# JSON Schema Auto-Generator (Infer from Samples) (`gochujang/json-schema-generator`) Actor 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. - **URL**: https://apify.com/gochujang/json-schema-generator.md - **Developed by:** [Hojun Lee](https://apify.com/gochujang) (community) - **Categories:** Developer tools, Automation, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 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**. **$0.01 per inference.** --- ### Why this exists You hit an API that returns JSON. You want to validate downstream payloads, store them in a typed table, or auto-generate TypeScript types — but writing a JSON Schema by hand from 30 nested fields is tedious. This actor takes one or more sample payloads and **infers** the schema. The result is a real, RFC-compliant JSON Schema you can drop into Ajv, BigQuery, OpenAPI, Apify input_schema.json, etc. --- ### What you get ```json { "_type": "schema", "samples_used": 3, "schema_uri": "https://json-schema.org/draft/2020-12/schema", "title": "User", "inferred_schema": { "$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": "", "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 ```json { "sample": { "id": 1, "email": "test@example.com", "tags": ["a", "b"], "meta": {"created": "2024-01-01", "verified": true} } } ``` #### Merge multiple samples (recommended — better union types) ```json { "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 ```json { "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](https://apify.com/gochujang/web-to-markdown) - [HTML Metadata Extractor](https://apify.com/gochujang/html-metadata-extractor) - [PDF Text Extractor](https://apify.com/gochujang/pdf-text-extractor) - [Sitemap URL Discovery](https://apify.com/gochujang/sitemap-url-discovery) *** ### Feedback A short review helps developers find it: [Leave a review on Apify Store](https://apify.com/gochujang/json-schema-generator#reviews) # Actor input Schema ## `samples` (type: `array`): Array of JSON objects to merge into one inferred schema. ## `sample` (type: `object`): Used when 'samples' is empty. ## `sampleUrls` (type: `array`): Fetch each URL as JSON and add to the sample set. ## `schemaUri` (type: `string`): Which JSON Schema version to target. ## `schemaTitle` (type: `string`): Embed in the generated schema's 'title' field. ## `schemaDescription` (type: `string`): Embed in the generated schema's 'description' field. ## `requireAll` (type: `boolean`): If false, drop the 'required' array. ## `userAgent` (type: `string`): Custom UA when fetching sampleUrls. ## Actor input object example ```json { "samples": [], "sample": {}, "sampleUrls": [], "schemaUri": "https://json-schema.org/draft/2020-12/schema", "schemaTitle": "", "schemaDescription": "", "requireAll": true, "userAgent": "" } ``` # Actor output Schema ## `dataset` (type: `string`): No description ## `summary` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("gochujang/json-schema-generator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("gochujang/json-schema-generator").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call gochujang/json-schema-generator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=gochujang/json-schema-generator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "JSON Schema Auto-Generator (Infer from Samples)", "description": "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. $0.01 per inference.", "version": "0.1", "x-build-id": "z75p1kP1aQcRB9C3M" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/gochujang~json-schema-generator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-gochujang-json-schema-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/gochujang~json-schema-generator/runs": { "post": { "operationId": "runs-sync-gochujang-json-schema-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/gochujang~json-schema-generator/run-sync": { "post": { "operationId": "run-sync-gochujang-json-schema-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "samples": { "title": "Inline samples", "type": "array", "description": "Array of JSON objects to merge into one inferred schema.", "default": [] }, "sample": { "title": "Single sample (shortcut)", "type": "object", "description": "Used when 'samples' is empty.", "default": {} }, "sampleUrls": { "title": "Sample URLs", "type": "array", "description": "Fetch each URL as JSON and add to the sample set.", "default": [], "items": { "type": "string" } }, "schemaUri": { "title": "Schema URI", "enum": [ "https://json-schema.org/draft/2020-12/schema", "http://json-schema.org/draft-07/schema#", "http://json-schema.org/draft-04/schema#" ], "type": "string", "description": "Which JSON Schema version to target.", "default": "https://json-schema.org/draft/2020-12/schema" }, "schemaTitle": { "title": "Schema title (optional)", "type": "string", "description": "Embed in the generated schema's 'title' field.", "default": "" }, "schemaDescription": { "title": "Schema description (optional)", "type": "string", "description": "Embed in the generated schema's 'description' field.", "default": "" }, "requireAll": { "title": "Mark all top-level keys as required", "type": "boolean", "description": "If false, drop the 'required' array.", "default": true }, "userAgent": { "title": "User-Agent (for sampleUrls)", "type": "string", "description": "Custom UA when fetching sampleUrls.", "default": "" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```