AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
翻译准备中
Live code graph that fuses static analysis (tree-sitter) with OpenTelemetry runtime traces, queried over MCP so AI coding agents get real full-stack context.
翻译准备中
取自仓库 README
正在加载完整 README。 以下为已保存的摘录。
NEAT solves the AI coding context problem. It constructs a live deterministic model of your codebase — static code and live runtime behavior fused into one graph — and hands your AI agents the grounded, full-stack context they need to query, code, debug, and write rules against it. This achieves the following:
• Coding LLMs hallucinate less & are more accurate.
• Rather than endlessly grepping files and guessing problems, NEAT provides time-travelling error logs along the model's nodes and edges so the LLM can infer exactly what's wrong.查看完整 README ↗目前无法加载完整 README。请通过原文链接阅读。 以下为已保存的摘录。
NEAT solves the AI coding context problem. It constructs a live deterministic model of your codebase — static code and live runtime behavior fused into one graph — and hands your AI agents the grounded, full-stack context they need to query, code, debug, and write rules against it. This achieves the following:
• Coding LLMs hallucinate less & are more accurate.
• Rather than endlessly grepping files and guessing problems, NEAT provides time-travelling error logs along the model's nodes and edges so the LLM can infer exactly what's wrong.查看完整 README ↗