Kantata Python API Docs | dltHub

Kantata is a project and resource management platform (formerly Mavenlink) providing a RESTful API for accessing workspaces, projects, tasks, users, time tracking, invoices and related resources. The REST API base URL is https://api.mavenlink.com/api/v1/ and all requests require an OAuth2 Bearer token (Authorization: Bearer ).

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 Kantata data in under 10 minutes.

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What data can I load from Kantata?

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

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How do I authenticate with the Kantata API?

Kantata uses OAuth2; obtain an access token via the OAuth authorization code flow and include it on every request in the Authorization header as: Authorization: Bearer <access_token>.

1. Get your credentials

1. Sign in to Kantata developer portal (https://developer.kantata.com/) and register a new application to receive a client_id and client_secret.
2. Direct users to the OAuth authorize endpoint (/oauth/authorize) with client_id, response_type=code and redirect_uri to obtain an authorization code.
3. Exchange the authorization code for an access token by POSTing to https://app.mavenlink.com/oauth/token with client_id, client_secret, grant_type, code and redirect_uri.
4. Store the returned access_token and use it in the Authorization header for API requests.

2. Add them to .dlt/secrets.toml

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[sources.kantata_source]
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 Kantata 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 kantata_pipeline.py

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

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Pipeline kantata_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset kantata_data
The duckdb destination used duckdb:/kantata.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 kantata_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 workspaces and projects from the Kantata 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 kantata_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.mavenlink.com/api/v1/",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "workspaces", "endpoint": {"path": "workspaces.json", "data_selector": "results"}},
            {"name": "projects", "endpoint": {"path": "projects.json", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="kantata_pipeline",
        destination="duckdb",
        dataset_name="kantata_data",
    )
    load_info = pipeline.run(kantata_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("kantata_pipeline").dataset()
sessions_df = data.workspaces.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM kantata_data.workspaces LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("kantata_pipeline").dataset()
data.workspaces.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 Kantata 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

Check that Authorization header is present and that the access token is valid. Invalid or revoked tokens return 401 Unauthorized. If using OAuth code flow ensure the code was exchanged at https://app.mavenlink.com/oauth/token and the returned access_token is used.

Rate limits

The API may return 429 Too Many Requests for excessive traffic. Retry after a delay and consult Kantata Knowledge Base for endpoint-specific limits (GET /workspaces and others may have limits).

Pagination and data selection

GET index endpoints return paginated results; responses include count and results keys. Use the results array as the canonical ordered list of matching records. You can paginate with page & per_page or limit & offset (limit/offset override page/per_page). Maximum per_page is 200.

Request by ID and includes

You can request specific IDs with ?only=ID1,ID2 or use RESTful show routes like /workspaces/{id}.json (which may return 404 if resource not found). Responses include related objects as top-level keyed objects; still iterate the results array to get canonical list.

Common API errors: 401 Unauthorized (invalid/missing token); 403 Forbidden (insufficient permissions); 404 Not Found (resource missing); 429 Too Many Requests (rate limit); 422 Unprocessable Entity (validation errors for POST/PUT).

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