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NeoSyntropy documentation
Build controlled AI workflows as explicit graphs. Models propose the next step; the control layer decides what is allowed to happen.A control layer for model-driven systems
NeoSyntropy separates probabilistic decisions from deterministic control. Models can propose what should happen next; a finite-state graph defines what is allowed to happen.
You author nodes and edges in the Python SDK. When backend credentials are set, the NeoSyntropy backend owns selection, routing, plan validation, and state commits. The client only executes local handlers and submits results.
Explicit graph
Nodes describe capabilities. Edges describe permitted movement.
Fail-closed gates
Transition legality and guards reject illegal steps before commit.
Typed tools
Nodes declare allow-listed tools with pydantic schemas; undeclared calls never run.
Two execution modes
With backend credentials, ControlManager runs an opaque execute/commit loop against POST /api/v1/control/runs. Without credentials, the SDK falls back to a local deterministic router for offline development.
Grounded in neosyntropy-framework README and docs/concepts.md.