aill

中文

AIL — AI Workflow Language

One rule: @ = AI executes. No @ = your code runs.

result = @ask("summarize: {text}")   # AI executes
words  = len(text.split())           # deterministic Python
flag   = @judge("is this complete: {result}")  # AI returns bool

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The problem

When you want an AI to execute a product's logic, you have two bad options:

• Natural language — flexible, but takes paragraphs to describe, and AI fills gaps however it wants
• Pseudocode — tighter, but can't express the full execution logic: no retries, no branching on AI judgment, no parallel calls, no fallbacks

The result: you spend more time writing prompts than building, and the AI still misses the intent.

The solution

AIL is a new language designed for this exact problem. One rule, one symbol:

@  →  AI executes   (semantic, non-deterministic)
   →  code runs     (deterministic, Python-compatible)

Write your logic the way you think it. @ask, @judge, @validate — the full execution intent in a fraction of the words. AIL comes with a guide written for AI, so you can paste it into any model and it immediately understands how to read and generate AIL.

aif is the Python framework that runs AIL. Plug in your own agent, decorate your tools, and your agent executes the workflow. Pure Python, no new concepts — if you know Python, you're ready in minutes.

Everything else is standard Python syntax — loops, functions, types, error handling. No new paradigm to learn.

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Quick look

Retry until quality passes:

retry max=3:
    report = @ask(generate_report)
    @validate(report, "must include conclusion, data, and sources")

Loop until AI says done:

loop max=10 until @judge("no unresolved issues in: {output}"):
    output = @ask(solve_issues)

Parallel AI calls:

tech, biz, ux = parallel:
    tech = @ask("analyze technically: {content}")
    biz  = @ask("analyze commercially: {content}")
    ux   = @ask("analyze from user perspective: {content}")

summary = @ask("synthesize three perspectives: {tech} {biz} {ux}")

Structured extraction:

type QueryInfo:
    intent:     str
    keywords:   list[str]
    multi_step: bool

info = @extract(analysis, type=QueryInfo)

Multi-turn conversation:

with context(system="you are a planning expert") as ctx:
    issues = @ask(analyze)      # AI sees prior turns
    output = @ask(solve)        # AI sees issues
    ctx.reset()                 # clear history

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@ operations at a glance

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Reliability built in

try:
    retry max=3:
        timeout 20s:
            result = @ask(generate)
        @validate(result, "must be complete")
fallback:
    result = @ask(basic_fallback)

retry, timeout, try/fallback compose freely.

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Tools, skills, plugins

use tool  vector_search(query: str, top_k: int) -> list[Document]   # deterministic function
use skill rag_agent(query: str) -> (str, list[str])                 # sub-agent with AI ops
use plugin database as db                                            # stateful external service

docs   = vector_search("deep learning", top_k=10)   # called like a function
answer, citations = rag_agent(query=user_input)
users  = db.query("SELECT * FROM users")

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Memory

memory.save(result, key="last_answer", tags=["history"])
pref    = memory.get("user_preference")
related = memory.search("RAG discussion", top_k=3)

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Full example

A complete RAG agent is in examples/ail/rag_agent.ail. More examples in examples/.

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Docs

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License

MIT