AI Object Generator
Generate structured objects and JSON instantly
NVIDIA: Nemotron 3 Super
Balanced Nemotron for demanding everyday work
NEW
FREE
Your prompt will appear here…
Your beautifully formatted article will appear here once you generate.
No history yet
Your generations will appear here. Sign in to save them permanently.
Ever needed twenty realistic user objects for a demo and typed out three before giving up? Or copied an API response into your editor to turn it into a typed structure by hand, field by field?
Building objects is not difficult work. It is transcription, and transcription is where typos live. The AI Object Generator writes the object for you: literal data, typed structures, config shapes, seed records, in whichever language you are working in.
Short answer: The AI Object Generator is a free AIToolsay tool that produces object and data structures from a description. Give it the fields and the rules, pick a language and style, and it returns literal objects, typed structures or sample records ready to paste in.
What is AI Object Generator?
An object in this context is any structured value your language uses: a JavaScript object literal, a Python dictionary or dataclass instance, a Go struct, a C# record, a PHP array or a Ruby hash.
The prompt box asks you to describe what the object generator should produce, with requirements, inputs and expected behaviour. For objects that means the fields, their types and any constraints on their values.
Language runs from Auto Detect through Python, JavaScript, TypeScript, Java, C#, C++, Go, PHP and Ruby, so a shape described once can be produced in whatever your current file expects.
Why Use AI Object Generator?
Manual object writing goes wrong in two predictable ways. Fields get missed, and values end up unrealistic. Both are annoying rather than serious, and both cost real time to find.
Realistic values matter more than people expect. Test data full of "test1", "test2" hides bugs that only appear with a name containing an apostrophe, an address with no postcode, or a date in December. Generated objects can carry plausible values by default.
| What you need | By hand | Generated |
|---|---|---|
| One object with fifteen fields | Five minutes and a missed field | Complete, matching the description |
| Twenty varied records | You give up at four | Varied values across the whole set |
| A typed structure from a JSON sample | Manual transcription | Paste the JSON, get the type |
Who Should Use It?
- Frontend developers who need fixture data before the API exists
- Backend developers turning a documented response into a typed structure
- QA engineers building varied records to exercise edge cases
- Anyone writing a demo where the data has to look plausible on screen
- Developers porting a model between two languages field by field
Note Say what the values should look like, not only what type they are. "A UK mobile number as a string with the country code" beats "phone: string" every time, and it is the difference between usable fixture data and filler.
How Does AI Object Generator Work?
- Prompt box. Open the AI Object Generator and describe the object: fields, types, value rules and how many you want.
- Model selector. Choose the engine from MSB AI, OpenAI ChatGPT, Google Gemini, Anthropic Claude AI, xAI Grok AI, DeepSeek, Qwen, Meta AI, NVIDIA AI, OpenRouter AI and MiniMax.
- Advanced options accordion. The panel holds ten controls: Language, Code Style, Comment Level and Output as dropdowns, four toggles, a Detail Level slider and a free text field.
- Generate button. The description, model and settings run through the prompt engineering layer written for code generation, which is the instruction set that returns data rather than a description of data.
- Output card. The objects appear below the button with a live word count. Copy, listen, reuse, download or open in full view.
- Export row. DOC, TXT and HTML downloads. TXT is the sensible choice for anything going into a code file.
- Activity history. Session generations stay listed under the result, so the TypeScript version and the Python version of one shape stay side by side.
Key Features
The right shape per language
Object literal, dictionary, struct, record or hash, depending on which language you selected.
Plausible values
Names, addresses, dates and identifiers that look like real data, which is what makes a demo hold up.
Sets, not just singles
Ask for twenty varied records and the variation is across the set rather than one object repeated.
Paste ready output
Export as TXT and drop it into a fixtures file without reformatting the indentation.
Best Use Cases
- Fixture data for frontend work before the backend is ready
- Typed structures generated from a pasted JSON sample
- Config objects with every option present and commented
- Seed records for a local database, varied enough to be useful
- Edge case objects: empty collections, maximum length strings, awkward characters
- The same model expressed in two languages so a client and server agree
Caution Generated data looks real, which is exactly why it must never be treated as real. Do not use it to fill a production table, and never paste real customer data in as a template for it.
