ell-ai

What it is and what it does

ell-ai treats language model interactions as first-class Python functions rather than string templates. You decorate a function with @ell.simple() or similar, write your system and user messages as function body code, and call it like any other function. The framework automatically versions and serializes your prompts to a local store, tracks iterations similar to machine learning checkpointing, and provides tooling for monitoring and visualization through Ell Studio.

The package emphasizes multimodal-first design: you can pass PIL images, audio, and other content types directly into message objects alongside text, with automatic type coercion. It integrates with OpenAI's API and depends on numpy, requests, cattrs, black, pillow, and other utilities to handle serialization, formatting, and image processing. The framework is still in alpha and has been actively maintained, with support for Python 3.7 through 3.13.

Use it for:

• Build and iterate on LLM prompts with automatic versioning and commit-like snapshots without leaving Python code.
• Develop multimodal applications that combine text, images, and other content types in a single prompt function.
• Monitor and visualize prompt engineering experiments over time using Ell Studio to catch regressions.
• Organize complex LLM workflows as composable Python functions with type hints and clear parameter passing.
• Prototype and test language model applications with rich tooling for prompt optimization and comparison.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

ell-ai is a functional prompt engineering framework that lets you write language model interactions as Python functions, with built-in versioning, serialization, and multimodal support for text, images, and other content types.

Yes, if you are actively doing prompt engineering with OpenAI models and want structured versioning and monitoring. The low install friction, permissive license, and recent maintenance make it a reasonable choice for iterative LLM development. However, the alpha status and dependence on 10 runtime packages mean you should verify it fits your specific workflow before committing to it in production.

Install

ell-ai on PyPI

pip install ell-ai

uv add ell-ai

poetry add ell-ai

Installing ell-ai

Before you install

Low friction install with a pure-Python wheel. The package is in alpha status and has been actively maintained with a recent release, though it depends on 10 runtime packages including openai and requests, which adds some transitive complexity.

License in practice

MIT license is permissive; you can use, modify, and distribute ell-ai freely in commercial and private projects with minimal restrictions.

Quickstart

pip install ell-ai

import ell

@ell.simple(model="gpt-4o")
def hello(world: str):
    """You are a helpful assistant."""
    return f"Say hello to {world}"

hello("user")

Requires Python 3.9 or later; you must have an OpenAI API key configured to use the framework with gpt-4o or other OpenAI models.

Verify before relying

• Whether the local Ell Studio tool (mentioned in description) is included in the base install or requires separate setup.
• Performance characteristics and latency overhead of the automatic versioning and serialization process.
• Compatibility with non-OpenAI language model providers beyond what the fact sheet indicates.

Package facts

Evidence: ell_ai-0.0.17-py3-none-any.whl

Tags

More Artificial Intelligence packages

Further reading