sc-router 0.3.0

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. It determines whether a query needs direct dispatch, pipeline decomposition, combinatorial search, or full agent delegation.

v0.3.0 adds distributed execution: register remote agents, execute across microservices, health checks with circuit breaker, and an optional HTTP gateway — all without touching the core classifier (<0.5ms p99).

Part of kore-stack — the complete cognitive middleware for LLMs. pip install kore-stack for the full stack, or install individually:

Install

pip install sc-router              # core router (zero dependencies)
pip install sc-router[gateway]     # + HTTP gateway (starlette, uvicorn, httpx, pyyaml)
pip install kore-bridge[sc]        # integrated with kore-bridge
pip install kore-stack             # full stack: mind + bridge + SC routing

Quick Start

Local routing (as before)

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', ...)]

Distributed routing (new in v0.3.0)

import asyncio
from sc_router import RemoteAgent, AgentRegistry, AgentStatus, route
from sc_router.executor import execute
from sc_router.catalog import Tool

# 1. Register remote agents
registry = AgentRegistry()
registry.register(RemoteAgent(
    id="search-agent",
    url="http://search-service:8081",
    tool=Tool(
        name="search",
        description="Search the web",
        input_types={"query"},
        output_types={"search_results"},
        capability_tags={"search", "web", "find"},
    ),
    status=AgentStatus.HEALTHY,
))

# 2. Classify (still <50ms, zero overhead)
result = route("Search for Python tutorials", registry.catalog)

# 3. Execute against remote agents
exec_result = asyncio.run(execute(result, registry))
print(exec_result.outputs)

YAML config + Gateway

# config.yaml
agents:
  - id: search-agent
    url: http://search:8081
    tool:
      name: search
      description: "Search the web"
      capability_tags: [search, web]
      input_types: [query]
      output_types: [search_results]

  - id: weather-agent
    url: http://weather:8082
    tool:
      name: weather
      description: "Get weather forecast"
      capability_tags: [weather, forecast]
      input_types: [location]
      output_types: [weather_data]

health:
  failure_threshold: 3
  recovery_timeout_s: 30

# Start the gateway
python -m uvicorn sc_router.gateway:create_app --factory --host 0.0.0.0 --port 8080

# Classify + execute
curl -X POST http://localhost:8080/route \
  -H "Content-Type: application/json" \
  -d '{"query": "What is the weather in Madrid?"}'

# Classify only (no remote calls)
curl -X POST http://localhost:8080/route \
  -H "Content-Type: application/json" \
  -d '{"query": "What is the weather in Madrid?", "execute": false}'

# Health check
curl http://localhost:8080/health

# List agents
curl http://localhost:8080/agents

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, then classifies the routing difficulty using a threshold-based decision tree — no ML required.

The classification runs in <0.5ms p99 and adds zero overhead to any routing pipeline.

Architecture (v0.3.0)

                          ┌─────────────────┐
       POST /route ──────►│    Gateway       │
                          │  (Starlette)     │
                          └────────┬─────────┘
                                   │
                     ┌─────────────▼──────────────┐
                     │     SC Classification       │
                     │   17 features, <0.5ms p99   │
                     └─────────────┬──────────────┘
                                   │
                     ┌─────────────▼──────────────┐
                     │    AgentRegistry            │
                     │  RemoteAgent + ToolCatalog   │
                     │  Health checks + Circuit     │
                     │  breaker                     │
                     └─────────────┬──────────────┘
                                   │
                ┌──────────────────┼──────────────────┐
                │                  │                   │
         ┌──────▼──────┐  ┌───────▼──────┐  ┌────────▼──────┐
         │ Agent A      │  │ Agent B      │  │ Agent C       │
         │ (search)     │  │ (weather)    │  │ (summarizer)  │
         └─────────────┘  └──────────────┘  └───────────────┘

Core (pip install sc-router): zero dependencies, classification + local routing.

Gateway (pip install sc-router[gateway]): Starlette HTTP gateway, distributed execution, health checks, YAML config.

Integration with kore-bridge

SC-Router plugs directly into kore-bridge as SCRouterProvider:

from kore_bridge import SCRouterProvider, Bridge, OllamaProvider
from kore_bridge.providers import OpenAIProvider
from kore_mind import Mind
from sc_router import ToolCatalog, Tool

catalog = ToolCatalog()
catalog.register(Tool(
    name="calculator",
    description="Arithmetic calculations",
    input_types={"expression"},
    output_types={"number"},
    capability_tags={"math", "calculate"},
))

router = SCRouterProvider(
    providers={
        "fast": OllamaProvider(model="llama3.2"),
        "quality": OpenAIProvider(model="gpt-4o"),
    },
    catalog=catalog,
)

bridge = Bridge(mind=Mind("agent.db"), llm=router)
bridge.think("What is 2+2?")          # SC(0) → Ollama
print(router.last_sc_level)           # 0

Performance

Benchmarked on 10-tool catalog, 100 iterations per query (v0.3.0):

Query	avg	p50	p95	p99
SC(0) direct	0.08ms	0.07ms	0.13ms	0.18ms
SC(1) pipeline	0.27ms	0.26ms	0.41ms	0.44ms
SC(2) constrained	0.47ms	0.46ms	0.56ms	0.59ms
SC(3) entangled	0.22ms	0.23ms	0.32ms	0.44ms

Distributed layer adds zero overhead to classification. Scales to 50+ tools under 50ms.

Part of kore-stack

Package	What it does
kore-mind	Memory, identity, traces, cache storage
kore-bridge	LLM integration, cache logic, rate limiting, A/B testing, SC routing
sc-router (this)	Query routing by Selector Complexity theory
kore-stack	All of the above, one install: pip install kore-stack

License

MIT