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clawbackai-mcp

Clawback.ai intelligently routes your AI agent's LLM calls to the most cost-effective model while maintaining quality. It audits real-time traffic to optimize model selection, significantly reducing operational costs for LLM applications.

intermediate1 hour5 steps

The play

Understand LLM Cost Optimization
Recognize the challenge of balancing performance and cost across various LLM providers in your AI agent applications. Clawback.ai aims to solve this by automating model selection.
Initiate a Free Audit
Visit Clawback.ai's website to sign up for their free initial audit. This will assess your current LLM usage and project potential cost savings tailored to your specific traffic.
Install the Clawback.ai MCP Package
Integrate the Clawback.ai Multi-Cloud Proxy (MCP) package into your AI agent's project. This package acts as your intelligent routing layer for all LLM calls.
Configure LLM Call Routing
Replace direct LLM API calls in your application with calls routed through the `clawbackai-mcp` service. Clawback.ai will then automatically select the Pareto-optimal LLM based on real-time performance and cost metrics.
Monitor and Realize Savings
Observe your LLM operational costs decrease as Clawback.ai continuously optimizes your model routing. Use their dashboard to review performance metrics, model usage, and detailed savings reports.

Starter code

import { ClawbackRouter } from 'clawbackai-mcp';

async function routeOptimizedLLMCall() {
  const router = new ClawbackRouter({
    apiKey: 'YOUR_CLAWBACK_API_KEY' // Replace with your actual API key
  });

  const prompt = "Generate a 5-word summary of quantum computing.";
  const llmParameters = {
    temperature: 0.7,
    max_tokens: 20
  };

  try {
    // Route the call through Clawback.ai for cost-effective, quality-assured LLM selection
    const response = await router.routeLLMCall({
      prompt: prompt,
      ...llmParameters
    });

    console.log(`Optimal LLM response (from ${response.model}): ${response.text}`);
  } catch (error) {
    console.error("Error routing LLM call:", error);
  }
}

routeOptimizedLLMCall();

Source

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