Entroly FAQs

Question 1

How much token consumption can Entroly reduce?

Accepted Answer

Entroly achieves an average of 78% token reduction per request. It does this through intelligent compression, deduplication, and boilerplate removal, ensuring your AI agents get more relevant information with fewer tokens.

Question 2

Which AI coding tools and platforms does Entroly support?

Accepted Answer

Entroly works universally with popular AI coding tools like Cursor, Claude Code, Copilot (via VS Code), Windsurf, OpenClaw, and Cody. It integrates via MCP server or a transparent HTTP proxy, supporting Linux, macOS, and Windows with a native Rust engine for minimal overhead.

Question 3

Does Entroly offer any security features?

Accepted Answer

Yes, Entroly includes built-in security scanning (SAST) with 55 rules. It automatically detects common vulnerabilities such as hardcoded secrets, SQL injection, command injection, and other critical CWE categories within your codebase during the context optimization process.

Question 4

How does Entroly's approach differ from traditional RAG (Retrieval-Augmented Generation)?

Accepted Answer

Unlike RAG which uses embedding similarity to retrieve top-K chunks, Entroly provides 100% codebase visibility, uses information-theoretic optimization (knapsack solver), handles duplicates efficiently, is dependency-aware, and learns from AI response quality over time to optimize context selection.

Question 5

What is Entroly and what problem does it solve?

Accepted Answer

Entroly is a context engineering engine for AI coding agents. It solves the problem of limited context window visibility by compressing your entire codebase into the AI's context, significantly reducing token consumption, and improving AI response quality.

Entroly

Entroly

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主要功能

使用案例