This project addresses the inefficiency and high cost of running all AI agent tasks on premium language models. It provides a skill for AI agent frameworks like OpenClaw to classify incoming tasks by complexity (Routine, Moderate, Complex) and route them to the most cost-effective model capable of handling them, aiming for significant cost reductions without sacrificing quality. This is beneficial for developers and users seeking to optimize AI operational expenses.

How It Works

The skill implements a classification system that categorizes tasks into three tiers: Routine (80% of tasks, handled by cheap models), Moderate (15%, mid-tier models), and Complex (5%, premium models). By intelligently routing requests, it prevents overspending on simple operations, leveraging cheaper models for the majority of workloads while reserving expensive ones for genuinely challenging problems. This tiered approach is designed to achieve substantial cost savings, estimated at around 10x.

Quick Start & Requirements

Installation for OpenClaw involves copying the SKILL.md file to ~/.openclaw/skills/model-hierarchy/ and restarting the gateway. Integration examples are provided for Claude Code/Codex, suggesting manual addition to project instructions. Testing can be performed using python -m pytest tests/ -v. No specific hardware or advanced software prerequisites beyond the agent framework itself are detailed.

Highlighted Details

• Claims approximately a 10x cost reduction for AI agent operations.
• Provides a cost breakdown example: routing 100K tokens/day via the hierarchy method costs ~$19/month, compared to ~$45/month for pure Sonnet or ~$225/month for pure Opus.
• Offers detailed classification rules within SKILL.md for integration into other agent systems.

Maintenance & Community

The provided README does not contain information regarding specific contributors, community channels (like Discord or Slack), sponsorships, or a public roadmap.

Licensing & Compatibility

The project is released under the MIT license, which is highly permissive and generally suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The skill's primary focus is cost optimization through model routing; it does not inherently improve model capabilities or address task complexity beyond classification. Its direct integration examples are for OpenClaw and Claude Code, implying potential framework-specific nuances for adoption in other systems. The README does not detail performance benchmarks beyond cost savings or mention unsupported platforms.