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rag_control
Runtime governance and policy controls for RAG systems
A Python control layer for governing RAG applications at runtime. Define generation policies, filter retrieval by document metadata and user context, apply organization-level rules, and inspect decisions with audit logs, traces, and metrics. Works with your existing LLM and vector-store adapters. Built by RetrievalLabs, which is making enterprise software development end-to-end AI-native.
Hi Product Hunt! rag_control helps teams add runtime governance to RAG applications without replacing their LLM or vector-store adapters. It covers generation policies, retrieval filters, organization-level rules, and auditability.
Start here: https://github.com/RetrievalLabs...
Docs: https://rag-control.retrievallab...
RetrievalLabs is making enterprise software development end-to-end AI-native. Follow the team on LinkedIn: https://www.linkedin.com/company...
Learn more: https://retrievallabs.org/
I’d love to hear how you’re managing policy enforcement and data access in RAG applications.
About rag_control on Product Hunt
“Runtime governance and policy controls for RAG systems”
rag_control was submitted on Product Hunt and earned 12 upvotes and 3 comments, placing #62 on the daily leaderboard. A Python control layer for governing RAG applications at runtime. Define generation policies, filter retrieval by document metadata and user context, apply organization-level rules, and inspect decisions with audit logs, traces, and metrics. Works with your existing LLM and vector-store adapters. Built by RetrievalLabs, which is making enterprise software development end-to-end AI-native.
On the analytics side, rag_control competes within Developer Tools and GitHub — topics that collectively have 563k followers on Product Hunt. The dashboard above tracks how rag_control performed against the three products that launched closest to it on the same day.
Who hunted rag_control?
rag_control was hunted by Yash Patel. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of rag_control including community comment highlights and product details, visit the product overview.
Yash Patel