Conni (by ananas.codes) Python API Docs | dltHub

Conni is a middleware platform by ananas.codes that integrates cloud-based HR and time-tracking software with physical hardware like Datafox terminals. The REST API base URL is n/a and no public REST API authentication available.

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 Conni (by ananas.codes) data in under 10 minutes.

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What data can I load from Conni (by ananas.codes)?

Here are some of the endpoints you can load from Conni (by ananas.codes):

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How do I authenticate with the Conni (by ananas.codes) API?

Conni functions as a middleware/integration platform that syncs data between systems like Datafox terminals and HR software; it does not provide a public REST API for general-purpose user access, but rather manages integrations via private, configuration-based connections between specific partner services.

No credentials required. The Conni (by ananas.codes) API is public, so there is nothing to obtain and nothing to add to .dlt/secrets.toml — the pipeline above runs as written.

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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 harness:

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dlthub 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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dlthub 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 Conni (by ananas.codes) 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 conni_by_ananas_codes_pipeline.py

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

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Pipeline conni_by_ananas_codes_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset conni_by_ananas_codes_data
The duckdb destination used duckdb:/conni_by_ananas_codes.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 conni_by_ananas_codes_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 integrations and sync from the Conni (by ananas.codes) 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 conni_by_ananas_codes_source():
    config: RESTAPIConfig = {
        "client": {
            "base_url": "n/a",
        },
        "resources": [
            {"name": "papershift", "endpoint": {"path": "papershift"}},
            {"name": "personio", "endpoint": {"path": "personio"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="conni_by_ananas_codes_pipeline",
        destination="duckdb",
        dataset_name="conni_by_ananas_codes_data",
    )
    load_info = pipeline.run(conni_by_ananas_codes_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("conni_by_ananas_codes_pipeline").dataset()
sessions_df = data.papershift.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM conni_by_ananas_codes_data.papershift LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("conni_by_ananas_codes_pipeline").dataset()
data.papershift.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 Conni (by ananas.codes) 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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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform 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