AI workflows as code, deployed in minutes.

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January 12, 2026

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ETLR

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AI workflows as code, deployed in minutes.

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Overview

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ETLR is an AI workflow automation tool enabling users to create, version, and deploy workflows using YAML. Implemented with a 'workflows as code' approach, it provides a straightforward way to manage AI workflows without the need for UI manipulation.

ETLR comes with built-in version control for workflows, making tracking changes and rolling back updates effortless. By deploying workflows with a single command, developers can avoid the tedious configuration of infrastructure - making the tool a timesaver.

It integrates with various tools like OpenAI, AWS, Slack, Python, and more, enhancing its functionality. Further, it offers notable features such as real-time metrics, structured logs, step-by-step execution traces which provide complete visibility into workflow execution.

ETLR also supports Python-transform at ease, discarding the need for 3rd party scripts. The robust error tracking system helps in easy debugging whenever required.

Also, ETLR enterprise users get the luxury of dedicated support and SLA guarantees. Lastly, the pricing strategy is simple and credit-based, where one credit equals one workflow execution.

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Initial release

January 12, 2026

Liam Betsworth

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Initial release of ETLR.

Author

Liam Betsworth

@liam-betsworth

Founder of Creu Labs, a Welsh venture studio building world-class startups. Former CTO at AMPLYFI. PhD in Human-Computer Interaction from Swansea University, with research collaborations at IBM and BBC.

betsworth.io

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Joined: January 2026

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Pros and Cons

Pros

YAML for workflow creation

Code-based workflow automation

Version history tracking

Change and upgrade models seamlessly

Git-friendly interface

Workflows as code

Google Gemini integration

AWS integration

Slack integration

Python integration

Built-in metrics

Structured logs for visibility

Execution traces for details

Automatic error detection

Accept HTTP webhooks

Transform data with Python

Forward processed data

Enterprise Support

SLA Guarantees

Credit-Based Pricing

One command deployment

Integrated version control

Python-Transform supported

Automatic error tracking

Step-by-step execution traces

Robust debugging support

Workflow roll-back capability

Supports production-ready observability

Real-time performance tracking

Track changes, review in PRs

Store workflows in repo

Automatic versioning

One-Click Rollback

Detailed logs for steps

Automated error detection

Manual Python functions

Simple, transparent pricing

Dedicated support for enterprise

Time-stamp and unique IDs addition

Webhook accept from any source

Custom code transformation

Data forwarding to external services

Health Monitoring

Can schedule health checks

Handle non-200 responses

Alerting via Discord

Customization of workflow parameters

View 42 more pros

Cons

Limited UI interaction

YAML-only workflow creation

No drag-and-drop convenience

Credit-based pricing model

No credit rollovers

Complex for non-developers

Limited enterprise customization

Only Python support

Enterprise tier required for SLA guarantees

Exclusively code-focused: less accessible

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Q&A

What is ETLR?

ETLR is a tool designed to manage and create AI workflows as code, bypassing conventional drag-and-drop creation and exploiting potential of code-based workflow automation. ETLR is aimed to meet developers' needs by using YAML to craft, version, and deploy AI workflows. ETLR allows smooth transition of model upgrades whilst preserving the whole version history, and also promotes a Git-friendly interface permitting workflows to be stored in your repo. Some of the highlights of ETLR include built-in metrics, logs, and traces provision for competent monitoring, debugging, and optimization of every workflow.

How does ETLR enhance AI workflow automation?

ETLR enhances AI workflow automation by using a code-based approach as an alternative to the drag-and-drop style. This approach caters to developers' needs, providing a more controlled and reproducible workflow. This tool also allows seamless integration with frequently used tools such as OpenAI, Google Gemini, AWS, and Slack among others. Using ETLR, workflows can accept HTTP webhooks from any source, apply transformation to data using custom Python code, and forward processed data to various external services.

What is the significance of YAML in ETLR?

YAML in ETLR is utilized to build, version, and deploy AI workflows. It has an easy-to-read syntax that has been accepted in configuration files and in applications where data is being stored or transmitted. It is human-friendly and supports complex data structures while maintaining readability. Hence, it can be used to effectively manage and create AI workflows in ETLR.

How does ETLR ensure version control?

ETLR ensures version control through the auto-creation of a new version with every deployment. It maintains a full version history which is kept track of in the dashboard which supports rollback to any previous version if necessary. This process can either be done through the UI or by CLI command. Workflow changes are treated ditto to code which can be approved, commented on, and merged with confidence.

