eco-ai 0.1.0

Analiza el impacto ecológico de tus prompts de IA y sugiere cómo optimizarlos

eco-ai

A command-line tool to estimate the ecological impact of your AI prompts and improve how you write them.

It calculates energy consumption, CO₂ emissions, water usage, and API cost — all locally, without sending any data to external servers.

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Installation

Requires Python ≥ 3.8. Pick the method that suits you:

Method	Command	Notes
pipx ⭐	pipx install eco-ai	Recommended — isolated install, no conflicts
pip	pip install eco-ai	Use inside a virtual environment
uv	uv tool install eco-ai	Fastest option if you already use uv

Don't have Python yet? Download it from python.org/downloads — no custom settings needed during install.

Manual install (for contributors)

git clone https://github.com/your-username/eco-ai.git
cd eco-ai
pip install -e .

Verify the install:

eco-ai --help

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Usage

eco-ai analyze "Explain what climate change is and what its main causes are"

eco-ai will show you:

• The estimated ecological impact of your prompt (energy, CO₂, water, and API cost)
• The detected task type and its energy consumption level
• Concrete suggestions to reduce the impact by rewriting your prompt more efficiently
• The recommended AI model based on the complexity of your request

Other commands

# Compare the impact of the same prompt across all available models
eco-ai compare "your prompt here"

# List all available models and their prices
eco-ai models

# Interactive usage guide
eco-ai guide

# Change the language of suggestions (es / en)
eco-ai config --lang en

Options for analyze

Option	Description	Default
-m / --model	Reference model for the calculation	Claude Sonnet 4.6
-f / --file	Read the prompt from a text file	—
-t / --output-type	Expected output type: auto, text, image, code, pdf, artifact	auto
--output-ratio	Estimated fraction of output tokens relative to input	0.4
--lang	Prompt language (es or en)	Saved config
--verbose	Show bibliographic references for each suggestion	—
--no-tips	Show only metrics, no suggestions	—
-y / --yes	Skip the privacy consent screen	—

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Supported models

Tier	Models
small	Claude Haiku 4.5, GPT-4o mini
medium	Claude Sonnet 4.6, Claude Sonnet 4, Gemini 1.5 Pro
large	Claude Opus 4.6, Claude Opus 4, GPT-4o, Llama 3 70B

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How it works

1. Tokenisation — counts tokens using tiktoken (cl100k_base) or approximates with words × 1.3
2. Energy metrics — estimates kWh based on the model tier (Luccioni et al., 2023)
3. CO₂ — converts energy to emissions using a cloud provider emission factor (IEA, 2023)
4. Water — estimates data centre cooling consumption (Microsoft Sustainability Report, 2023)
5. Prompt analysis — detects high-energy verbs, missing output limits, incomplete ROCKS structure, courtesy phrases, vague language, and more
6. Model recommendation — suggests the most appropriate tier based on the prompt's complexity

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Sources

• Luccioni et al. (2023) Power Hungry Processing: Scrutinizing Energy Use in NLP
• Liu et al. (2023) Lost in the Middle: How Language Models Use Long Contexts
• Min et al. (2022) Rethinking the Role of Demonstrations in Few-Shot Prompting
• Webson & Pavlick (2021) Do Prompt-Based Models Really Understand the Meaning of Their Prompts?
• Anthropic Prompt Engineering Guide (2024)

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Privacy

All analysis happens on your device. Your prompt text is never sent to any external server.

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License

MIT © Maria Prokhorova