Clara Rules
A forward-chaining rules engine for Clojure and ClojureScript with Java interoperability.
What is Clara Rules?
Clara is a forward-chaining rules engine implemented in Clojure and ClojureScript that allows developers to create expert systems using declarative rules. It solves the problem of managing complex business logic by separating rules from application code, enabling more maintainable and understandable systems. The engine automatically infers new facts from existing data through pattern matching and rule conditions.
Clojure and ClojureScript developers who need to implement complex business logic, expert systems, or decision-making workflows in their applications. It's also suitable for Java developers seeking a rules engine with Clojure interoperability.
Developers choose Clara for its seamless integration with Clojure ecosystems, clean declarative syntax, and Java interoperability. Its developer-centric design makes rule-based programming practical and accessible compared to more complex enterprise rules engines.
Overview
Forward-chaining rules in Clojure(Script)
Use Cases
Best For
- Implementing business rules and decision logic in Clojure applications
- Building expert systems that require forward-chaining inference
- Separating complex conditional logic from core application code
- Creating maintainable rule-based workflows in ClojureScript web applications
- Adding rules engine capabilities to Java projects via Clojure interop
- Developing systems where business rules change frequently and need to be managed separately
Not Ideal For
- Projects in non-JVM languages like Python or Ruby, where integration would be complex or impractical
- Applications with simple, static business logic that can be handled with basic if-else statements
- Teams lacking Clojure or functional programming experience, as the syntax and concepts require adaptation
- High-frequency trading or real-time systems where the inference latency of a rules engine is unacceptable
Pros & Cons
Pros
Uses idiomatic Clojure syntax for defining rules with defrule and mk-session, making it seamless for Clojure developers as shown in the README example.
Automatically infers new facts from data through pattern matching, simplifying complex conditional logic and enabling expert systems.
Can be used from Java applications, expanding its reach beyond Clojure ecosystems, which is highlighted in the GitHub description.
Separates business rules from application code using a clean API, improving maintainability for frequently changing logic.
Cons
Requires familiarity with Clojure's functional syntax and concepts, which can be a barrier for developers accustomed to imperative languages.
Adds inference layers and session management that may not be optimal for high-throughput applications, a common trade-off with rules engines.
Being niche in the Clojure community, it has fewer third-party tools or integrations compared to more established engines like Drools.
The README mentions needing npm and puppeteer for ClojureScript tests, indicating additional setup steps that can complicate initial use.
Frequently Asked Questions
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