LLM Routing And Fallback

LLM Routing and Fallback

When building Claude Code skills or automation that calls LLM APIs, routing through a proxy layer reduces cost, adds reliability, and enables model switching without code changes.

Core patterns

Pattern 1: litellm unified interface

litellm provides an OpenAI-compatible interface for 100+ LLMs. Claude calls look identical to GPT calls at the code level.

import litellm

# Route to Claude
response = litellm.completion(
    model="anthropic/claude-sonnet-4-6",
    messages=[{"role": "user", "content": "Hello"}]
)

# Fallback: if claude fails, try gpt-4o
response = litellm.completion(
    model="anthropic/claude-sonnet-4-6",
    messages=[{"role": "user", "content": "Hello"}],
    fallbacks=["openai/gpt-4o"]
)

Pattern 2: Budget caps

import litellm
litellm.max_budget = 0.50  # $0.50 cap per run

response = litellm.completion(
    model="anthropic/claude-haiku-4-5-20251001",  # cheapest first
    messages=[{"role": "user", "content": prompt}]
)

Pattern 3: Model tiering

Route by task complexity:

• Simple tasks → claude-haiku (cheapest)
• Standard tasks → claude-sonnet
• Complex/creative → claude-opus (only when justified)

def route_by_complexity(prompt: str, complexity: str) -> str:
    models = {
        "simple": "anthropic/claude-haiku-4-5-20251001",
        "standard": "anthropic/claude-sonnet-4-6",
        "intensive": "anthropic/claude-opus-4-6"
    }
    return litellm.completion(
        model=models[complexity],
        messages=[{"role": "user", "content": prompt}]
    ).choices[0].message.content

Pattern 4: Vercel AI SDK (TypeScript)

import { anthropic } from "@ai-sdk/anthropic";
import { generateText } from "ai";

const { text } = await generateText({
    model: anthropic("claude-sonnet-4-6"),
    prompt: "Hello",
    maxRetries: 3,
});

When to use each pattern

• Building a tool that should work across multiple LLM providers → Pattern 1
• Enforcing API spend limits → Pattern 2
• Optimizing cost by routing based on task complexity → Pattern 3 + claude-usage-orchestrator
• Building TypeScript/Next.js skills → Pattern 4

Related skills

• claude-usage-orchestrator (routing decisions)
• sentry-and-otel-setup (observability for LLM calls)

Related Skills

• ai-cost-optimizer — cost-based routing
• llm-caching-strategies — cache before routing
• claude-api-skill — Claude API integration

GitNexus Index

This skill is indexed by GitNexus for knowledge graph traversal. Index path: /Users/localuser/.claude/skills/llm-routing-and-fallback/.gitnexus Last indexed: 2026-05-23