entroly: Entroly is a context engineering engine that compresses an entire codebase into the AI context window using variable-resolution representation, removing duplicates and boilerplate. It achieves 78% fewer tokens per request, improves AI responses over time via reinforcement learning, and works with any AI coding tool via MCP server or HTTP proxy. The Rust-based core ensures sub-10ms overhead.; context-mode: Every tool call in an MCP (Model-Controller-Program) environment dumps raw data into the context window, quickly consuming space and causing the agent to lose track of ongoing tasks. Context Mode is an MCP server that tackles this by sandboxing tool outputs to significantly reduce context usage, tracking session events in SQLite for continuity, and promoting 'think in code' to minimize data processing within the LLM.
Optimizing context for coding agents like Cursor and Claude Code
Deep repository research and analysis (e.g., architecture, contributors, issues)