Compressor V2: three compression layers for a 50% LLM agent cost cut

8 hours ago

Token compression is critical for coding agents due to economic pressure and operational bottlenecks like dollar cost, latency, context window limits, and throughput.
Edgee's Compressor V2 includes three orthogonal strategies: brevity (reducing output tokens), tool surface reduction (TSR) for MCP tool catalogs, and tool result trimming for verbose tool outputs.
Statistical analysis using paired sign tests, bootstrap confidence intervals, and coefficient of variation shows significant savings: brevity achieves ~30% cost reduction on SWE-bench Lite, TSR reduces tokens by ~33%, and tool result trimming adds incremental 5–10% savings.
Brevity works by eliminating meta-text in outputs, preserving cache amortization, and targeting the most expensive token class without prefix changes.
TSR rewrites tool catalogs into a single virtual tool, reducing prefix bloat and token volume, especially beneficial for tool-heavy workloads.
The three strategies are composable and target different layers (output, prefix, history), allowing users to enable combinations per API key based on workload shape.

Hasty Briefsbeta