eachlabs-workflows

Trigger asynchronous multi-step AI workflows by defining optional webhooks in your workflow configuration. When executions run, the EachLabs API sends asynchronous notifications to your webhook endpoint, allowing you to monitor progress and collect results without blocking.

Referencing step outputs in multi-model orchestration allows you to pass data between chained models. You define input variables for subsequent steps that reference the outputs of previous steps, creating a repeatable data pipeline.

Yes, you can version AI workflows for different production environments. The workflow versioning feature allows you to design repeatable pipelines, ensuring consistent behavior and schema validation across teams and deployments.

You do not need webhooks to monitor workflow execution results, though they are supported for asynchronous notifications. You can also trigger executions and actively monitor progress to collect results across steps synchronously.

Limitations of API workflow orchestration include the requirement for clear versioning and schema validation before execution. Complex multi-step pipelines depend on correctly defining input variables and step output references to avoid data mismatch failures.