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Title of Kestra: "Kestra, Open Source Declarative Orchestration Platform"

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We maintain SDKs in several languages, a CLI, and an infrastructure-as-code provider (each with its own way of cutting a release). Here's how we replaced that patchwork with a single rule: push a tag to ship, and the tag is the version.

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Python excels at execution logic. YAML excels at defining workflows. Here's a practical breakdown of when to reach for each, and how modern orchestrators let you use both.

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Building AI pipelines means stitching together LLM calls, vector search, document ingestion, and data preprocessing. Kestra ships plugins for all of it. This post maps every plugin category that matters for AI workflows (outside of plugin-ai) so you know exactly what to reach for.

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Build an end-to-end RAG pipeline in declarative YAML with Kestra: ingestion, chunking, embeddings, vector storage, and grounded queries, starting from zero dependencies and scaling to Qdrant or PGVector. Clone the blueprint.

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How to automate parallel releases across multiple versions with automatic retry handling.

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