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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.
Source coveringMarkTechPost
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

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

MarkTechPost9 min read

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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