Stop wasting tokens.
Exit early when confident.
Entroplain monitors LLM reasoning entropy in real-time. When the model confidently knows the answer, it exits — saving tokens without sacrificing quality.
How it works
Monitor entropy
Track token-level entropy in real-time. Low entropy means high confidence — the model knows the answer.
Detect valleys
When entropy drops into a sustained valley, the model has converged on a confident, stable answer.
Exit early
Stop generation once confidence is established. Save up to 50% tokens without quality loss.
Features
Proxy-based integration
Works with any agent — Claude Code, Cursor, OpenAI, any framework. Zero code changes needed.
Real-time dashboard
Watch entropy visualization live. See exactly when and why early exit triggered.
Cost tracking
Know exactly how much you saved. Token counts, costs, and savings per request.
Multiple exit strategies
Valleys, velocity, confidence threshold, repetition detection. Pick your strategy.
Multi-provider support
OpenAI, Anthropic, NVIDIA, Google Gemini, OpenRouter, local models via Ollama.
Python + Node.js
Available on PyPI and npm. Use it wherever your agent runs.