AI·rete·RAG Launch: Combining Rete Engine with RAG for Auditable Decisions and Explanations

Summary:AI·rete·RAG is a novel decision support system designed to address the issue of unaccountable AI decisions in critical areas such as lending, fraud detection, and clinical triage. It combines a pure-Python Rete rule engine with Retrieval-Augmented Generation (RAG) technology. The Rete engine evaluates YAML rules against facts to make decisions, while RAG retrieves relevant passages from policy documents and generates plain-English explanations for the decisions. This approach ensures traceabilit

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AI·rete·RAG: An Innovative Decision Support System Combining Rete Engine with RAG

Core Features and Design Philosophy

AI·rete·RAG aims to address the issue of unaccountable AI decisions in critical areas such as lending, fraud detection, and clinical triage. Its core design philosophy involves combining a Rete rule engine with Retrieval-Augmented Generation (RAG) technology, featuring the following characteristics:

1. Rete Rule Engine:

   • Implemented in pure Python, evaluates YAML rules to make decisions.
   • Utilizes salience-based conflict resolution to ensure consistency in decision-making given the same facts.
   • Rules are organized as a graph, supporting nested logical structures (e.g., all/any/not) and forward chaining between rules.

2. RAG-Driven Explanation Generation:

   • Retrieves relevant passages from user policy documents.
   • Generates plain-English explanations for decisions using a large language model, without altering the decision outcome.

3. Advanced Features:

   • Visual Rule Graph Editor: Allows non-technical users to build rules through a visual editor or generate rule drafts with citations by pasting policy documents.
   • Audit Mode: Records detailed information about each rule evaluation, including rules that did not fire, and provides snapshots of the rule set for traceability.
   • Rule-Driven Retrieval Optimization: Triggered rules can narrow the search scope, and retrieved text can be converted into facts for the engine.

4. Openness and Availability:

   • Offers a no-signup online demo covering multiple domains such as loans, fraud, healthcare, and more.
   • MCP server supports calling the /decide interface by agents like Claude.
   • MCP client is open source (MIT license), but the engine and platform are not currently open source.

Technical Highlights

• Rule Graph and Forward Chaining: Rules are organized as a graph, enabling complex logical structures and inference chains between rules.
• Audit Mode: Provides detailed records of rule evaluations, supporting traceability and analysis of the rule set.
• Rule-Driven Retrieval Optimization: Optimizes retrieval by guiding the search process through rules, improving retrieval efficiency and generating more relevant facts.

Industry Impact and Developer Recommendations

AI·rete·RAG provides a new solution for AI applications requiring high auditability, particularly in sectors like finance, healthcare, and law. Its design not only enhances the transparency and interpretability of decisions but also offers user-friendly tools for non-technical users to build rules. Developers can leverage its open-source MCP client to build custom intelligent agent applications and explore the potential of rule-driven retrieval optimization in complex scenarios.

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Source: Hacker News AI Feed (2026-09-22)

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Tags: #AI Decision #Rete Engine #RAG #Auditability #Intelligent Agent