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 0.94The 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)
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)
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 + confidenceReal 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.
- 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.
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 synthesizerBake 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).