Klaus Kode—The Agentic Data Integrator

Why use Klaus Kode?

To save time. A huge part of software and data engineering is simply about wrangling data. You need to get data out of one system and pipe it into another system, while preventing data loss. This usually requires writing custom “glue” code which is a lot of busywork.

What about connectors?  The data ecosystem is sprawling with connectors for a patchwork of different systems—some well-maintained, others barely touched. Instead of hunting down the right connector, why not get AI to help you build and test your own connectors?

What makes it "agentic"?

Klaus Kode is built in Python using the OpenAI agents SDK and the Claude Code SDK. OpenAI agents handle simple tasks like log file and schema analysis whereas Claude Code handles code generation, debugging, as well as environment variable and dependency management. 

It uses the Quix Cloud portal API to provision testing sandboxes, install dependencies to run the code. Quix Cloud uses Apache Kafka under the hood to store your data in Kafka topics (but don’t worry you don't need to know anything about Kafka to use it).

If you’re already familiar with Claude Code, think of it as a brute force approach to forcing Claude to follow a precise step-by-step workflow, and to only use AI for what's really necessary. For standard steps of the workflow (running and deploying code) we use deterministic logic

But I use AI-tools already! Why do I need another one?

Even with AI, building and testing (source and sink) connectors can require a lot of prompt context engineering coupled with trial and error. 

You can do it with Claude Code or Cursor, but it’s kind of a slog setting up your environment and prompts. And AI doesn't always obey sequential workflows despite our best efforts to craft precise project prompts.

Klaus Kode, solves this problem by wrapping a structured workflow around the Claude Clode CLI.

It can:

• test your connection code in a containerized cloud sandbox (so you don't have to worry about setting up a local environment)
• read the log output and see if data is being written or read correctly (if its broken it can fix its own mistakes and try again)
• deploy your connector as a containerized application (once you’ve established that it works)

For example — suppose you want to read sensor data from an internal websocket API and sink it to an S3 bucket later.

The normal workflow might look like this:

1. Read the Websocket docs and learn about the data schema
2. Set up your environment and install dependencies, and configure your S3 env vars in a file somewhere.
3. Write some code to read from the websocket
4. Write some code to write to S3.
5. Test the code locally.
6. It’s broken, go back to #3, otherwise proceed.
7. When it works, you package it for deployment
8. You deploy it to a server somewhere so that it can run continuously

Here’s how it works with Klaus Kode

1. Save the Websocket docs to Klaus Kode’s knowledge folder
2. Start the “Source workflow” wizard in your terminal and tell Klaus Kode what data you want to get from the Websocket
3. Klaus Kode will generate the code and ask you if it looks OK.
4. Assuming “yes”, Klaus Kode will go through a questionnaire to get sensible values for any necessary env vars and secrets necessary.
5. Klaus Kode will upload it to a code sandbox (hosted in Quix Cloud) and execute it, then read the logs to see if it works.
6. If it doesn't work the first time, you can set it to “auto-debug” so that Klaus Kode iterates for a configured number of cycles until it works.
7. Once the code works, Klaus Kode will deploy it to a container that outputs data to “sensor-data” Kafka topic in Quix Cloud
8. To get it into S3, you would run the “Sink workflow” and read from the same “sensor data” topic.

Those look like the same number of steps — so what's the difference? Klaus Kode  lets you run the workflow on easy mode, so you can do something else while it builds and deploys your connector. All you have to do is answer a few questions and let it run.

What are the prerequisites for using Klaus Kode?

1. Git and Python for starters.
2. The Claude Code CLI (you don’t need a Claude Code subscription though)
3. API keys for both Anthropic and OpenAI (this is so we can save on token cost for cheaper tasks)
4. A Quix Cloud PAT token — you can sign up for free to get one (this lets Klaus Kode run the code in a cloud sandbox)

You also need to be prepared to follow a pre-defined workflow. This isn’t a free form chat interface, it's more of a wizard. To learn more about the exact workflow steps, see the Workflow section of project’s README.

How do I try it out?

You can find more detailed instructions on the project’s README, but here’s a quick overview

1. Clone the Klaus Kode, repo 
2. Set up a virtual environment and activate it
3. Run pip install -r requirements.txt to install the dependencies.
4. Create a .env file (make a copy of the .env.example file) and enter your API keys and PAT token
5. Run python main.py 

Why did we build this?

We started building Klaus Kode to help our customers. They have to build their own data integrations, even though data engineering is not their core focus (and they don't have data specialists on-hand to help). Our customers often come from the world of industrial engineering and research and development.

They know their way around MATLAB and Python, but don’t have much experience with DevOps and data pipelines.

They know AI could help but not sure how to get started. That was the inspiration for Klaus Kode—to give customers a structured onramp into coding connectors with AI, with all the context engineering and resource provisioning taken care of.

But Klaude Kode is not just for industrial engineers, it's for anyone who wants to build data integrations with the help of AI and has high volumes of data to manage.

Disclaimer

Klaus Kode is an early prototype and may contain bugs or produce incorrect results. Some features may be unstable or incomplete. Code and configurations generated by the tool should always be reviewed and tested before being used in production. While Klaus Kode is designed to speed up integration work, it is not a substitute for validation by an engineer.

The goal of this release is to gather early feedback and learn how the tool can be most useful. We expect to make significant enhancements over time, and your input will help guide that progress.