ClawRoute Technical Architecture: How Smart Model Routing Works

Overview

ClawRoute is a distributed AI routing system that intelligently routes requests across multiple LLM providers using a unified 0-100 scoring system, Thompson Sampling for exploration/exploitation balance, circuit breakers for fault tolerance, predictive rate limiting, and multi-provider support. The system optimizes for cost, speed, and reliability while providing zero-configuration developer APIs.

Core Architecture

1. Request Router (router.py)

The main entry point that receives requests and routes them based on:

• Unified 0-100 quality score (task-specific weights)


• Cost optimization


• Latency requirements


• Availability and health status

Key Features:

• 
  Unified Scoring System: All models rated 0-100 with weights adjusted per task type


• 
  Thompson Sampling: Balances exploration and exploitation for model selection


• 
  Smart Fallback: Automatic switching when primary model underperforms


• 
  Global Distribution: Routes to geographically closest healthy endpoints

2. Provider Adapters

Modular adapters for each LLM provider:

OpenAI Adapter

• GPT-3.5, GPT-4, GPT-4 Turbo support


• API key rotation and rate limit handling

Anthropic Adapter

• Claude 3 family support


• API key management

Google Adapter

• Gemini Pro/Ultra support

Custom Endpoints

• Self-hosted OpenAI-compatible models


• Local LLM deployments

3. Unified 0-100 Scoring System

Every model response receives a score from 0-100 based on five dimensions, with weights that adjust based on task type:

final_score = (0.25 * relevance) + (0.20 * coherence) + (0.20 * completeness) + 

              (0.15 * latency_score) + (0.10 * cost_efficiency) + (0.10 * task_specific)

Scoring Dimensions (0-100 each):

• 
  Relevance: Does response address the prompt? (semantic similarity)


• 
  Coherence: Is response logically structured and consistent?


• 
  Completeness: Does it fully answer the question?


• 
  Latency Score: Normalized response time (faster = higher score)


• 
  Cost Efficiency: Quality per dollar spent


• 
  Task Specific: Custom dimension based on use case

Task-Specific Weight Examples:

• 
  Coding Tasks: Quality weight increased to 0.35, latency reduced to 0.10


• 
  Creative Writing: Relevance weight 0.30, coherence 0.25


• 
  Data Analysis: Completeness weight 0.30, cost efficiency 0.15


• 
  Real-time Chat: Latency weight 0.25, relevance 0.20

4. Thompson Sampling for Model Selection

Instead of static routing, ClawRoute treats each model as a "bandit arm" and uses Thompson Sampling to balance exploration and exploitation:

For each request:

  1. Sample from each model's Beta(α, β) distribution

     where α = successes + 1, β = failures + 1

  2. Select model with highest sampled value

  3. Execute request

  4. Observe outcome (score 0-100)

  5. Update distribution:

        if score >= threshold: α += 1

        else: β += 1

This dynamically shifts traffic toward better-performing models while still testing alternatives.

5. Circuit Breaker Pattern

Prevents cascading failures with three states:

CLOSED → [failures ≥ threshold] → OPEN

  ▲                                 |

  |                                 |

  |                    [timeout]    |

  |                                 ▶

HALF-OPEN ← [probe success] —— CLOSED

Configuration:

• Failure threshold: 5 consecutive low scores (< 60)


• Timeout: 30 seconds before half-open


• Half-open: Allow one test request

6. Predictive Rate Limiting

Learns provider limits from 429 responses:

class AdaptiveRateLimiter:

    def __init__(self, provider):

        self.provider = provider

        self.window = 60  # seconds

        self.requests = deque()

        self.limit = None  # Learned from 429s

        self.safety_margin = 0.8  # Stay under 80% of limit



    def allow_request(self):

        now = time.time()

        # Remove old requests

        while self.requests and self.requests[0] < now - self.window:

            self.requests.popleft()



        # Predictive check

        if self.limit and len(self.requests) >= self.limit * self.safety_margin:

            return False



        return len(self.requests) < (self.limit or 1000)

7. Multi-Provider Abstraction

Unified interface hides provider differences:

response = clawroute.generate(

    prompt="Explain RSA encryption",

    task_type="coding",  # Adjusts scoring weights

    max_tokens=500

)

Provider Capabilities Matrix:

Technical Implementation

Request Flow

def route_request(request):

    # 1. Apply task-specific weights

    weights = get_task_weights(request.task_type)



    # 2. Thompson Sampling selects candidate models

    candidates = thompson_sample(request.context)



    # 3. Filter by circuit breaker state

    healthy = [m for m in candidates if circuit_breaker[m].state == "CLOSED"]



    # 4. Check predictive rate limits

    available = [m for m in healthy if rate_limiter[m].can_send()]



    # 5. Select highest expected score

    selected = max(available, key=lambda m: m.beta_distribution.mean())



    # 6. Execute and score

    response = providers[selected].call(request)

    score = score_response(response, weights)



    # 7. Update learning systems

    update_thompson(selected, score)

    update_rate_limiter(selected, response.headers)

    return response

Scoring Algorithm

def score_response(response, weights):

    scores = {

        'relevance': semantic_similarity(response, request.prompt) * 100,

        'coherence': coherence_model.score(response) * 100,

        'completeness': completeness_check(response, request) * 100,

        'latency': normalize_latency(response.latency) * 100,

        'cost_efficiency': (base_quality / response.cost) * 100,

        'task_specific': task_specific_scorer[request.task_type](response)

    }



    return sum(scores[k] * weights[k] for k in weights)

Deployment & Scaling

Horizontal Scaling

• Stateless router instances behind load balancer


• Shared Redis for scoring history and rate limit tracking


• Consistent hashing for provider affinity

Database Schema

model_performance (

    model_id, 

    timestamp, 

    task_type, 

    score_0_100,

    latency_ms,

    cost_usd,

    success_bool

)



rate_limit_state (

    provider, 

    window_start, 

    request_count, 

    learned_limit

)

Monitoring

• Real-time score distributions per model


• Alert on scoring distribution shifts (model drift)


• Track cost savings vs baseline routing


• Latency and success rate dashboards

Performance Impact

A/B Test Results (vs Round Robin)

Task-Specific Gains

• 
  Code Generation: 22% higher quality scores


• 
  Customer Support: 18% faster responses


• 
  Content Creation: 15% better coherence

Getting Started

Install via npm:

npm install @clawroute/sdk

Initialize with providers:

import { ClawRoute } from '@clawroute/sdk';



const ai = new ClawRoute({

  providers: {

    openai: { apiKey: process.env.OPENAI_API_KEY },

    anthropic: { apiKey: process.env.ANTHROPIC_API_KEY },

    google: { apiKey: process.env.GOOGLE_API_KEY }

  },

  scoring: {

    // Optional: customize task weights

    taskWeights: {

      coding: { relevance: 0.30, coherence: 0.15, completeness: 0.25, 

               latency: 0.10, cost: 0.10, taskSpecific: 0.10 }

    }

  }

});



// Route automatically based on task type

const result = await ai.generate({

  prompt: "Create a Python function to calculate fibonacci",

  taskType: "coding",

  maxTokens: 200

});

Future Enhancements

• 
  Online Learning: Real-time weight adjustment based on user feedback


• 
  Multi-Objective Optimization: Pareto frontier for cost vs quality


• 
  Prompt Caching: Semantic caching for repeated queries


• 
  Edge Deployment: Regional model providers for lower latency

ClawRoute is open source under MIT License. Visit github.com/clawhub/clawroute for documentation and examples.

ClawRoute: Intelligent AI routing that learns and adapts to deliver the best model for every request.