noCRM.io Python API Docs | dltHub

noCRM.io is a full REST API available for customers using the Expert and Dream editions, allowing integration with their CRM data. The REST API base URL is https://{subdomain}.nocrm.io/api/v2 and All requests require either an API key passed in the X-API-KEY header or a user token passed in the X-USER-TOKEN header for authentication..

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 noCRM.io data in under 10 minutes.

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What data can I load from noCRM.io?

Here are some of the endpoints you can load from noCRM.io:

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How do I authenticate with the noCRM.io API?

Authentication requires either an API key or a user token. The API key should be passed in the X-API-KEY header, and the user token in the X-USER-TOKEN header.

1. Get your credentials

1. Log into your noCRM account and go to Admin Panel > API Keys.
2. Click Create an API Key to generate a private API key (X-API-KEY).
3. For user-scoped actions, obtain a USER token by calling POST/GET /auth/login using basic auth (email:password) or use API key then call /auth/log_as to get a user token.
4. Use the token in request headers: X-API-KEY or X-USER-TOKEN.

2. Add them to .dlt/secrets.toml

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[sources.no_crm_io_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 noCRM.io 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 no_crm_io_pipeline.py

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

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Pipeline no_crm_io_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset no_crm_io_data
The duckdb destination used duckdb:/no_crm_io.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 no_crm_io_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 leads and rows from the noCRM.io 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 no_crm_io_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{subdomain}.nocrm.io/api/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "leads", "endpoint": {"path": "leads", "data_selector": "(not verified in scraped examples  documented as list endpoint)"}},
            {"name": "rows", "endpoint": {"path": "rows"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="no_crm_io_pipeline",
        destination="duckdb",
        dataset_name="no_crm_io_data",
    )
    load_info = pipeline.run(no_crm_io_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("no_crm_io_pipeline").dataset()
sessions_df = data.rows.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM no_crm_io_data.rows LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("no_crm_io_pipeline").dataset()
data.rows.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 noCRM.io 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 X-API-KEY or X-USER-TOKEN header is present; missing/invalid token returns 401. For user token, use basic auth to /auth/login to get a token.

Rate limits (quota)

The API enforces a daily quota of 2000 requests; exceeding it returns 429 Too Many Requests and headers API-RETRY-AFTER and API-LIMIT-RESET.

Pagination / list responses

Many list endpoints return top-level arrays (examples: rows, webhooks, attachments). Verify response shape for each endpoint in your account  if paginated behavior appears, follow provided pagination parameters (not shown in scraped excerpts).

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