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title: README
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NeoSyntropy
Deterministic control for AI workflows
Models interpret. Your application decides and executes. NeoSyntropy turns AI workflows into controlled graphs with application-owned schemas, tools, validation, routing, state transitions, and business policy.
Website · Framework · Documentation
Focused role models
| Model | Responsibility |
|---|---|
| Runtime Structure | Structured extraction into application-owned schemas |
| Runtime Route | Valid workflow topology and candidate selection |
| Runtime Deterministic Reasoning | Schema-constrained reasoning and declared tool calls |
| Runtime Stochastic Reasoning | Open reasoning with controlled tool requests |
| Runtime Guard | Evidence-based validation against supplied rules |
| Runtime Score | Rubric-grounded scoring and measurement |
One contract from data to inference
Workflow contract → diverse scenarios → teacher gold → schema and semantic validation → critic gate → frozen evaluation → role adapter
Every accepted sample preserves prompt version, source lineage, teacher provenance, validation checks, and a canonical content hash. Training artifacts and metrics are published only after evaluation against frozen test sets. Each model card links to its matching private, versioned pilot dataset for inspection.