I built a context window optimization framework for coding agents — open source + paper

Been working on a problem that I think a lot of people here face: agentic coding pipelines blowing through their context window way too fast, losing important information, and degrading task quality mid-session. Apohara Context Forge is my approach to this. It's a methodology + implementation for structured context assembly in LLM agents — basically a tiered relevance scoring system that decides what goes into the context window and in what order, depending on the current task and agent role. Key ideas: \- Role-aware context segmentation (different agents need different context shapes) \- Tiered priority scoring to evict low-value tokens first \- Benchmarked against vanilla context packing — significant improvement in task completion on long sessions \- Works with any model (Claude, GPT-4o, Gemini, local models) Happy to answer questions or discuss the design decisions.