Multi Model Routing

# Multi-Model Routing

Part of [Agent Skills™](https://github.com/itallstartedwithaidea/agent-skills) by [googleadsagent.ai™](https://googleadsagent.ai)

## Description

Multi-Model Routing is the intelligent dispatch of agent tasks to the optimal model provider based on task characteristics, cost constraints, latency requirements, and availability. Production AI systems that rely on a single model provider are fragile and expensive. Multi-Model Routing creates a resilient, cost-efficient agent architecture that leverages the strengths of Claude, GPT, Gemini, and open-source models, automatically selecting the best model for each task and failing over gracefully when a provider is unavailable.

This skill documents the multi-model routing architecture powering the Buddy™ agent at [googleadsagent.ai™](https://googleadsagent.ai), which routes between Claude (primary — strongest reasoning), GPT-4o (secondary — strong function calling), and Gemini (tertiary — large context, low cost) based on task classification. The routing layer reduced costs by 45% compared to using Claude for all tasks while maintaining equivalent quality scores, because many subtasks (formatting, summarization, data extraction) perform identically on cheaper models.

The routing decision incorporates four factors: model strengths (code reasoning, long context, structured output, creative writing), cost per token (varies 100x between model tiers), latency targets (real-time vs. batch), and availability (rate limits, outages, degraded performance). A circuit breaker pattern ensures that temporary provider issues don't cascade into user-facing failures.

## Use When

- Monthly AI costs need reduction without sacrificing quality
- You need resilience against single-provider outages or rate limits
- Different subtasks have fundamentally different model requirements
- Latency-sensitive and latency-tolerant tasks coexist in the same system
- You want to evaluate new models without fully committing to them
- Compliance requires not being locked into a single AI vendor

## How It Works

```mermaid
graph TD
    A[Incoming Task] --> B[Task Classifier]
    B --> C{Task Type}
    C -->|Code Reasoning| D[Claude Sonnet/Opus]
    C -->|Structured Extraction| E[GPT-4o / Claude Haiku]
    C -->|Long Context| F[Gemini Pro]
    C -->|Simple Format| G[Claude Haiku / GPT-4o-mini]
    
    D --> H{Provider Available?}
    E --> H
    F --> H
    G --> H
    
    H -->|Yes| I[Execute]
    H -->|No| J[Fallback Chain]
    J --> K[Next Provider]
    K --> H
    
    I --> L[Result + Metrics]
    L --> M[Router Learning]
    M --> B
```

The routing pipeline classifies each incoming task by type (code reasoning, structured extraction, long context processing, simple formatting), then maps to the optimal model provider. Before dispatch, a circuit breaker checks provider availability — if a provider has failed recently, the task is immediately routed to the fallback chain. After execution, the result quality and performance metrics feed back into the router, allowing it to refine its model-task mapping over time.

## Implementation

**Model Provider Registry:**

```typescript
interface ModelProvider {
  id: string;
  name: string;
  models: ModelConfig[];
  circuitBreaker: CircuitBreakerState;
}

interface ModelConfig {
  id: string;
  strengths: string[];
  costPer1kInput: number;
  costPer1kOutput: number;
  maxContextTokens: number;
  avgLatencyMs: number;
}

const PROVIDERS: ModelProvider[] = [
  {
    id: "anthropic",
    name: "Anthropic",
    models: [
      { id: "claude-opus", strengths: ["reasoning", "code", "analysis"], costPer1kInput: 0.015, costPer1kOutput: 0.075, maxContextTokens: 200000, avgLatencyMs: 3000 },
      { id: "claude-sonnet", stren