HubSpot Python API Docs | dltHub

HubSpot is a customer relationship management (CRM) platform that provides REST APIs to manage CRM objects, properties, and associations. The REST API base URL is https://api.hubapi.com and All requests require a Bearer token (OAuth or Private App) or a legacy hapikey query parameter..

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 HubSpot data in under 10 minutes.

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What data can I load from HubSpot?

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

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How do I authenticate with the HubSpot API?

HubSpot supports OAuth 2.0 and Private App tokens. Include the token in the request header as "Authorization: Bearer <ACCESS_TOKEN>". Some older endpoints require a "hapikey" query parameter.

1. Get your credentials

1. Private App token: In HubSpot, go to Settings → Integrations → Private Apps, create a new private app, assign scopes, and copy the generated access token. 2) OAuth app: In a HubSpot developer account, create an app, note the client_id and client_secret, and follow the OAuth 2.0 authorization code flow to obtain an access token. 3) Legacy API key: In the developer account overview, locate the Developer API key (hapikey) and copy it for use in query parameters.

2. Add them to .dlt/secrets.toml

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[sources.hubspot_crm_source]
api_token = "your_private_app_or_oauth_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 HubSpot 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 hubspot_crm_pipeline.py

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

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Pipeline hubspot_crm_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset hubspot_crm_data
The duckdb destination used duckdb:/hubspot_crm.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 hubspot_crm_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 contacts and companies from the HubSpot 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 hubspot_crm_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.hubapi.com",
            "auth": {
                "type": "bearer",
                "token": api_token,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "crm/v3/objects/contacts", "data_selector": "results"}},
            {"name": "companies", "endpoint": {"path": "crm/v3/objects/companies", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="hubspot_crm_pipeline",
        destination="duckdb",
        dataset_name="hubspot_crm_data",
    )
    load_info = pipeline.run(hubspot_crm_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("hubspot_crm_pipeline").dataset()
sessions_df = data.contacts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM hubspot_crm_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("hubspot_crm_pipeline").dataset()
data.contacts.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 HubSpot 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 or INVALID_AUTHENTICATION errors, verify that you’re using an active OAuth or Private App access token and providing it in the Authorization: Bearer <token> header; also ensure the token includes required scopes (e.g., crm.objects.contacts.read). Some legacy endpoints still require the hapikey query parameter.

Rate limits

HubSpot enforces rate limits; excessive requests return 429 Too Many Requests. Respect the recommended burst limits per account and inspect response headers for limit details. Implement exponential backoff and retry after the indicated delay.

Pagination

List endpoints return a JSON object with a results array and an optional paging object. Use the limit (max 100) and after (value from paging.next.after) query parameters to retrieve subsequent pages, e.g., GET /crm/v3/objects/contacts?limit=100&after=33452.

Common API errors

• 400 Bad Request – malformed payload or invalid query parameters.
• 401 Unauthorized / INVALID_AUTHENTICATION – missing or invalid token.
• 403 Forbidden – insufficient scopes or permissions.
• 404 Not Found – resource or endpoint does not exist.
• 429 Too Many Requests – rate limiting; retry after backoff. Error responses typically include JSON fields such as status, message, correlationId, and category.

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