Help Scout Python API Docs | dltHub

Help Scout is a customer service platform providing Docs and Inbox APIs to read and write knowledge base articles and mailbox/conversation data. The REST API base URL is Docs API: https://docsapi.helpscout.net/v1/; Inbox (Mailbox) API v2: https://api.helpscout.net/v2 and Docs API uses HTTP Basic with API key in username; Inbox API uses OAuth2 Bearer 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 Help Scout data in under 10 minutes.

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What data can I load from Help Scout?

Here are some of the endpoints you can load from Help Scout:

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How do I authenticate with the Help Scout API?

Docs API: pass API key via HTTP Basic auth as username (password can be dummy). Inbox API: use OAuth2 and include Authorization: Bearer <access_token> header.

1. Get your credentials

1. Docs API: Log into Help Scout web app > click profile (person icon) > Authentication > API Keys > generate/copy API key.
2. Inbox API: In Help Scout > Your Profile > My apps > Create My App to obtain client ID/secret and perform OAuth2 flows to get access token.

2. Add them to .dlt/secrets.toml

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[sources.help_scout_source]
# For Docs API (basic auth API key)
api_key = "YOUR_DOCS_API_KEY"
# For Inbox API (OAuth2 bearer token)
access_token = "YOUR_OAUTH2_ACCESS_TOKEN"

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 Help Scout 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 help_scout_pipeline.py

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

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Pipeline help_scout_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset help_scout_data
The duckdb destination used duckdb:/help_scout.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 help_scout_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 conversations and articles from the Help Scout 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 help_scout_source(For Docs API use 'api_key'; for Inbox API use 'access_token'=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Docs API: https://docsapi.helpscout.net/v1/; Inbox (Mailbox) API v2: https://api.helpscout.net/v2",
            "auth": {
                "type": "Docs API: http_basic; Inbox API: bearer",
                "Docs API: api_key (used as username in Basic); Inbox API: token": For Docs API use 'api_key'; for Inbox API use 'access_token',
            },
        },
        "resources": [
            {"name": "conversations", "endpoint": {"path": "conversations", "data_selector": "conversations"}},
            {"name": "articles", "endpoint": {"path": "v1/articles", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="help_scout_pipeline",
        destination="duckdb",
        dataset_name="help_scout_data",
    )
    load_info = pipeline.run(help_scout_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("help_scout_pipeline").dataset()
sessions_df = data.conversations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM help_scout_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("help_scout_pipeline").dataset()
data.conversations.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 Help Scout 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

Docs API: 401 returned if API key missing/invalid; ensure API key is used as Basic auth username and include dummy password (e.g., X). Inbox API: 401/403 for invalid/expired tokens; refresh OAuth2 token.

Rate limiting

Docs API: limits depend on number of Docs sites (e.g., 2000/10min for 1 site). Responses include X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset headers. 429 returned when exceeded.

Pagination and response envelopes

Docs API returns collections envelope with fields: page, pages, count, items (items is the array of records). Maximum 50 records per page. Mailbox API v2 uses JSON responses with resource-specific list keys (e.g., "conversations", "mailboxes", "customers"). Use provided pagination links in responses to iterate.

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