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JSON Schema Auto-Generator

Provide one or more JSON samples (inline or from URLs) and get an inferred JSON Schema (Draft 7 / 2020-12) describing their shape. Bootstrap API validators, Apify input schemas, BigQuery / DuckDB schemas. Powered by genson. $0.01 per inference.

Why this exists

You hit an API that returns JSON. You want to validate downstream payloads, store them in a typed table, or auto-generate TypeScript types — but writing a JSON Schema by hand from 30 nested fields is tedious.

This actor takes one or more sample payloads and infers the schema. The result is a real, RFC-compliant JSON Schema you can drop into Ajv, BigQuery, OpenAPI, Apify input_schema.json, etc.

What you get

{
"_type":"schema",
"samples_used":3,
"schema_uri":"https://json-schema.org/draft/2020-12/schema",
"title":"User",
"inferred_schema":{
"$schema":"https://json-schema.org/draft/2020-12/schema",
"type":"object",
"title":"User",
"additionalProperties":false,
"properties":{
"id":{"type":"integer"},
"email":{"type":"string"},
"tags":{"type":"array","items":{"type":"string"}},
"meta":{
"type":"object",
"properties":{
"created":{"type":"string"},
"verified":{"type":"boolean"}
},
"required":["created"]
}
},
"required":["id","email"]
},
"schema_str":"<pretty-printed schema as text>",
"top_level_keys":["id","email","tags","meta"],
"top_level_required":["id","email"]
}

The full schema is also saved as inferred_schema.json in the run's KeyValueStore — easy to download.

Quick start

Single sample

{
"sample":{
"id":1,
"email":"test@example.com",
"tags":["a","b"],
"meta":{"created":"2024-01-01","verified":true}
}
}

Merge multiple samples (recommended — better union types)

{
"samples":[
{"id":1,"name":"foo"},
{"id":2,"name":"bar","deleted_at":"2024-01-01"},
{"id":3,"name":"baz","deleted_at":null}
]
}

Fetch samples from URLs

{
"sampleUrls":[
"https://api.github.com/users/torvalds",
"https://api.github.com/users/octocat"
],
"schemaTitle":"GitHub User",
"schemaUri":"https://json-schema.org/draft/2020-12/schema"
}

Pricing

Pay-Per-Event: $0.01 per schema inference.

Cheap, fixed-cost. Run as many times as you want during API evolution.

Use cases

1. Bootstrap Apify input_schema.json — Use it on a sample input, drop the schema into your actor's .actor/input_schema.json
2. REST API validators — Generate Ajv-compatible schemas from real production responses
3. BigQuery / DuckDB tables — Convert JSON Schema → DDL with a follow-up tool
4. OpenAPI — Drop schemas under components.schemas for type-safe SDK generation
5. Comparison — Run on prod vs staging payloads; diff the schemas to spot drift

Output details

• inferred_schema — the schema as a JS object (use this in code)
• schema_str — pretty-printed text (paste into a file)
• top_level_keys — convenience for renaming / sorting fields
• additionalProperties: false is set by default for objects (strict mode). To allow extras, remove it before using the schema.

Limitations / gotchas

• More samples = better schema. A single sample can't tell required vs optional. Pass 5-10 samples covering all common cases.
• null handling — When a field is sometimes null, the inferred type becomes ["string", "null"] (or similar). This is correct JSON Schema but some tools (e.g. old Avro) don't like unions.
• Format detection — genson doesn't infer format: date-time etc. by default. For format-aware generation, post-process manually.

Engine

• genson v1.2+ — the reference Python implementation. Well-maintained, used in many data pipelines.

Related actors (same author)

• Web Page → Markdown Converter
• HTML Metadata Extractor
• PDF Text Extractor
• Sitemap URL Discovery

Feedback

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