[Architecture] Preventing RAG "Context Rot" : A Deterministic Temporal Decay Proxy for LangChain Agents

OSS Product Help LangChain
1

Hey everyone,

I wanted to share an architectural pattern we’ve been using to solve a critical failure mode in enterprise LangChain agents: “Context Rot.”

Standard vector stores retrieve perfectly on semantic similarity, but they are blind to temporal validity. If a compliance rule or API spec is updated, the old chunk might still have a 0.92 cosine similarity. The retriever pulls it, stuffs it into the LangChain agent’s context window, and the agent confidently hallucinates.

To stop this without writing brittle post-retrieval filters for every chain, I built KU-Gateway.

It’s an open-source edge proxy that sits between your vector DB and LangChain. It physically intercepts the retrieved payload and runs a mathematical temporal decay function ($e^{-\lambda t}$).

If a document has decayed past the acceptable threshold, the proxy hard-gates it and drops it before it hits the LLM.

Instead of failing silently, it outputs a 5-step Ops Runbook trace directly into the terminal (including the expected recovery action and routing queue), turning an agent failure into a structured data engineering ticket.

Validation:

We ran this proxy architecture through a private staging evaluation with a Tier-1 Cloud Provider’s managed AI team. Their internal testing validated that deterministic edge-gating cut token burn by ~50% and functionally eliminated temporally-induced hallucinations in their agent loops.

The Ask for the Community:

The core proxy is running on Uvicorn and works natively as a middleware layer. However, I’m currently looking at the best way to tightly integrate this 5-step Ops trace into LangGraph’s state management and checkpointing system.

Are folks here handling edge-proxy failures via custom CallbackHandlers, or mapping them directly as conditional edges in a StateGraph?

Would love to hear how you are handling deterministic guardrails.