Local web search tool says it cuts token use 87% and cost 66%
Remote-Breadfruit204 · reddit · 2026-07-23
A local web search stack claims 87% fewer tokens and 66% lower cost
The author built webfetch as an alternative to hosted search from Anthropic and OpenAI, arguing that the default model is expensive: $10 per 1k searches plus roughly 17k result tokens per query.
Key pieces of the system:
- RRF across 4 search engines, local page fetching, BM25 + bi-encoder retrieval, and a cross-encoder reranker
- Sentence-level compression that halves result tokens without measured recall loss
- Semantic caching with embedding match + NLI verification and adaptive TTLs
- Provenance shown for cached results, with the model able to force a fresh search
On its SimpleQA benchmark, the author says the same agent loop matched hosted search at 96% accuracy while using 87% fewer tokens and costing 66% less. One test loop of 16 searches reportedly saved $1.50. It ships as a PyPI install and can be added to Claude Code as an MCP server.
Related event: Open-source Webfetch slashes AI agent search costs and tokens(4 posts)→
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