A programming language AI agents write, not humans. Named from Toki Pona for "tool".

Python                                    ilo
─────                                     ───
def total(price, quantity, rate):          tot p:n q:n r:n>n;s=*p q;t=*s r;+s t
    sub = price * quantity
    tax = sub * rate
    return sub + tax

4 lines, 30 tokens, 90 chars              1 line, 10 tokens, 20 chars

0.33× the tokens. 0.22× the characters. Same semantics. Type-verified before execution.

AI agents pay three costs per program: generation tokens, error feedback, retries. ilo cuts all three:

• Shorter programs - prefix notation eliminates parentheses; positional args eliminate boilerplate
• Verified first - type errors caught before execution; agents get ILO-T004 not a stack trace
• Compact errors - one token, not a paragraph; agents correct faster, fewer retries

curl -fsSL https://raw.githubusercontent.com/ilo-lang/ilo/main/install.sh | sh

Invoke-WebRequest -Uri https://github.com/ilo-lang/ilo/releases/latest/download/ilo-x86_64-pc-windows-msvc.exe -OutFile ilo.exe

npm i -g ilo-lang

# or run without installing
npx ilo-lang 'dbl x:n>n;*x 2' 5

cargo install ilo

All install methods →

# Inline
ilo 'dbl x:n>n;*x 2' 5                    # → 10

# From file
ilo program.ilo functionName arg1 arg2

Tutorial: Write your first program →

Guards - flat, no nesting:

cls sp:n>t;>=sp 1000 "gold";>=sp 500 "silver";"bronze"

Pipes - left-to-right composition:

run x:n>n;x>>dbl>>inc

Data pipeline - fetch, parse, filter, sum:

fetch url:t>R ? t;r=($!url);rdb! r "json"
proc rows:L ?>n;clean=flt pos rows;sum clean
pos x:?>b;>x 0

Auto-unwrap ! - eliminates Result matching:

ilo 'inner x:n>R n t;~x  outer x:n>R n t;~(inner! x)' 42  # → 42

ilo ships as an Agent Skill. Install the plugin and the agent learns ilo automatically.

For manual context loading:

ilo -ai              # compact spec for LLM system prompts
ilo help lang        # full spec

Per-call time (ns) across 8 micro-benchmarks. Lower is better. Full results →

10000 iterations, Darwin arm64, 2026-03-14

• ilo-lang.ai - docs, playground, and examples
• r/ilolang - discussion and updates
• hello@ilo-lang.ai - get in touch

1. Token-conservative - every choice evaluated against total token cost
2. Constrained - small vocabulary, one way to do things, fewer wrong choices
3. Verified - types checked before execution, all errors reported at once
4. Language-agnostic - structural tokens (@, >, ?, ^, ~, !, $) over English words

See the manifesto for full rationale.