Have you noticed that as your AI coding session progresses, your API bill explodes - and the model starts hallucinating or ignoring code you wrote 10 minutes ago?
Here is why: Context Rot.
Every time you ask Claude Code, Cursor, Codex, or OpenClaw a question, your agent dumps full repository files, old terminal logs, test outputs, and duplicate code into the prompt. You end up paying for 100,000+ tokens on every single turn, 90% of which the model never needed to read.
The 90% Noise Problem
Most tools try to fix this with naive text compressors or truncators that randomly chop off code. The result? The model loses function definitions, imports, or test cases, leading to broken code and hallucinations.
Entroly takes a different approach. It acts as a local Context OS for your agent:
- Cuts Up to 90% Context Bloat: Automatically filters out irrelevant logs, duplicate snippets, and unneeded files under a strict token budget.
- 100% Evidence Recovery: Generates SHA-256 receipts for omitted code. If the model ever needs an omitted function, it recovers exact source bytes automatically.
- Preserves Prompt Caching: Keeps static system prompts intact so Anthropic and OpenAI prompt caching hits reliably.
- Zero-Cost Verification: Validates generated code claims locally before you execute them - without calling another expensive LLM.
Test It on Your Codebase in 30 Seconds (No API Key Required)
You don't even need an API key to test how much context bloat is sitting in your repo:
pip install -U entroly
cd /your/project
entroly verify-claims && entroly simulate
Stop paying provider bills for 100k tokens of noise.
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