Plangrid Python API Docs | dltHub

PlanGrid is a construction productivity platform providing access to plan sheets, documents, photos, tasks, RFIs and other field data via a REST API. The REST API base URL is https://io.plangrid.com and API supports either API key (Basic auth header) or OAuth2 (Bearer token) depending on app type..

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 Plangrid data in under 10 minutes.

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What data can I load from Plangrid?

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

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How do I authenticate with the Plangrid API?

Requests require an Authorization header. For API key usage send Authorization: Basic <base64(APIKey+:)> (API key as username, empty password) and include Accept: application/vnd.plangrid+json; version=1. For OAuth use Authorization: Bearer <access_token> per the Authorization Code grant documentation.

1. Get your credentials

1. Sign into PlanGrid/Autodesk account and go to developer portal at https://developer.plangrid.com.
2. For simple integrations request/generate an API key (or set via account settings or environment variable PLANGRIDAPIKEY).
3. For OAuth2 apps register an application to obtain client_id and client_secret and follow the Authorization Code grant documentation to get access/refresh tokens.

2. Add them to .dlt/secrets.toml

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[sources.plangrid_source]
api_key = "your_plangrid_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 Plangrid 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 plangrid_pipeline.py

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

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Pipeline plangrid_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset plangrid_data
The duckdb destination used duckdb:/plangrid.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 plangrid_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 sheets from the Plangrid 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 plangrid_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://io.plangrid.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "projects", "endpoint": {"path": "projects", "data_selector": "projects"}},
            {"name": "sheets", "endpoint": {"path": "projects/{project_id}/sheets", "data_selector": "sheets"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM plangrid_data.projects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("plangrid_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 Plangrid 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 Unauthorized verify your Authorization header. For API key use Authorization: Basic <base64(APIKey+:)>. Ensure the Accept header is set to application/vnd.plangrid+json; version=1. For OAuth check token expiry and refresh.

Rate limits and throttling

PlanGrid enforces rate limiting on its API endpoints. If you receive 429 Too Many Requests back off and retry after the delay indicated in Retry-After header. Use pagination to reduce request volume.

Pagination

List endpoints are paginated. Use skip and limit query parameters (or the API-provided pagination fields) to page through results. Handle empty pages and stop when fewer records than requested are returned.

Common error responses

400 Bad Request — malformed request or invalid parameters. 401 Unauthorized — invalid credentials. 403 Forbidden — insufficient permissions for the resource. 404 Not Found — resource id not found. 429 Too Many Requests — rate limit exceeded. 5xx — server errors, retry with backoff.

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