Install the plexe library using pip. Choose the installation method that best suits your needs.

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Standard Installation

This installs the core plexe library along with common dependencies needed for most tasks, excluding large deep learning libraries.

pip install plexe

This includes libraries like pandas, scikit-learn, xgboost, litellm, and smolagents.

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Lightweight Installation

For minimal dependencies, suitable if you only need the basic structure or plan to install other dependencies manually.

pip install plexe[lightweight]

This installs only the absolute minimum required packages to run the core agent logic, without data handling or specific ML libraries. Use this if you are managing dependencies tightly in a constrained environment.

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Installation with Deep Learning Support

To include optional deep learning libraries like TensorFlow and PyTorch (CPU versions by default), use the [all] extra. This is needed if you expect Plexe to generate models using these frameworks.

pip install plexe[all]

This installs everything in the standard installation plus tensorflow-cpu and torch.

If you require GPU support for TensorFlow or PyTorch, you will need to install the appropriate GPU-enabled versions separately after installing plexe[all]. Consult the official TensorFlow and PyTorch documentation for GPU installation instructions specific to your system and CUDA version.

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Verifying Installation

After installation, you can verify it by importing plexe in a Python interpreter:

import plexe

print(f"Plexe library imported successfully.")
# You can also check the internal configuration for available packages
from plexe.config import config
print(f"Allowed packages: {config.code_generation.allowed_packages}")
print(f"Deep learning available: {config.code_generation.deep_learning_available}")

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Setting API Keys

Remember to set the necessary API keys for your chosen LLM provider(s) as environment variables.

export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
# or
export ANTHROPIC_API_KEY="YOUR_ANTHROPIC_API_KEY"
# etc.

Refer to the LiteLLM Providers documentation for the correct environment variable names.