Build machine learning models using natural language.
Quickstart | Features | Installation | Documentation
plexe lets you create machine learning models by describing them in plain language. Simply explain what you want, and the AI-powered system builds a fully functional model through an automated agentic approach. Also available as a managed cloud service.
demo.mp4
You can use plexe as a Python library to build and train machine learning models:
import plexe
# Define the model
model = plexe.Model(
intent="Predict sentiment from news articles",
input_schema={"headline": str, "content": str},
output_schema={"sentiment": str}
)
# Build and train the model
model.build(
datasets=[your_dataset],
provider="openai/gpt-4o-mini",
max_iterations=10
)
# Use the model
prediction = model.predict({
"headline": "New breakthrough in renewable energy",
"content": "Scientists announced a major advancement..."
})
# Save for later use
plexe.save_model(model, "sentiment-model")
loaded_model = plexe.load_model("sentiment-model.tar.gz")Define models using plain English descriptions:
model = plexe.Model(
intent="Predict housing prices based on features like size, location, etc.",
input_schema={"square_feet": int, "bedrooms": int, "location": str},
output_schema={"price": float}
)The system uses a team of specialized AI agents to:
- Analyze your requirements and data
- Plan the optimal model solution
- Generate and improve model code
- Test and evaluate performance
- Package the model for deployment
Build complete models with a single method call:
model.build(
datasets=[dataset_a, dataset_b],
provider="openai/gpt-4o-mini", # LLM provider
max_iterations=10, # Max solutions to explore
timeout=1800 # Optional time limit in seconds
)Plexe supports distributed model training and evaluation with Ray for faster parallel processing:
from plexe import Model
# Optional: Configure Ray cluster address if using remote Ray
# from plexe import config
# config.ray.address = "ray://10.1.2.3:10001"
model = Model(
intent="Predict house prices based on various features",
distributed=True # Enable distributed execution
)
model.build(
datasets=[df],
provider="openai/gpt-4o-mini"
)Ray distributes your workload across available CPU cores, significantly speeding up model generation and evaluation when exploring multiple model variants.
Generate synthetic data or infer schemas automatically:
# Generate synthetic data
dataset = plexe.DatasetGenerator(
schema={"features": str, "target": int}
)
dataset.generate(500) # Generate 500 samples
# Infer schema from intent
model = plexe.Model(intent="Predict customer churn based on usage patterns")
model.build(provider="openai/gpt-4o-mini") # Schema inferred automaticallyUse your preferred LLM provider, for example:
model.build(provider="openai/gpt-4o-mini") # OpenAI
model.build(provider="anthropic/claude-3-opus") # Anthropic
model.build(provider="ollama/llama2") # Ollama
model.build(provider="huggingface/meta-llama/...") # Hugging Face See LiteLLM providers for instructions and available providers.
Note
Plexe should work with most LiteLLM providers, but we actively test only with openai/* and anthropic/*
models. If you encounter issues with other providers, please let us know.
pip install plexe # Standard installation
pip install plexe[lightweight] # Minimal dependencies
pip install plexe[all] # With deep learning support# Set your preferred provider's API key
export OPENAI_API_KEY=<your-key>
export ANTHROPIC_API_KEY=<your-key>
export GEMINI_API_KEY=<your-key>See LiteLLM providers for environment variable names.
For full documentation, visit docs.plexe.ai.
See CONTRIBUTING.md for guidelines. Join our Discord to connect with the team.
If you use Plexe in your research, please cite it as follows:
@software{plexe2025,
author = {De Bernardi, Marcello AND Dubey, Vaibhav},
title = {Plexe: Build machine learning models using natural language.},
year = {2025},
publisher = {GitHub},
howpublished = {\url{https://github.com/plexe-ai/plexe}},
}