Entroly helps AI coding tools like Cursor, Copilot, and Claude Code use the right context from your entire codebase—improving output quality while reducing token usage.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"entroly": {
"args": [
"-y",
"entroly-wasm"
],
"command": "npx"
}
}
}Are you the author?
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Every AI coding tool — Claude, Cursor, Copilot, Codex — has the same blind spot: it only sees 5–10 files at a time. The other 95% of your codebase is invisible. This causes hallucinated APIs, broken imports, missed dependencies, and wasted developer hours fixing AI-generated mistakes.
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No known CVEs.
Checked entroly-wasm against OSV.dev.
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Cut your Claude / OpenAI / Gemini bill 70–95% on AI coding.
Compress context, keep provider caches hot, and verify every answer with a $0 hallucination guard.
Drop-in for Cursor, Claude Code, Codex, Aider + 34 more and custom providers — 30s, no code changes.
Auditable context control plane · every answer gets a receipt: what was used, what was omitted, why, and the risks that remain · local-first · Rust + WASM · reversible · savings measured on real workloads
pip install entroly && cd /your/repo && entroly go
Get started · Proof · Memory OS · Integrations · What's inside · Architecture · For teams · Limitations
Entroly is an auditable context control plane for AI agents. It decides what context to send, records what it left out, and produces a receipt you can inspect before trusting a hard multi-file answer.
Use it however you work: wrap your agent, run it as a **prox