Practical overview
When it fits
Use Synthgenai when you want a Python-based CLI workflow for requesting synthetic data from an LLM provider, or a modular codebase to adapt for dataset-generation tasks.
Who it is for
- Python developers preparing synthetic datasets for AI or machine-learning workflows.
- Teams wanting a modular starting point for LLM-backed dataset generation across provider backends.
Documented uses
- Generate a dataset by selecting a dataset type, model, topic, domain and entry count.
- Review available dataset types and supported LLM providers from the CLI.
- Extend a modular dataset-generation package with provider support or features.
- Optionally save generated datasets to Hugging Face Hub.
Requirements & limits
The package is MIT-licensed and can be installed with pip or uv. Python 3.10 or later is required. Saving datasets to Hugging Face Hub requires an account and generated token; this is optional.
- The package itself is the dataset-generation library; LiteLLM is a component used for standardized access to multiple LLM providers.
- The README lists a broad set of providers, including OpenAI, Anthropic, Gemini, Bedrock, Groq, Mistral AI, Hugging Face, Ollama and vLLM.
- Ollama, Langfuse, Hugging Face Hub and Gradio are described as optional for specific use cases.
- The project is early in development, and some features may be incomplete or untested.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Synthgenai and inspect CLI options
Set up the package and use its documented commands to review environment setup, dataset types and provider options.
Before you start
- Python 3.10 or later
- pip or uv
Steps
Install the package with the command below or the command below.
pip install synthgenaiuv add synthgenaiRun
synthgenai env-setupto view required environment variables.Run
synthgenai list-typesto see available dataset types.Run
synthgenai providersto list supported LLM providers.
Expected outcome
The package is installed, and its CLI displays setup guidance, dataset types and provider choices.
Guide limitations
- The maintainers say the project is in early development and some features may be incomplete or untested.
Generate an instruction dataset from the CLI
Use the README’s example workflow to request a topic- and domain-specific synthetic dataset.
Before you start
- Python 3.10 or later and Synthgenai installed
- An API key configured for the chosen provider; the example uses an OpenAI API key
Steps
Run
synthgenai env-setupto view environment-variable requirements.Set the API key for the provider you plan to use; the README demonstrates
OPENAI_API_KEY.Run
synthgenai generate instruction --model "openai/gpt-5" --topic "Python Programming" --domain "Software Development" --entries 100.
Expected outcome
The CLI is instructed to generate 100 instruction entries on Python programming in the software-development domain.
Guide limitations
- The README cautions that some features may be incomplete or untested; the example does not establish output quality or suitability for a particular use.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- Shekswess/synthgenai · Official homepage title · retained in its source language