Optimize AI workflows with Arachne. Automatically assembles the perfect code context (Tree, Target, Deps, Semantic) to fit context windows without noise. Built for efficiency and scale.
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Updated
Mar 22, 2026 - JavaScript
Optimize AI workflows with Arachne. Automatically assembles the perfect code context (Tree, Target, Deps, Semantic) to fit context windows without noise. Built for efficiency and scale.
Give one OpenClaw session durable memory, topic-aware continuity, and bounded token growth.
Given a bug and a token budget, how should you pick which code to show the LLM? This repo contains a benchmark that evaluates different context assembly methods on that question, using controlled single-line mutations across two Python repositories. Still in development, will expand to more methods, models and repositories.
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