Long Context Isn’t Free — I Built a Safe Prompt-Pruning Layer That Makes LLM Systems Work

Long-running LLM systems suffer from growing prompt sizes, which increases costs and can degrade reasoning performance. A deterministic, multi-pass pipeline prunes redundant state from prompts without using another model, ensuring predictable behavior. The process eliminates expired tool outputs and duplicate retrieved documents, then runs a dependency restoration pass to ensure no critical information is lost. Benchmarks show this approach significantly reduces token count for RAG and tool-heavy agents while preserving all necessary context.