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Workflows 2026 Rank: #2411

ETLR ✓

AI workflows as code, deployed in minutes.

Source rating 5.0 (2) 582 views 4 saves Freemium
API Workflows As Code Workflow As Code
More categories (4)
Code-based Workflow Execution Trace AI Workflow Automation Workflows
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Free + from $27/mo

Verdict

A quick read before you dig into the full page.

Best if
AI workflows as code, deployed in minutes
Skip if
Complex for non-developers
Pick this when
YAML for workflow creation

Considering PixieBrix instead? Compare side by side

Overview

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.

ETLR website

Access & connections

Where you can use ETLR, and what it plugs into.

Platforms

Web

Integrations

Slack

API

Public API available

Browse other tools with an API

Capabilities

What this tool accepts, produces, and is strongest at.

Input

  • API
  • Code
  • Tabular
  • Text

Output

  • Code
  • API
  • Text

Highlights

  • API

Supported features

  • Automation
  • Integrations
  • API

Pros & 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
Show 39 more
  • 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

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
Show 2 more
  • Enterprise tier required for SLA guarantees
  • Exclusively code-focused: less accessible

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Releases 1

Initial release January 12, 2026 Current
Initial release of ETLR.

FAQ 12

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.
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.
ETLR Free + from $27/mo
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