Sculptor – Python library for LLM structured data extraction (MIT)
I built Sculptor after repeatedly seeing founders try to hire data scientists for a task that ultimately boiled down to extracting structured data from unstructured text (customer records, social posts, websites, etc) using an LLM API. We ended up reinventing this pattern internally at least three times in the past year, so I published Sculptor as a streamlined, open-source solution: - Simple schema-based extraction, with parallelization and type validation. - Multi-step pipelines with filtering or transforms between steps. - Configure everything in YAML/JSON for easy reuse. It’s MIT…
Sculptor – Python library for LLM structured data extraction (MIT) launched on January 14, 2025 with 9 votes, #2,006 of 6,142 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Sculpt: Structuring unstructured data with LLMs. Contribute to lightning-rod-labs/sculpt development by creating an account on GitHub.
Sculptor is now Sculpt. Please update any imports to reference the new package name. Sculptor streamlines structured data extraction from unstructured text using LLMs. Sculptor makes it easy to: Some examples can be found in the examples/examples.ipynb notebook. Sculptor : Extracts structured data from text using LLMs. Define your schema (via add() or config files), then extract data using sculpt() for single items or sculpt_batch() for parallel processing. SculptorPipeline : Chains multiple Sculptors together with optional filtering between steps. Often a cheap model is used to filter, followed by an expensive model for detailed analysis. from sculpt . sculptor import Sculptor import…from github.com
In the maker’s words, at launch
I built Sculptor after repeatedly seeing founders try to hire data scientists for a task that ultimately boiled down to extracting structured data from unstructured text (customer records, social posts, websites, etc) using an LLM API. We ended up reinventing this pattern internally at least three times in the past…
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