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.

Agents at compile time. Never at runtime.

pip install strot-ai

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

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:

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:

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

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

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:

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.)

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

MIT. Default decision model: Apache-2.0 (SupersonicLabs/Julia-1). Implements ideas from STROT (arXiv:2505.01636).