# imaurer/awesome-llm-json

Resource list for generating JSON using LLMs via function calling, tools, CFG. Libraries, Models, Notebooks, etc.

Repository: https://github.com/imaurer/awesome-llm-json
Canonical: https://ross.abutalabs.com/products/awesome-llm-json
Homepage: https://genomoncology.com/
License: MIT
License Family: permissive
Topics: awesome-list, large-language-models, llm, function-calling, gpt-actions, structured-generation
Last push: 2025-02-18T13:48:42+00:00

## Health v2 (maintenance only)
Score: 33/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 7, release rhythm 35, longevity 89
- inputs: {"age_days": 1254, "days_push": 561, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2172, forks 94 (observed 2026-08-28T04:06:21.841008+00:00)

## What it is
A curated awesome list of resources for generating JSON and other structured outputs with Large Language Models, covering techniques like function calling, JSON mode, tool usage, and grammar-constrained (CFG) generation. It catalogs hosted and local models, Python libraries, notebooks, articles, and leaderboards.

## Use cases
- get llm to output valid json
- learn about structured outputs from language models
- find libraries for llm function calling
- constrain llm generation to a json schema
- compare models for structured output quality
- resources on guided generation with context-free grammars

## When to choose
- you need a curated starting point for LLM structured-output techniques and tooling
- you want to discover libraries, models, and tutorials for JSON generation with LLMs

## When to avoid
- you need a runnable library or tool rather than a resource list
- you need up-to-the-minute benchmark results rather than curated links

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-inference, prompt-engineering, serialization, json
- domain: large-language-models, artificial-intelligence, awesome-lists, developer-tools
- platform: python, cross-platform
- tags: awesome-list, structured-output, function-calling, json-mode, guided-generation, cfg, llm-tools

## Member repositories
- imaurer/awesome-llm-json (main) score 33

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:21.841008+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:49:13.559667+00:00, confidence not recorded.
  - readme: https://github.com/imaurer/awesome-llm-json (fetched 2026-08-28T04:06:21.841008+00:00, sha 671a8fa91dc9)
  - homepage: https://genomoncology.com/ (fetched 2026-08-29T10:29:40.714724+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.