> ## Documentation Index > Fetch the complete documentation index at: https://aletyx.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # What is DRL? > DRL (Drools Rule Language) is the native, text-based language of Drools for authoring business rules with a simple when/then structure for complex inference. **DRL (Drools Rule Language)** is [Drools'](/docs/faq/concepts/what-is-drools) native, text-based language for authoring business rules. Each rule follows a simple **when / then** shape: a `when` block describes the conditions to match against the facts in working memory, and a `then` block describes the actions to take when those conditions hold. Because it is written as code rather than diagrams, DRL is more expressive and powerful than DMN for complex inference — chaining rules, reasoning over large fact sets, and fine-grained control — at the cost of being Drools-specific rather than a portable standard. DRL example showing a when/then rule that flags a high-risk loan applicant The engine evaluates DRL rules with a high-performance pattern-matching algorithm (Rete), so you declare *what* should happen and the engine decides *when* each rule fires. ## Where to go next How DRL compares to DMN and BPMN, and when to use each. How DRL fits into decision automation in the Aletyx architecture. The fundamentals of DRL syntax and rule structure. How the when block matches facts in working memory. How the then block defines what happens when a rule fires. Worked DRL rule examples you can learn from and adapt.