Planview Community Python API Docs | dltHub

Planview Community API is a REST interface for accessing AdaptiveWork and Portfolios data. The REST API base URL is https://apie1.clarizen.com and All requests require a Bearer token for authentication..

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 Planview Community data in under 10 minutes.

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What data can I load from Planview Community?

Here are some of the endpoints you can load from Planview Community:

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How do I authenticate with the Planview Community API?

Authentication is performed with an API token sent in the Authorization: Bearer <token> header.

1. Get your credentials

1. Log in to the Planview Success Center with your account.
2. Navigate to Administration → API Access.
3. Click Create New Token, give it a name, and set required scopes.
4. Copy the generated token; it will be shown only once.
5. Store the token securely for use in the api_key (or token) parameter of the dlt source.

2. Add them to .dlt/secrets.toml

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[sources.planview_community_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 Planview Community 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 planview_community_pipeline.py

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

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Pipeline planview_community_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset planview_community_data
The duckdb destination used duckdb:/planview_community.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 planview_community_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 work and query from the Planview Community 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 planview_community_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://apie1.clarizen.com",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "work", "endpoint": {"path": "public-api/v1/work"}},
            {"name": "query", "endpoint": {"path": "v2.0/services/data/query"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="planview_community_pipeline",
        destination="duckdb",
        dataset_name="planview_community_data",
    )
    load_info = pipeline.run(planview_community_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("planview_community_pipeline").dataset()
sessions_df = data.work.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM planview_community_data.work LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("planview_community_pipeline").dataset()
data.work.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 Planview Community 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

• Symptom: 401 Unauthorized response.
• Cause: Missing or invalid Bearer token.
• Resolution: Verify that the token supplied in the Authorization header is correct and has not expired.

Rate limits

• Symptom: 429 Too Many Requests after rapid calls.
• Cause: Exceeding the global limit of 25 requests per second or the daily quota of 1,000 calls per paid license.
• Resolution: Implement client‑side throttling to stay under 25 req/s and monitor daily usage.

Request size / batch limits

• Symptom: 400 Bad Request when sending large payloads.
• Cause: Exceeding the 100‑item batch limit or 25 MB request size.
• Resolution: Split large queries into smaller batches.

Filter syntax errors

• Symptom: 400 Bad Request with message about unsupported filter.
• Cause: Using filters other than project.id.eq xxxx on the Work endpoint.
• Resolution: Restrict filters to the supported project.id.eq syntax as documented.

Condition limits

• Symptom: Error code General with message CZQL query has a maximum limit of 200 conditions.
• Cause: Including more than 200 logical conditions in a single query.
• Resolution: Simplify queries or break them into multiple calls.

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