plexe ✨

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

1. Quickstart

Installation

pip install plexe

Using plexe

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")

2. Features

2.1. 💬 Natural Language Model Definition

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}
)

2.2. 🤖 Multi-Agent Architecture

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

2.3. 🎯 Automated Model Building

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
)

2.4. 🚀 Distributed Training with Ray

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.

2.5. 🎲 Data Generation & Schema Inference

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 automatically

2.6. 🌐 Multi-Provider Support

Use 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.

3. Installation

3.1. Installation Options

pip install plexe                  # Standard installation
pip install plexe[lightweight]     # Minimal dependencies
pip install plexe[all]             # With deep learning support

3.2. API Keys

# 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.

4. Documentation

For full documentation, visit docs.plexe.ai.

5. Contributing

See CONTRIBUTING.md for guidelines. Join our Discord to connect with the team.

6. License

Apache-2.0 License

7. Product Roadmap

• Fine-tuning and transfer learning for small pre-trained models
• Use Pydantic for schemas and split data generation into a separate module
• Plexe self-hosted platform ⭐ (More details coming soon!)
• Lightweight installation option without heavy deep learning dependencies
• Distributed training with Ray on AWS
• Support for non-tabular data types in model generation

8. Citation

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}},
}