Show HN: EchoMode – A stability layer that prevents persona drift in LLMs

Hey HN! I’m Sean, and I built EchoMode to solve a frustrating problem with long LLM conversations: persona drift — when models slowly forget their intended tone, style, or role after multiple turns.

The Problem: Even well-prompted models (GPT-4, Claude, Gemini) start to lose persona after ~8 turns. Studies like Measuring and Controlling Persona Drift (arXiv:2402.10962) confirm that tone consistency decays fast with topic shifts and long contexts.

The Solution: EchoMode is a middleware protocol that keeps LLMs consistent by monitoring tone drift in real time and repairing deviations automatically. Think of it as TCP/IP for language stability.

How it works: – Finite-state machine (Sync / Resonance / Insight / Calm) tracks conversation state – Calculates a driftScore between each output and baseline persona – Triggers a repair loop if deviation > threshold – EWMA smoothing (λ≈0.3) prevents overcorrection – Works across OpenAI, Anthropic, Gemini, Mistral APIs

Tech Stack: TypeScript SDK • Drift telemetry dashboard (soon) • Apache-2.0 open core + commercial observability layer

Use Cases: AI agents that must stay on-brand, compliant, or emotionally consistent over long sessions.

Try it: github.com/Seanhong0818/Echo-Mode