Can I integrate ETLR with other commonly-used tools like OpenAI, Google Gemini, AWS, and Slack?

Yes, ETLR can be integrated seamlessly with commonly used tools such as OpenAI, Google Gemini, AWS, and Slack among others. ETLR is designed to optimize the workflow by enabling intercommunication between these platforms. This feature allows ETLR to utilize the unique advantages of each platform, providing a more efficient and comprehensive workflow management solution.

How does ETLR facilitate model updates?

ETLR streamlines model updates by using a code-based approach. Changes or upgrades to models can be coded and deployed seamlessly. Thanks to the version control system, all changes are tracked, enabling an easy rollback to a previous version if needed.

+ Show 34 more

How can I track changes and restore previous versions using ETLR?

ETLR provides a way to track changes and restore previous versions through its version control system. With every new deployment, a new version is created automatically. All the versions along with their changes are maintained and can be viewed in the dashboard. If there's ever a need to restore a previous version, it can be done instantly with the help of UI or CLI command.

Is ETLR git-friendly? Can I store workflows in my repo?

Yes, ETLR is git-friendly. It offers a git-friendly interface where workflows can comfortably be stored in your repository. This feature allows you to review changes in PRs like with any other code.

How does ETLR help in monitoring, debugging, and optimization?

ETLR comes with robust features for monitoring, debugging, and optimization. It features a built-in metrics, logs, and traces provision that provides complete visibility into every workflow run including execution counts, success rates, and performance trends. The built-in metrics give you statistical data on the performance and success of your workflows. Logs provide specific details for every step that's taken, which helps in the debugging process. ETLR also provides execution traces that give a step-by-step timeline showing what happened and how long it took, enhancing the debugging and optimization process.

How can I track execution counts, success rates and performance trends in ETLR?

To track execution counts, success rates, and performance trends in ETLR, you can make use of the built-in metrics, logs, and traces. These provide real-time reporting and complete visibility into every workflow run. The metrics show the total execution count, success rate, and average duration of your workflows, presenting performance trends in an illustrative manner for convenience.

Can ETLR automatically detect errors for quick debugging?

Yes, ETLR can automatically detect errors for prompt debugging. ETLR provides automatic error detection with stack traces and context, enabling rapid identification and resolution of issues. This feature drastically reduces the time and effort required for debugging while improving the efficiency of workflows.

How does ETLR use HTTP webhooks?

In ETLR, HTTP webhooks can be accepted from any source. This feature facilitates the real-time collection of data from APIs, databases, or third-party services. These HTTP webhooks are part of the workflow and contribute to the automation and efficiency of processes within ETLR.

Can I transform data using custom Python code in ETLR?

Yes, ETLR allows the transformation of data using custom Python code. With the support for custom Python code, one can normalise, validate, or transform data precisely according to the requirements. This adds another layer of customization to the workflow automation, allowing more specific command over data processing.

Can ETLR forward processed data to external services?

Yes, ETLR has the capability to forward processed data to various external services. Users can forward processed data to databases, APIs, or notification services, which also is equipped with built-in retry logic. This adds an output layer to the AI workflows, enabling further manipulation or utilization of the processed data.

How is the deployment of AI workflows facilitated in ETLR?

ETLR promotes quick deployment of AI workflows by utilizing YAML for crafting the workflows, which can then be deployed with a single command. There is no requirement to configure any infrastructure or manage any queues, which accelerates the deployment process considerably.

What kind of logs and traces does ETLR provide?

ETLR provides structured logs and execution traces for efficient tracking. Structured logs deliver complete visibility into every step that's conducted, aiding in searches, filters, and debugging. On the other hand, execution traces help create a detailed step-by-step timeline displaying exactly what happened and how long it took, thus making the debugging process more efficient.

Does ETLR allow webhooks from any source?

Yes, ETLR allows HTTP webhooks to be accepted from any source, thereby enabling greater flexibility in data ingestion. It can handle real-time data reception from an array of sources like APIs, databases, or third-party services. This way it amplifies data accessibility and enhances workflow automation.

How flexible is ETLR when dealing with Python code?

ETLR provides immense flexibility when dealing with Python code. Users can integrate custom Python functions to normalise, validate, or transform data exactly as needed. This enables users to tailor their workflow precisely according to their specific requirements and adds another dimension to workflow customization.

How are external services integrated into the ETLR process?

External services are integrated into the ETLR process through the feature which allows the forwarding of processed data to various external services. Once the data has been processed through ETLR's workflows, the data can be sent to databases, APIs, or notification services. This feature also comes with built-in retry logic, ensuring the successful delivery of data.

