SDK Documentation | Platform Documentation | Usage Examples

The official SDK of MOSTLY AI, a Python toolkit for high-fidelity, privacy-safe Synthetic Data.

• Client mode connects to a remote MOSTLY AI platform for training & generating synthetic data there.
• Local mode trains and generates synthetic data locally on your own compute resources.
• Generators, that were trained locally, can be easily imported to a platform for further sharing.

The SDK allows you to programmatically create, browse and manage 3 key resources:

1. Generators - Train a synthetic data generator on your existing tabular or language data assets
2. Synthetic Datasets - Use a generator to create any number of synthetic samples to your needs
3. Connectors - Connect to any data source within your organization, for reading and writing data

Client mode only

pip install -U mostlyai

Client + Local mode

pip install -U 'mostlyai[local]'       # for CPU
#pip install -U 'mostlyai[local-gpu]'  # for GPU

NOTE: installing mostlyai[local] on Linux requires --extra-index-url https://download.pytorch.org/whl/cpu to be specified.

Optional Connectors

Add any of the following extras for further data connectors support: databricks, googlebigquery, hive, mssql, mysql, oracle, postgres, snowflake.

E.g.

pip install -U 'mostlyai[local, databricks, snowflake]'

For client mode, initialize with base_url and api_key obtained from your account settings page. For local mode, initialize the client simply with local=True.

import pandas as pd
from mostlyai.sdk import MostlyAI

# 1) Initialize the SDK in local or client mode
mostly = MostlyAI(local=True)
# mostly = MostlyAI(base_url='https://app.mostly.ai', api_key='YOUR_API_KEY')

# 2) Load your original data
trn_df = pd.read_csv('https://github.com/mostly-ai/public-demo-data/raw/dev/census/census.csv.gz')

# 3) Train a synthetic data generator
g = mostly.train(name='census', data=trn_df)  # shorthand syntax for 1-table config

# 4) Live probe small synthetic samples
df_samples = mostly.probe(g, size=10)

# 5) Generate a full synthetic dataset
sd = mostly.generate(g, size=100_000)
syn_df = sd.data()

• Broad Data Support
  • Mixed-type data (categorical, numerical, geospatial, text, etc.)
  • Single-table, multi-table, and time-series
• Multiple Model Types
  • TabularARGN for SOTA tabular performance
  • Fine-tune HuggingFace-based language models
  • Efficient LSTM for text synthesis from scratch
• Advanced Training Options
  • GPU/CPU support
  • Differential Privacy
  • Progress Monitoring
• Automated Quality Assurance
  • Quality metrics for fidelity and privacy
  • In-depth HTML reports for visual analysis
• Flexible Sampling
  • Up-sample to any data volumes
  • Conditional generation by any columns
  • Re-balance underrepresented segments
  • Context-aware data imputation
  • Statistical fairness controls
  • Rule-adherence via temperature
• Seamless Integration
  • Connect to external data sources (DBs, cloud storages)
  • Fully permissive open-source license

Please consider citing our project if you find it useful:

@software{mostlyai,
    author = {{MOSTLY AI}},
    title = {{MOSTLY AI SDK}},
    url = {https://github.com/mostly-ai/mostlyai},
    year = {2025}
}