reaatech/context-window-planner

These packages give you a deterministic engine for deciding what content to include, summarize, or drop when packing prompts into an LLM's context window. You'd adopt them to solve the problem of overflowing a model's token budget—replacing ad-hoc truncation with a configurable planner that enforces budgets, reserves space for generation, and emits structured warnings about every decision. The most distinctive thing is the pluggable strategy system: you can swap between priority-greedy, sliding-window, summarize-and-replace, or RAG relevance selection strategies, or compose custom ones, all while using typed context item primitives and tokenizer adapters for different model families.