What is the role of ETLR in structured logs for visibility?

ETLR supports structured logs to enhance visibility into workflows. These logs offer detailed information for each step of the workflow, providing a comprehensive view into the performance and state of your workflows. This data can be used to pinpoint issues, understand where improvements can be made, and monitor the overall health and efficiency of your workflows.

What is ETLR?

ETLR is a tool designed to create and manage AI workflows as code. It facilitates developers to build, version, and deploy AI workflows using YAML and integrates with commonly used platforms like OpenAI, AWS, Slack and Python.

What does ETLR stand for?

The term ETLR doesn't stand for anything in particular. It's the name of a program designed for creating, managing, and deploying AI workflows as code.

What platforms does ETLR support and integrate with?

ETLR supports and integrates with a variety of platforms including OpenAI, Google Gemini, AWS, Slack, and Python among others.

Can ETLR accept HTTP webhooks from any source?

Yes, ETLR can accept HTTP webhooks from any source allowing for a flexible and dynamic data input.

What are the key features of ETLR?

Key features of ETLR include automation of AI workflows as code, quick deployment, version tracking, integrations with tools like OpenAI, AWS, Slack, Python, real-time metrics tracking, structured logging, efficient debugging, enterprise support, and SLA guarantees to name a few.

How does ETLR facilitate tracking changes in AI workflows?

ETLR provides a built-in version control for workflows that helps in keeping track of changes. It maintains a full version history, thereby facilitating easy review of changes and restorations to previous versions if necessary.

Is there built-in metrics in ETLR?

Yes, ETLR does have built-in metrics. These metrics along with logs and traces enable efficient monitoring and optimization of every workflow run.

How can ETLR enable efficient monitoring and debugging of workflow run?

ETLR provides an integrated system for monitoring and debugging each workflow run. It enables tracking of execution counts, success rates, performance trends, and provides structured logs for visibility and execution traces for detailed step-by-step timelines. It also has an automatic error detection system for quick debugging.

What is a code-based workflow automation?

Code-based workflow automation is a method where workflows are written and managed as code, instead of using visual or UI-based tools. It offers more control, customization and tracking capabilities to developers.

What language is used to build, version, and deploy AI workflows in ETLR?

ETLR uses YAML to build, version, and deploy AI workflows.

What makes ETLR different from other AI tools?

ETLR stands out for its code-centric approach to managing AI workflows. Instead of a drag-and-drop or UI-centric method, it empowers developers to build, version, and deploy workflows directly through code using YAML. It also integrates with popular tools and has extensive features such as automatic error detection, real-time metrics, and structured logging.

How does ETLR support the changing or upgrading of models?

ETLR maintains a full version history which allows for seamless changing or upgrading of models. Any upgrades or changes create a new version, with the complete history being easily accessible for review or rollback.

Does ETLR offer enterprise support and SLA guarantees?

Yes, ETLR offers dedicated enterprise support and Service Level Agreement (SLA) guarantees for its users.

How is ETLR's pricing structured?

ETLR's pricing is credit-based. Each credit equates to one workflow execution. They offer different tiers of pricing including a free tier with basic capabilities, a professional tier at £20/month with additional credits and priority support, and a custom enterprise tier with unlimited credits and dedicated support.

Can Python code be used for data transformation in ETLR?

Yes, ETLR supports the use of custom Python code for data transformation. This allows developers to manipulate data exactly how they need it within their workflows.

What is the significance of a Git-friendly interface in ETLR?

A Git-friendly interface in ETLR means that workflows can be stored in developers' own repositories. This allows for better version control, change tracking, and code reviews.

How does ETLR simplify the deployment of AI workflows?

ETLR simplifies the deployment of AI workflows by providing a YAML-based workflow modeling and a command line interface. Workflows can be built, versioned, and deployed with simple commands, bypassing complex infrastructure configurations.

What is the use of structured logs and execution traces in ETLR?

Structured logs and execution traces in ETLR provide detailed visibility into workflow execution. Structured logs offer clear, step-by-step logs of each run, while execution traces provide timelines of each step, enhancing the ability to monitor, optimize and debug workflows.

How does ETLR promote real-time metrics tracking?

ETLR promotes real-time metrics tracking by offering built-in metrics. These provide insights into execution counts, success rates, and performance trends, all updated in real time.

How does ETLR ensure automatic error detection and quick debugging?

ETLR ensures automatic error detection and quick debugging through its integrated monitoring and error handling features. If a step in the workflow fails, ETLR identifies the problem and provides detailed error messages to aid in debugging.

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