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TinyFish's BigSet addresses the persistent pipeline complexity of building structured datasets by using a two-tier multi-agent system to infer schemas and scrape live web data from natural language prompts. While the process requires 2–5 minutes for real-time research and deduplication, the open-source AGPL-3.0 release offers a scalable alternative to manual scraper configuration and maintenance. This approach effectively bridges the gap between abstract data requirements and ready-to-use CSV or XLSX files with automated refresh capabilities.

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TinyFish Launches BigSet: An Open-Source Multi-Agent System That Builds Structured Live Datasets from Plain-English Descriptions

MarkTechPost2 iunie 20269 min de cititArticolul și perspectiva sunt în engleză.

Describe a dataset in one sentence; Bigset's orchestrator and parallel sub-agents research the live web and return structured tables. The post TinyFish Launches BigSet: An Open-Source Multi-Agent System That Builds Structured Live Datasets from Plain-English Descriptions appeared first on MarkTechPost.

Building a structured dataset from the web is still a pipeline problem. You identify a data source, write or configure a scraper, design a schema, handle deduplication, schedule refreshes, and fix breakage when upstream sites change. That process stays roughly the same whether you do it once or a hundred times. TinyFish is releasing BigSet to address that workflow directly. Bigset is an open-source multi-agent system licensed under AGPL-3.0. It takes a natural-language description as input and returns a structured, exportable dataset built from live web data. The full codebase is available on GitHub. Bigset positions itself as the layer between a data requirement and a usable table. You describe what you want in a sentence. The system infers the schema, dispatches agents to gather data, deduplicates results, and produces a downloadable CSV or XLSX file. A practical…

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