Strot 开源：用 LLM 编译决策逻辑，实现零成本本地推理

原标题：Show HN: Strot – Compile an LLM's intent once, run decisions locally forever

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核心综述

开源工具 Strot 提出“编译时智能体”范式，利用大模型将自然语言意图转化为冻结的本地决策模型。该方案在首次运行时调用 LLM 生成类型安全的 JSON 决策逻辑，后续所有记录处理均在本地硬件以零边际成本完成，兼顾了语义理解能力与生产环境的确定性、低延迟需求。

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⬢ STROT ### The open decision compiler Compile your intent once with an LLM. Then decide on every record forever — classification, scoring, routing — as type-safe JSON with probabilities and confidence, on your own hardware, at zero marginal cost. Website · Docs · Benchmarks · PyPI `Agents at compile time. Never at runtime.`

BASH

pip install strot-ai

PYTHON

import strot

result = strot.run(
    tickets,
    query="Triage incoming support tickets into the correct workflow.",
    action_registry={
        "billing_dispute":  "Refunds, invoice corrections, double charges",
        "technical_defect": "Crashes, bugs, broken behavior",
    },
    llm="anthropic/claude-opus-4-8",   # used once, on first compile only
)

for o in result.outcomes:
    print(o["action"], round(o["confidence"], 2))
# billing_dispute 0.97
# technical_defect 0.94

The LLM runs once, at compile time. Production is a frozen, self-contained Python artifact: a small local decision model arbitrates every record, nothing is generated, every output is bounded, and low-confidence records abstain instead of guessing.

STROT = Agentic Schema Intent + Compiler & State + Code + Execution + NLU ⟹ JSON Decisions (classification · scoring · routing · typed answers)

How it works

文本

            COMPILE  (once per intent + schema)
  intent + sample ─▶ synthesize ─▶ golden gate ─▶ teacher labels ─▶ calibrate
                     (labels,      (your known      (annotate         (fit the
                      projection,   answers must     a sample,         confidence
                      handlers)     pass)            once)             threshold)
                                        │
                                        ▼
                             frozen artifact  (.py)

RUNTIME  (every record, forever)
  record ─▶ projection ─▶ decision model ─▶ confidence gate ─▶ typed JSON
            (compiled)     (144M, CPU,        (decide or         decision
                            ~60 ms)            abstain)

Two surfaces

run() — route each record to an action and execute the compiled handler.

decide() — answer typed questions (choice / score / boolean) per record:

PYTHON

result = strot.decide(records, questions={
    "is_bug_report":     {"type": "boolean", "instructions": "mentions crashes/errors?"},
    "customer_sentiment":{"type": "score", "min": 1, "max": 5, "instructions": "1 angry, 5 happy"},
    "routing_bucket":    {"type": "choice", "options": ["billing", "engineering", "spam"]},
})
# → {"booleans": {...}, "scores": {...}, "choices": {...}}  with probabilities + confidence

Measured

Real model (SupersonicLabs/Julia-1, 144M, CPU), real datasets, clean Docker containers:

	Result
Grounded routing accuracy (398 records)	92.5% compiled vs 74.8% naive — the compiler is worth +18pp on the same model
Semantic text (AG News, unseen labels)	86.7% compiled vs 72.7% naive (+14pp)
Typed decisions vs the leading hosted decision API's own example	near-identical (sentiment 1.79 vs 1.8)
Latency / marginal cost / LLM calls per record	~60 ms · $0 · 0

Reproduce everything: benchmarks/ — with the losses published too.

What makes it different

Compile once, decide forever — content-addressed artifact cache; schema drift triggers exactly one recompile.
Golden-record promotion — artifacts must pass your known answers before they serve.
Fitted guarantees — temperature scaling + split conformal prediction, baked in: dispatched decisions are wrong ≤ α of all records (marginal); the rest abstain.
Measured, not guessed — the compiler scores labelings, and can train a distilled head, keeping only what beats the baseline on held-out data.
0% structural hallucination — output is selection + computation over your registry; nothing is generated at runtime.
Model-independent — the decision model is a slot (nlu=): local Julia-1 by default, hosted APIs, or any drop-in specialist.

Install

BASH

pip install strot-ai            # core: pure stdlib
pip install strot-ai[local]     # + the local decision model (torch/transformers)
pip install strot-ai[hosted]    # + openai, for a hosted decision runtime
pip install strot-ai[anthropic] # + anthropic, for the synthesizer

Bake the local model into your image for offline production:

DOCKERFILE

RUN pip install strot-ai[local] \
 && python -c "import strot; strot.prefetch_nlu(local_dir='/opt/julia-1', install=True)"
ENV STROT_NLU_CHECKPOINT=/opt/julia-1

(Never pip install julia — unrelated package.)

Documentation

DEVELOPERS.md — architecture, compile lifecycle, calibration semantics and caveats, deployment, model swapping, full API reference.