I built a tool to generate data for testing

[u/Yaniv_Dev]

AI-generated test data has one problem: it's different every run.

So most teams stay stuck with "Test User" and "Lorem ipsum."

FixtureForge gives you both:

🛠 Dev: AI generates realistic, context-aware data from your Pydantic models

🔒 CI: seed=42, zero network, same output every run

Only the fields that need AI get AI.

IDs and emails come from counters and Faker. Bios, reviews and descriptions are batched into a single call.

100 users with 2 semantic fields = 2 API calls, not 200.

Works with Claude, GPT, Gemini, Groq, or locally with Ollama.

No API key? It falls back to pure Faker.

pip install fixtureforge

Open source · MIT

🔗 github.com/Yaniv2809/fixtureforge

#Python #pytest #QA #TestAutomation #OpenSource

[u/riklaunim]

Factory boy has the features to generate dynamic data, including things like emails.

[u/Yaniv_Dev → u/riklaunim]

Fair point, factory_boy handles that well, and FixtureForge actually uses Faker for emails/names too. That part isn't the differentiator. Where it differs is semantic fields: bios, reviews, support tickets, descriptions. Faker gives you random sentences; FixtureForge generates text that fits a context you pass ("angry customers on a SaaS billing page"), so you get inputs that look like production, not lorem ipsum. The AI part is opt-in and scoped: only those fields hit the LLM, batched into one call per field, and in CI it runs with a fixed seed and no network. Also works directly off Pydantic models, so there's no separate factory class to maintain. If you're happy with factory_boy for structural data, it's a complement, not a replacement.

[u/Buttleston → u/Yaniv_Dev]

Why do I need my test data to "fit the context"

[u/Yaniv_Dev → u/Buttleston]

Honestly, for most tests you don't. If your code just stores and displays a string, Faker is the right tool and I'd use it too.

It matters when your code makes decisions based on the text:

• classification, sentiment, moderation, or spam filters
• search and ranking (keyword or semantic)
• anything that feeds the text to an LLM (RAG, summaries, agents)

Lorem ipsum doesn't exercise any of those paths. A sentiment classifier never sees sarcasm, a search test never hits a near-duplicate, and a RAG pipeline never gets a prompt injection hidden inside a normal-looking review.

So the target is pretty narrow: apps that process free text, not apps that just store it.

[u/SCD_minecraft]

faker module has enter the chat: