Contacts-plus Python API Docs | dltHub

Contacts+ is a cross‑platform contact management platform providing a REST API to access and manage user and team contacts, tags, teams, webhooks and related objects. The REST API base URL is https://api.contactsplus.com and OAuth 2.0 Bearer token required for all requests..

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 Contacts-plus data in under 10 minutes.

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What data can I load from Contacts-plus?

Here are some of the endpoints you can load from Contacts-plus:

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How do I authenticate with the Contacts-plus API?

The API uses OAuth2 authorization code and refresh token flows. After obtaining an access_token, include it in the request header as Authorization: Bearer <access_token>.

1. Get your credentials

1. Sign in at https://app.contactsplus.com.
2. Open https://app.contactsplus.com/apps to register a new application.
3. Record client_id and client_secret and configure a redirect URI and requested scopes (e.g. contacts.read, contacts.write).
4. Use the /oauth/authorize endpoint to obtain an authorization code and exchange it at /v3/oauth.refreshToken to obtain access_token and refresh_token.
5. Use access_token in Authorization header for API calls.

2. Add them to .dlt/secrets.toml

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[sources.contacts_plus_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
refresh_token = "your_refresh_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 Contacts-plus 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 contacts_plus_pipeline.py

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

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Pipeline contacts_plus_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset contacts_plus_data
The duckdb destination used duckdb:/contacts_plus.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 contacts_plus_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 tags from the Contacts-plus 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 contacts_plus_source(client_id, client_secret, refresh_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.contactsplus.com",
            "auth": {
                "type": "bearer",
                "access_token": client_id, client_secret, refresh_token,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "api/v1/contacts.get", "data_selector": "contacts"}},
            {"name": "tags", "endpoint": {"path": "api/v1/tags.get", "data_selector": "tags"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM contacts_plus_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("contacts_plus_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 Contacts-plus 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 errors

If you receive 401/403 responses, verify your access_token is valid and not expired. Use the refresh_token flow to obtain a new access_token. Ensure the Authorization header is exactly: Authorization: Bearer <access_token> and that requested scopes include the resource (e.g. contacts.read).

Pagination and scrolling

Many large‑list endpoints use a scroll cursor. Responses include a cursor or scrollCursor string when more pages exist; provide that value as searchCursor/scrollCursor in the next request to retrieve the next page.

Rate limits and retries

The public docs do not publish explicit rate limit numbers. Implement exponential backoff and retries for 429 responses and 5xx server errors.

Common error responses

• 400 Bad Request: invalid payload or parameters.
• 401 Unauthorized: invalid/missing token.
• 403 Forbidden: insufficient scopes or access to team data.
• 404 Not Found: resource does not exist.
• 429 Too Many Requests: rate limit exceeded — retry after backoff.
• 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