sc-router 0.1.0

AI routing based on Selector Complexity theory

SC-Router

AI routing based on Selector Complexity theory.

"What is the minimum cost of choosing the right strategy?"

SC-Router classifies queries by the difficulty of the routing decision itself — not just the query content. Based on the mathematical framework of Selector Complexity (IPS proof complexity), it determines whether a query needs direct dispatch, pipeline decomposition, combinatorial search, or full agent delegation.

Install

pip install sc-router

Quick Start

from sc_router import ToolCatalog, Tool, route

catalog = ToolCatalog()
catalog.register(Tool(
    name="weather",
    description="Get weather forecast",
    input_types={"location"},
    output_types={"weather_data"},
    capability_tags={"weather", "forecast", "temperature"}
))
catalog.register(Tool(
    name="calculator",
    description="Perform arithmetic calculations",
    input_types={"expression"},
    output_types={"number"},
    capability_tags={"math", "calculate", "arithmetic"}
))

result = route("What's the weather in Madrid?", catalog)
print(result.sc_level)           # 0
print(result.strategy)           # 'direct'
print(result.tool_assignments)   # [ToolAssignment(tool='weather', ...)]

SC Levels

SC	Query Type	Routing Action	Example
SC(0)	1 tool, obvious	Direct dispatch	"What's the weather in Madrid?"
SC(1)	Decomposable	Pipeline/parallel	"Search flights to Paris, book the cheapest"
SC(2)	Ambiguous/complex	Search combinations	"Plan trip: flights+hotel+restaurants, budget $2000"
SC(3)	Globally entangled	Full agent	"Analyze market trends, cross with social sentiment, build predictive model"

How It Works

SC-Router extracts 17 structural features from each query (analogous to the 17 features in IPS proof complexity), then classifies the routing difficulty using a threshold-based decision tree — no ML required.

The classification runs in <50ms and adds minimal overhead to any routing pipeline.

License

MIT