AgentWeave filters the tool list before reasoning to cut agent cost

AgentWeave (arXiv 2608.23078) is a deterministic pre-inference routing layer that shrinks the candidate tool set an LLM sees, filtering on eligibility, requirement, capability and routing signals before an unchanged downstream model does function calling. On 48 fresh BFCL V4 multiple-function tasks with the frozen Hammer2.1-1.5b model it reached 6/48 successes versus 0/48 for all-tools, random-top-8 and semantic-top-8 baselines (McNemar p=0.03125), while exposing 70.18% fewer tools, using 61.70% fewer input tokens and cutting mean local-model latency by 50.95%. The authors themselves frame it as a BFCL-derived routing-pressure study, not a leaderboard result, and absolute success stays low.

Tool-catalog bloat is a real cost driver now that agents routinely face hundreds of MCP tools, and the token and latency cuts here are large. But treat the headline as directional: 48 tasks and a frozen 1.5b model is a stress test, not production evidence, and the 0/48 for semantic top-8 selection is the more interesting warning — similarity-based tool selection may be the wrong default.