Lessannoyingcrm Python API Docs | dltHub

LessAnnoyingCRM is a simple CRM platform providing REST API access to contacts, companies, tasks, events, notes, pipelines, calendars, users and related CRM data. The REST API base URL is https://api.lessannoyingcrm.com/v2/ and API supports API Key authorization (header) and OAuth2 (integration partners)..

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 Lessannoyingcrm data in under 10 minutes.

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What data can I load from Lessannoyingcrm?

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

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How do I authenticate with the Lessannoyingcrm API?

For API key usage include the API key value in the Authorization HTTP header (Authorization: YOUR_API_KEY_HERE). OAuth2 (authorization code grant) is supported; include the access token as a Bearer token (Authorization: Bearer ACCESS_TOKEN_HERE).

1. Get your credentials

1. Log into your Less Annoying CRM account. 2) Open the Programmer API settings page at https://account.lessannoyingcrm.com/app/Settings/Api. 3) Create a new API key (or view existing active keys). 4) Store the generated key securely – keys are encrypted and cannot be retrieved after creation; if lost, create a new one. For OAuth: apply to the Integration Partner program, receive a ClientId and ClientSecret, and follow the OAuth authorization code flow as documented.

2. Add them to .dlt/secrets.toml

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[sources.lessannoyingcrm_source]
api_key = "your_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.

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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 Lessannoyingcrm 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 lessannoyingcrm_pipeline.py

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

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Pipeline lessannoyingcrm_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset lessannoyingcrm_data
The duckdb destination used duckdb:/lessannoyingcrm.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 lessannoyingcrm_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 tasks from the Lessannoyingcrm 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 lessannoyingcrm_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.lessannoyingcrm.com/v2/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "v2/", "data_selector": "Results"}},
            {"name": "tasks", "endpoint": {"path": "v2/", "data_selector": "Results"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM lessannoyingcrm_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("lessannoyingcrm_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 Lessannoyingcrm 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 HTTP 400 or 401 errors, ensure you are sending the API key in the Authorization header (Authorization: YOUR_API_KEY_HERE). API keys are encrypted and cannot be retrieved; if lost create a new key via Programmer API settings. Admins can disable API access for an account which will deactivate keys.

OAuth token errors

Token endpoint errors return 4XX responses with error messages. Access tokens expire (expires_in ~3600 sec) and refresh tokens are single-use; exchange refresh tokens at the token endpoint and handle token refresh errors accordingly.

API call format and errors

All API calls are made as POST requests to https://api.lessannoyingcrm.com/v2/ with JSON body {"Function":"FunctionName","Parameters":{...}}. The API returns error codes and descriptions (e.g. an error object with ErrorCode and ErrorDescription in 400 responses). Inspect HTTP status codes (400 for bad requests) and the returned JSON error fields for debugging.

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