PandaDoc Python API Docs | dltHub

PandaDoc is a document automation platform to generate, send, sign, and manage documents via a REST API. The REST API base URL is https://api.pandadoc.com/public/v1 and All requests require API authentication (API key or OAuth2 access token)..

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 PandaDoc data in under 10 minutes.

---

What data can I load from PandaDoc?

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

---

How do I authenticate with the PandaDoc API?

PandaDoc supports API Key authentication for server‑to‑server calls and OAuth 2.0 for user‑scoped integrations. Include the key or bearer token in the Authorization header (e.g., "Authorization: Bearer ").

1. Get your credentials

1. Sign in to PandaDoc (or create a sandbox at signup.pandadoc.com). 2) Open the Developer Dashboard (app.pandadoc.com/a/#/api-dashboard/configuration). 3) For an API key: create a new API Key in the API Keys section. 4) For OAuth: create an application, note the client_id and client_secret, and follow the OAuth authorization code flow to obtain access tokens.

2. Add them to .dlt/secrets.toml

Copy
[sources.panda_doc_source]
api_key = "your_pandadoc_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 PandaDoc 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:

Copy
python panda_doc_pipeline.py

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

Copy
Pipeline panda_doc_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset panda_doc_data
The duckdb destination used duckdb:/panda_doc.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline panda_doc_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.

---

Python pipeline example

This example loads documents and templates from the PandaDoc API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def panda_doc_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.pandadoc.com/public/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "documents", "endpoint": {"path": "documents", "data_selector": "results"}},
            {"name": "templates", "endpoint": {"path": "templates", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="panda_doc_pipeline",
        destination="duckdb",
        dataset_name="panda_doc_data",
    )
    load_info = pipeline.run(panda_doc_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.

---

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):

Copy
import dlt

data = dlt.pipeline("panda_doc_pipeline").dataset()
sessions_df = data.documents.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM panda_doc_data.documents LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("panda_doc_pipeline").dataset()
data.documents.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load PandaDoc 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.

---

Troubleshooting

Authentication failures

If you receive 401 Unauthorized or errors referencing invalid credentials, verify the API key or OAuth token and that you are using the correct environment (sandbox vs production). Ensure the Authorization header is set and the token has not expired.

Rate limits and 429 responses

PandaDoc enforces rate limiting. If you receive 429 Too Many Requests, back off and retry after the period suggested in the Retry-After header. Implement exponential backoff.

Pagination quirks

List endpoints are paginated. Use query parameters page and page_size (or offset/limit depending on the endpoint) and iterate pages until the results array is empty. Check response metadata for total or next page indicators.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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