Let Local LLMs Search the Web Without Burning 100k+ Tokens (medium.com)

TinySearch has been launched as a local-first web research tool specifically designed for smaller local LLM models and users who need streamlined web searches without cumbersome context overhead. Developed to address the common frustrations associated with existing search tools that inundate models with irrelevant text and high token costs, TinySearch optimizes the web research process. It simplifies the workflow to just search, crawl, rank, and return a compact, source-grounded prompt that empowers local models to generate accurate responses without overwhelming them with excessive data. The significance of TinySearch lies in its capacity to enhance the functionality of local agents and reduce the burden of extraneous context, making it especially relevant for the AI/ML community focusing on smaller models and experimental workflows. Instead of delivering vast amounts of unstructured text, TinySearch provides concise, relevant pieces of evidence alongside clear citations and instructions, ensuring that the LLM has access to clean data for reasoning. This tool supports a variety of applications, from personal research to MCP workflows, and contributes to the growing trend of developing efficient, localized AI solutions.