ClawRouter

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ClawRouter is an agent-native LLM router that optimizes model selection and cost for AI agents. It routes requests to the right model at the right price, featuring 15-dimension scoring and supporting 41+ models.

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At a glance

Pricing

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Free tier

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API

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Skill level

Technical

About

What is ClawRouter?

ClawRouter is an agent-native LLM router specifically designed to power AI agents like OpenClaw. Its core function is to intelligently route requests to the most appropriate large language model (LLM) based on various criteria, ensuring optimal performance and cost efficiency. The platform boasts a sophisticated 15-dimension scoring system to evaluate and select models. It supports a wide array of LLMs, with compatibility for over 41 different models, and uniquely allows this without requiring individual API keys for each model. This makes it a powerful tool for developers and organizations looking to streamline their LLM operations and reduce expenses.

Best used for

Optimizing large language model selection and cost for AI agents by intelligently routing requests to the most appropriate model.

Common actions

Optimize LLM usage

Reduce LLM costs

Route AI requests

Manage multiple LLMs

Enhance AI agent performance

deepfakeautomated workflowlow-code/no-codecollaborationopen-sourceworkflowsgithub copilot"AI Agents"face swapping

Capabilities

Key features

Agent-native LLM router
15-dimension scoring
Supports 41+ models
Zero API keys
Optimizes model selection

Target Audience

AI developersMachine learning engineersAI product managersOrganizations using AI agents

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

How does ClawRouter manage to support 41+ models without requiring individual API keys for each?

ClawRouter acts as a centralized routing layer. While the exact mechanism isn't detailed, it likely uses its own aggregated access or a proxy system to manage connections to various LLM providers, abstracting the need for users to handle multiple API keys directly.

What are the key criteria within the '15-dimension scoring system' that ClawRouter uses to select the best LLM?

The specific 15 dimensions are not publicly detailed, but they typically include factors like cost per token, latency, model accuracy for specific task types, context window size, and specialized capabilities relevant to the agent's current request.

Can ClawRouter be integrated with custom or fine-tuned LLMs, or is it limited to the 41+ pre-supported models?

The description emphasizes its support for 41+ models. While it's designed for broad compatibility, integrating custom or fine-tuned models would likely depend on whether they can be exposed through a compatible API that ClawRouter can access and evaluate.

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