Advanced Options Guide
| Option | What it controls | When to change it | Suggested start |
|---|---|---|---|
| Language | Auto Detect, Python, JavaScript, TypeScript, Java, C#, C++, Go, PHP or Ruby | Set it explicitly, since the same shape has a different syntax in each | The language of the file you are pasting into |
| Code Style | Clean / Idiomatic, Beginner Friendly, Production Ready, Minimal, Verbose, Functional, Object Oriented or Performance Optimized | Minimal for plain literals, Object Oriented when you want constructors | Minimal for data, Clean / Idiomatic for structures |
| Comment Level | No Comments, Light Comments, Well Commented or Fully Documented | Well Commented for config objects where each option needs explaining | No Comments for fixtures, Well Commented for config |
| Output | Code Only, Code + Explanation, Code + Tests, Code + Usage Example or Step by Step | Code Only is usually right. You want data, not commentary | Code Only |
| Add Comments | Adds inline notes beside fields | Turn on for config objects, off for fixture sets | Off |
| Include Error Handling | Adds guards and validation where the object is constructed | Only relevant when generating factory functions rather than literals | Off for plain data |
| Include Example Usage | Shows the object being consumed | Useful when the object is a config someone else will pass in | Off for fixtures |
| Generate Tests | Adds tests around the structure | Only worth it when you asked for a factory or a builder | Off |
| Detail Level | Slider from 1 to 100 controlling richness and how many optional fields appear | Raise it when you want every optional field populated | 40 for literals, 70 for full config objects |
| Custom Instructions | Free text up to 1000 characters over the settings | Use it to fix locale, date format and identifier style | "UK addresses, ISO dates, ids as uuid v4 strings" |
Comparison Table
| Way of getting objects | Good for | Falls down when |
|---|---|---|
| Typing them by hand | One or two objects | You need twenty, or the shape is wide |
| A faker library | Volume with random values | Values must satisfy business rules |
| Copying production data | Total realism | Always. Do not do this |
| AI Object Generator | Realistic values that follow your rules | Very large volumes, where a library is faster |
Where it wins
- Wide objects with many fields, generated complete
- Values that respect rules a random generator would ignore
- Turning a JSON sample into a typed structure in one step
- Edge case records you would never think to type
Where to reach for something else
- Thousands of records, where a seeding library belongs
- Data that must reference real identifiers in your database
- Anything going near a production system
- ✅ Field names and types listed
- ✅ Value rules described, not just types
- ✅ Locale and formats set in Custom Instructions
- ✅ Language matches the file you are pasting into
- ✅ Nothing real was pasted in as a template
Pro tip Ask for the awkward records explicitly. An empty list, a name with an apostrophe, a date on the last day of February, a maximum length string. Those five objects find more bugs than fifty ordinary ones, and the AI Mock Data Generator is the tool to scale that set up once you know which shapes matter.
AIToolsay is a free AI tools platform where every tool is a dedicated workspace with its own prompt engineering and its own options panel, rather than one general chat box relabelled many times. Each tool is free to run and no account is needed. You also decide which engine answers, choosing from MSB AI, OpenAI ChatGPT, Google Gemini, Anthropic Claude AI, xAI Grok AI, DeepSeek, Qwen, Meta AI, NVIDIA AI, OpenRouter AI and MiniMax, and for data generation the engines vary noticeably in how plausible their values look. Next to the tools you get an AI directory, an AI models directory, courses, prompts, guides and news. The wider set is one click away on the AIToolsay homepage, with the other code generation tools grouped next to this one.
Frequently Asked Questions
Is the AI Object Generator free?
Yes. It is free to use, nothing is installed, and no account is needed to generate objects.
Can it turn a JSON response into a typed structure?
Yes, and it is one of the best uses. Paste the JSON, set Language to TypeScript, Go or whatever you need, and ask for the type definition rather than the data.
How many objects can I ask for at once?
Twenty to fifty varied records is comfortable. Beyond that the output gets unwieldy and a seeding library in your own project is the better tool.
Will the values be realistic?
They will be plausible rather than random, which is usually what you want. Set locale, formats and any business rules in Custom Instructions to keep them consistent.
Can I use the data in production?
No. It is generated sample data, and it is meant for development, tests and demos. Equally, do not paste real customer records in as a template.
What is the difference between this and a mock data tool?
This produces the shape you described, including one off structures and config objects. A mock data tool is aimed at volume. They pair well: design the shape here, scale it there.
Does it handle nested objects and arrays?
Yes. Describe the nesting in your prompt and raise Detail Level, and nested structures with populated child collections come back intact.
Fixture data is one of those jobs that never appears on a plan and always takes an afternoon. Describe the shape and the value rules once, let the AI Object Generator produce something realistic enough to be worth testing against, and get back to the code that actually needed writing.
Thanks for reading. If it saves you a transcription session, join the AIToolsay community, follow along on social media, turn on push notifications for new tools, and subscribe to the newsletter for the highlights.
Let AI Speak.
Sabir
Founder of AIToolsay and a passionate AI enthusiast dedicated to building practical, user-friendly AI tools that simplify everyday tasks.
Can't find the tool you need?
Tell us what you need and we will build it. New tools ship every week.
Request a Tool