Insightly Python API Docs | dltHub

Insightly is a RESTful CRM platform providing access to contacts, organisations, leads, opportunities, projects, tasks, notes, events, custom fields and other CRM objects via a JSON HTTP API (v3.1). The REST API base URL is https://api.{pod}.insightly.com/v3.1/ and all requests require HTTP Basic auth using the user's API key as the username (blank password)..

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 Insightly data in under 10 minutes.

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What data can I load from Insightly?

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

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How do I authenticate with the Insightly API?

Uses HTTP Basic authentication with your API key as the username (Base64-encoded) and a blank password; include as an Authorization header (Basic {base64(api_key:)}). In sandbox/test UI you can paste the API key directly.

1. Get your credentials

1. Log in to Insightly web app. 2) Open User Settings -> API section. 3) Copy the API Key and note the API URL (pod) shown next to it (e.g. na1). 4) Use that API key as the username in HTTP Basic auth (password blank). For sandbox/testing paste the API key directly.

2. Add them to .dlt/secrets.toml

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[sources.insightly_source]
api_key = "your_insightly_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 Insightly 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 insightly_pipeline.py

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

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Pipeline insightly_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset insightly_data
The duckdb destination used duckdb:/insightly.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 insightly_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 organisations from the Insightly 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 insightly_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.{pod}.insightly.com/v3.1/",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "Contacts"}},
            {"name": "organisations", "endpoint": {"path": "Organisations"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM insightly_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("insightly_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 Insightly 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, ensure you are using HTTP Basic auth with your API key as the username and a blank password. The API key must be Base64-encoded in the Authorization header (Basic {base64(api_key:)}). In the sandbox UI you may paste the API key directly.

Rate limits

Insightly enforces per-instance daily request quotas (varies by plan) and returns HTTP 429 when the quota is exceeded. Check rate-limit headers returned with each request and implement retry/backoff until the next quota window.

Pagination and total counts

GET endpoints are paginated (default 100 records, max 500). Use top and skip query parameters to page results. Add count_total=True to get X-Total-Count header with the total number of matching records.

Concurrency and conditional updates

PUT requests support concurrency via ETag in GET responses and If-Match header for updates. If ETag mismatch occurs the API returns 412 Precondition Failed; fetch latest ETag and retry.

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