Quip Python API Docs | dltHub

Quip is a collaborative productivity platform (documents, spreadsheets, chat) with REST APIs for Automation and Admin functions. The REST API base URL is https://platform.quip.com and all requests require OAuth2 access tokens (Bearer) or Personal Access Tokens.

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 Quip data in under 10 minutes.

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What data can I load from Quip?

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

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How do I authenticate with the Quip API?

Quip uses OAuth2 (authorization code / client credentials via API keys) and accepts tokens in the Authorization header as "Bearer {{token}}". Admin APIs require API keys created in the Quip Admin Console to obtain access tokens; Personal Access Tokens can be used for testing.

1. Get your credentials

1. In Quip Admin Console go to API Keys (Admin > API keys). 2) Create an API key, note client_id and client_secret. 3) Exchange client_id/client_secret at Token URL https://platform.quip.com/1/oauth/access_token (OAuth flow) to obtain an access token. 4) (Optional) Create a Personal Access Token in developer settings for quick testing.

2. Add them to .dlt/secrets.toml

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[sources.quip_source]
access_token = "your_oauth_or_personal_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 Quip 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 quip_pipeline.py

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

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Pipeline quip_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset quip_data
The duckdb destination used duckdb:/quip.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 quip_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 threads_get and threads_export_status from the Quip 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 quip_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://platform.quip.com",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "threads_get", "endpoint": {"path": "1/threads/{thread_id}"}},
            {"name": "threads_export_status", "endpoint": {"path": "1/threads/export/async", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="quip_pipeline",
        destination="duckdb",
        dataset_name="quip_data",
    )
    load_info = pipeline.run(quip_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("quip_pipeline").dataset()
sessions_df = data.threads_get.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM quip_data.threads_get LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("quip_pipeline").dataset()
data.threads_get.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 Quip 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

Ensure Authorization header: "Authorization: Bearer ". Tokens must be obtained via the OAuth token endpoint using an API key (client_id/client_secret) or use a valid Personal Access Token. 401 indicates invalid/expired token.

Rate limits

Quip enforces per-user and per-company rate limits (defaults: ~100 req/min per user, 600 req/min per company for Admin APIs). Monitor X-Ratelimit-Limit, X-Ratelimit-Remaining, and X-Ratelimit-Reset response headers and implement exponential backoff on 429 responses.

Pagination and search limits

Search and events endpoints return pagination fields such as more_to_read, next_page, next_cursor or response_metadata.next_cursor. Use those to fetch additional pages. Some admin search endpoints have additional pagination‑specific rate limits.

Common error responses

Quip uses standard HTTP codes (400,401,403,404,429,500). Error bodies often contain {"error": "string","error_code": ,"error_description": "string"}.

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