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Alternatives

Products that do what Structs does

From raw text to structured JSON available as MCP Tools

  1. 1
    Structura100

    Visualize and edit JSON like Lego blocks

    Oct 2025

  2. 2DS
  3. 3JF

    After two years of improvement, I think it's time to share it with you all. Here’s a quick overview: - Common features include validation, formatting, minification, and more. - Visualize JSON in a graph or table view. - Structured comparison with fallback to text comparison. - Navigate though JSON using JSON pointer. - Supports jq. Would love to hear the community's questions, thoughts and comments!

    2024 · github.com

  4. 4

    Extract web data into structured JSON, no scraper required.

    Jun 2026 · tabstack.ai

  5. 5

    Extract structured data from any website

    2018

  6. 6

    Get structured web data with just a prompt

    2025

  7. 7DO

    Documind is an open-source tool that turns documents into structured data using AI. What it does: - Extracts specific data from PDFs based on your custom schema - Returns clean, structured JSON that's ready to use - Works with just a PDF link + your schema definition Just run npm install documind to get started.

    2024 · github.com

  8. 8SO

    Built a tool for transforming unstructured data into structured outputs using language models (with 100% adherence). If you're facing problems getting GPT to adhere to a schema (JSON, XML, etc.) or regex, need to bulk process some unstructured data, or generate synthetic data, check it out. We run our own tuned model (you can self-host if you want), so, we're able to have incredibly fine grained control over text generation. Repository: https://github.com/automorphic-ai/trex Playground: https://automorphic.ai/playground

    2023 · automorphic.ai

  9. 9

    Extract structured content from the semantic web

    2021

  10. 10GJ

    Hey HN, I've been using GPT a lot lately in some side projects around data generation and benchmarking. During the course of prompt tuning I ended up with a pretty complicated request: the value that I was looking for, an explanation, a criticism, etc. JSON was the most natural output format for this but results would often be broken, have wrong types, or contain missing fields. There's been some positive movement in this space, like with jsonformer (https://github.com/1rgs/jsonformer) the other day. But nothing that was plug and play with GPT. This library consolidates…

    2023 · github.com

  11. 11AT

    Hey HN! Erik here from banana.dev We’ve trained a small(ish) language model on structured extraction, and today we’re launching a playground for it at https://anythingtojson.com. Give it a try! This model continues our work on structured generation, following last week’s launch of Fructose[1], a python client for strongly-typed LLM responses. There seem to be two distinct halves of the problem intended to be solved by Fructose and structured generation: 1. the reasoning ability of the model, such as performing chain of thought, creative acts, and natural language tasks. In a way,…

    2024 · anythingtojson.com

  12. 12
    ST162

    Bend JSON to your will like never before

    2017

  13. 13

    A tool to query JSON data structures

    2015

  14. 14SR
  15. 15JM

    2012 · github.com

  16. 16
    Structa116

    Design databases with AI, edit with clicks

    Nov 2025

  17. 17JS
  18. 18

    Fast, private JSON tools for developers

    Apr 2026 · structkit.dev

  19. 19

    Tool for lazy developers to convert JSON to C# Objects

    2020

  20. 20JA
  21. 21JQ
  22. 22AN

    When building workflows that rely on LLMs, we commonly use structured output for programmatic use cases like converting an invoice into rows or meeting transcripts into tickets or even complex PDFs into database entries. The model may return the schema you want, but with hallucinated values like `invoice_date` being off by 2 months or the transcript array ordered wrongly. The JSON is valid, but the values are not. Structured output today is a big part of using LLMs, especially when building deterministic workflows. Current structured output benchmarks (e.g., JSONSchemaBench) only validate…

    Apr 2026 · interfaze.ai

  23. 23SR
  24. 24AT

    Hello all To preface this is just something I've been making as a learning exercise, so all feedback is appreciated. This is a tool that converts JSON schemas into TypeScript utility classes for use in Deno. Automatic Type Generation: Typescript interfaces for the compressed and uncompressed versions of your data. Compression & Decompression: Compress and decompress your data. Validation: Built-in data validation using Ajv ensures your data adheres to the schema. Reusability: Once generated, the utility classes can be used in other Deno projects. It currently only supports a subset of JSON…

    2023 · github.com

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