chu2bard

chu2bard / structify

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Type-safe structured output extraction from LLMs

json-schema llm openai structured-output typescript
16
0
69% credibility
Found Feb 11, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

Structify is a helper library for pulling neatly organized data from AI language model responses based on predefined patterns, with built-in fixes for errors and support for popular AI services.

How It Works

1
🕵️ Discover the Helper

While working on a project that uses AI to understand text, you find structify, a smart tool that turns messy chat responses into neat, organized information.

2
📥 Add to Your Project

You easily bring this helper into your own work, so it's ready to assist right away.

3
📋 Describe Your Data Needs

You simply outline the exact pieces of info you want, like names, numbers, or lists, in a clear way the tool understands.

4
📝 Share Some Text

You hand over a snippet of text from an AI conversation that holds the details you're after.

5
🔄 It Extracts and Fixes Automatically

The tool reads the text, pulls out the structured info perfectly, and if something's off, it asks the AI to correct it until everything fits just right.

🎉 Get Perfect Organized Data

You now have clean, reliable data ready to power your project, making everything work smoothly and accurately.

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Star Growth

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AI-Generated Review

What is structify?

Structify is a TypeScript library for type-safe structured output extraction from LLMs, using Zod schemas to parse prompts into reliable JSON objects. It handles messy LLM responses by validating output, feeding errors back for automatic retries, and supports OpenAI and Anthropic providers out of the box. Developers get clean, typed data like person names, ages, or skills arrays from natural language text, without manual string hacking.

Why is it gaining traction?

It stands out with smart retry logic that corrects parse failures on the fly, batch processing for concurrent extractions, and easy custom provider adapters—saving time over raw API calls or brittle regex parsers. The Zod integration delivers full TypeScript inference, making structured extraction from LLMs feel native and error-proof. Type_safe GitHub projects like this hook devs tired of flaky AI outputs in production pipelines.

Who should use this?

Backend engineers building RAG apps or chatbots that need to extract entities from user queries, like addresses or product specs. AI prototyping teams handling LLM extraction for data pipelines, avoiding custom validation boilerplate. TypeScript devs integrating structured output into Node.js services with OpenAI or Anthropic.

Verdict

Worth a spin for small-scale LLM extraction needs—it's functional, MIT-licensed, and delivers on type-safe promises—but with only 16 stars and a 0.699999988079071% credibility score, expect basic docs and no tests; treat it as an early prototype, not production-ready without tweaks.

(178 words)

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