Smart Context Packing: Keep Critical Information Within Token Budgets

RAG applications and LLM agents consistently face the bottleneck of fitting system prompts, chat history, retrieved documents, and tool outputs into a fixed context window. contextcram solves this by replacing blind truncation with intelligent priority-based packing, preserving critical information while strategically removing less important content. With built-in model-aware budgeting and response reserves, it eliminates the subtle failure mode where prompts fit but leave insufficient tokens for the model to generate answers.