A context engineering engine that compresses your entire codebase for AI coding agents, reducing token usage by 70-95% while providing full code visibility.
Entroly is a context engineering engine that solves the 'AI blindness' problem in coding assistants by compressing your entire codebase into the AI's context window. It uses mathematical optimization to select the most relevant code fragments at variable resolutions, reducing token usage by 70-95% while ensuring the AI has full dependency awareness to prevent hallucinations.
Developers using AI-powered coding tools like Cursor, Claude Code, GitHub Copilot, Windsurf, or Cody who want to improve accuracy and reduce costs by giving their AI complete codebase context.
Developers choose Entroly because it provides a zero-configuration solution that dramatically reduces AI hallucination and token costs, runs entirely locally for privacy, and uses a performant Rust engine for near-instant optimization without requiring complex embeddings or prompt engineering.
Open-source context engine that catches AI hallucinations and cuts your token bill 70–95%. The only AI helper that shows its work. Claude · Cursor · Copilot · Codex,GPT & Custom Providers
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The `entroly go` command auto-detects your IDE and configures everything in 30 seconds, eliminating manual prompt engineering as demonstrated in the installation section.
Achieves 70-95% fewer tokens through mathematical optimization, reducing costs from ~$560 to as low as $28 per 1K requests, with specific benchmarks in the README.
All processing runs locally, ensuring your code never leaves your machine, highlighted in the privacy-first philosophy and architecture details.
Uses a Rust core for 50-100x faster processing with less than 10ms overhead, backed by performance claims and modular design in the technical deep dive.
Deeply integrates with OpenClaw for context sharing across agents, providing tailored token savings for different agent types as shown in the integration table.
The README lists common issues like macOS 'externally-managed-environment' errors and Windows pip problems, requiring additional troubleshooting steps that complicate setup.
Since it runs locally, it may not scale for distributed teams or cloud-only workflows, and fallback to Python engine if Rust fails can degrade performance.
While it supports major IDEs, integration is limited to listed tools like Cursor and Claude Code; newer or niche AI assistants might require manual configuration or lack support.
As a newer project, it lacks the extensive community and third-party integrations of established RAG systems, and some features like RL weight learning are optional or experimental.