Top 5 Fixes to Run Local LLM Coding Agents on Large Codebases (No More Looping!)

Running local LLMs with coding agents on large codebases often causes infinite loops, context bloating, and memory issues. Several optimizations can fix this: using a MoE model like Qwen 3.6 MTP for faster inference, switching to the PyCoding agent (which sends fewer tool calls), enabling its compaction feature to manage token budgets, installing RTK (Rust Token Killer) as a CLI proxy to reduce token consumption by 60–90%, adding custom skills for loop detection and done-signal emission, setting appropriate context lengths in model.json, tuning KV cache quantization in LM Studio, and optionally adding a system prompt that enforces strict no-loop behavior. A live demo shows a real website codebase being analyzed and modified across 8 files with 2.2 million tokens consumed and zero looping.