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AI·rete·RAG

a Rete rule engine decides, RAG explains why

Details

External ID
49803683
Source
HN
Company
—
Product
AI·rete·RAG
Website domain
ai-rete-rag.com
Launched
Sept. 22, 2026
Cohort
—
Upvotes
44
Upvotes percentile
0.8381180223285486
Tags
—
Fetched at
Sept. 26, 2026, 10:53 p.m.
Updated at
Sept. 26, 2026, 10:53 p.m.

Description

Hi HN, I built ai·rete·rag because I kept seeing teams put an LLM in charge of decisions that need to be auditable (lending, fraud, clinical triage), then bolt on "guardrails" after the fact.It runs the two in series instead:1. A pure-Python Rete engine evaluates YAML rules against your facts. The verdict comes only from here. Same facts, same verdict, every time, with salience-based conflict resolution. 2. RAG retrieves passages from your own policy documents, and an LLM writes a plain-English explanation of the decision that was already made, citing those passages. It can't change the verdict.A few things that went further than I expected: - Rules are a graph, not flat lists: nested all/any/not, and rules can assert facts that other rules consume (forward chaining). The decision trace shows the causal chain. - Audit mode records every rule evaluated, including the ones that didn't fire, condition by condition, with a snapshot of the rule set for replay. - Rules can steer retrieval (a fired rule narrows which documents get searched), and retrieved text can be turned into facts for the engine. - Non-technical authors can build rules in a visual editor, or paste a policy document and get LLM-drafted rules with citations. Drafts are never saved without review. YAML is still there for engineers.The landing page has a live demo with no signup (8 demo domains: loan, fraud, clinical, insurance, legal, ops, e-commerce, blockchain). There's also an MCP server, so Claude and other agents can call /decide as a tool: `uvx ai-rete-rag-mcp`.To be upfront: it's a hosted product with a free tier. The MCP client is open source (MIT, github.com/zaharajabeen13-create/ai-rete-rag-mcp); the engine and platform are not open source right now.I'd especially like to hear from anyone who has had to explain an automated decision to a regulator or an auditor: what did they actually ask for?

Enrichment

Theme
automated legal and compliance tools
Vertical
Horizontal
Function
Dev tools
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
rule engine with rag for developers
Manually corrected
False

Could you build this?

Yes The product connects an open-source Python Rete algorithm implementation (like experta or pyknow) with an LLM prompt pipeline to explain matched deterministic rules.

Discussion

2 comments analyzed.

Competitors mentioned: Drools

Concerns raised: Using LLMs to explain rules instead of displaying passed/failed logic

Competitors

Other products that read as similar to this one — 72 launches clear the similarity bar, closest 8 shown.

Attention rank: #7 of 73 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 323 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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