AI·rete·RAG
ai-rete-rag.com- Category
- Developer Tools
- Rank
- No. 4031Tools index
Previous survey · No. 3810 ·
- Type
- TOOL
- Use case
- Workflow Automation
- Date
About
AI·rete·RAG separates auditable decisions from AI-generated explanation: a pure-Python Rete rule engine evaluates YAML rules against input facts to produce a deterministic verdict with salience-based conflict resolution, while a retrieval-augmented layer has an LLM write a plain-English explanation citing the organization's own policy documents, without being able to change the outcome. Rules form a graph with nested all/any/not logic and forward chaining, an audit mode records every rule evaluated (including ones that didn't fire) for replay, and it ships with a visual rule editor plus an MCP server for use from Claude and other agents.
At a glance
AI·rete·RAG combines a rules engine with document retrieval so a case can get a rule-based verdict and a readable explanation. It is worth exploring if your policy already has explicit decision criteria and you need to trace how a case was decided.
Good fit
Teams prototyping a policy workflow with known input fields and human review.
Consider something else when
Use a simpler rules engine if you only need the verdict, or keep human review in charge if the policy cannot be stated reliably as rules.
Start here
Use synthetic cases just above and below one threshold. Compare the returned verdict, fired rule and cited policy before adding another rule or real documents.
Cost and requirements
The project documents shared demo domains and account-based hosted access with decision quotas. Its MCP client is available separately.
Limits to know
A rule-based verdict still depends on the rules and facts supplied. Retrieved facts and generated explanations can be wrong. Review data handling and verify imported policy rules before use.
Sources checked 2026-11-05. Source-based assessment.
Why it made the leaderboard
For lending, fraud, or clinical decisions, ai·rete·rag keeps the verdict from a deterministic rule engine while an LLM only explains it after the fact, addressing a real auditability gap in LLM-based decisioning.
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