike_project_management_pipeline",
        destination="duckdb",
        dataset_name="wrike_project_management_data",
    )
    load_info = pipeline.run(wrike_project_management_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("wrike_project_management_pipeline").dataset()
sessions_df = data.folders.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM wrike_project_management_data.folders LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("wrike_project_management_pipeline").dataset()
data.folders.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 Wrike 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 – the Bearer token is missing, expired, or invalid. Verify that the Authorization: Bearer <access_token> header (or access_token query parameter) contains a valid token.
• 403 Forbidden – the token lacks required scopes for the requested resource.

Rate limiting

• 429 Too Many Requests – the API rate limit has been exceeded. The response includes a Retry-After header indicating how long to wait before retrying.

Pagination quirks

• Many list endpoints return a nextPage object with token and nextHref. Use the token (or nextHref) in the subsequent request's query parameters (pageToken or page) to retrieve the next batch of records.
• The perPage parameter controls page size (default 100, max 1000). Ensure your pipeline respects these limits to avoid 429 errors.

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