drip 💧

How much water is your AI agent drinking? drip estimates the water used by terminal AI agents (Claude Code and Codex CLI) and shows it live in your status line, a split-pane meter, or a full breakdown page, with every number traced back to published research.

Opus 5.5 │ my-project │ ████░░░░░░ 48% │ 💧 412 mL (1.7 glasses) · today 3.1 L

• Real token counts. drip reads your agent's local logs, so the token counts aren't guesses.
• Research-backed coefficients. Low, mid and high estimates, each with its source cited.
• Private. Everything is local: no accounts, no telemetry, and nothing leaves your machine.
• No dependencies. Python 3.11+ standard library only.

Install

git clone https://github.com/beausterling/drip-ai-water-usage.git ~/.drip
~/.drip/bin/drip install

install plays a short intro, imports your existing Claude Code and Codex history, puts drip on your PATH (via ~/.local/bin), and turns on the Claude Code status line. If you already have a custom status line, it shows the line to add instead of replacing yours.

Ways to see it

drip run uses whatever layout your terminal supports:

The meter closes automatically when the agent exits. So far only the iTerm2 setup has been tested by hand. The others follow each terminal's documentation; please open an issue if one misbehaves.

Commands

drip                          this session + today + last 14 days
drip open                     browser breakdown
drip watch [--tool codex]     live meter for a split pane
drip run <cmd>                run an agent with the meter beside it
drip history --by day|week|month|model|tool|project [--days N]
drip session [ID]             per-model breakdown + low/mid/high range
drip explain [MODEL]          mL per 1K tokens and where each number comes from
drip splash                   replay the intro animation
drip sync                     import new log data (usually automatic)

--band low|mid|high           default mid (or DRIP_BAND)
--onsite                      count data-center cooling water only (or DRIP_ONSITE=1)
DRIP_LINKS=0                  turn off the clickable status-line link

How the estimate works

water (mL) = tokens × energy per token (Wh) × water per kWh (L/kWh)
water per kWh = on-site cooling (WUE ÷ PUE) + water used to generate the electricity (EWIF)

• Token counts:
  • Claude Code: transcripts in ~/.claude/projects, including subagents, counted once per API response.
  • Codex: rollout logs in ~/.codex/sessions.
• Energy per token: based on measured inference energy (ML.ENERGY, Microsoft in Joule, Google) and Claude Code-specific analyses. Cache reads are counted at a fraction of normal input.
• Water per kWh: based on LBNL's 2024 US data center report, Li et al. ("Making AI Less Thirsty", CACM 2025), and provider disclosures.
• Per-model scaling: uses list price, the only public signal of relative serving cost. Models drip doesn't know show a ~.
• Scope: inference only. Training and hardware manufacturing are excluded.

The full derivation is in RESEARCH.md, and every source is linked on the breakdown page. The coefficients live in drip/coefficients.toml. To override them, copy the file to ~/.config/drip/coefficients.toml. drip stores only token counts (~/.local/share/drip/usage.db) and computes water when you view it, so edited coefficients re-price all of your history.

How accurate is it?

• Token counts: exact.
• Water: an estimate. The high estimate is about 10–30× the low one, because no AI provider publishes energy per token. The biggest unknown is the energy cost of cache reads, which make up most of an agent's tokens.
• Reliable: trends and comparisons with yourself ("3× more than last week").
• Not reliable: exact litres, or comparisons between models.

Support

drip is free and MIT-licensed. If it's useful, you can chip in on Gumroad (link coming soon).

Development

python3 -m unittest tests/test_drip.py

MIT © 2026 Beau Sterling