About This Site
Enhances AI coding workflow with flexible model routing and cost optimization. Claude Code Router is an open-source Node.js command-line interface (CLI) tool that extends Anthropic's Claude Code CLI. It serves as a foundation for coding infrastructure, enabling users to route AI coding requests to various large language models (LLMs) from different providers such as OpenRouter, DeepSeek, Ollama, Google Gemini, and Volcengine. This tool offers enhanced flexibility and customization for AI coding workflows, allowing users to tailor coding assistance to specific needs, optimize for different tasks (e.g., background processing, high-reasoning, long contexts), and manage costs by selecting appropriate models. It builds upon Anthropic's official tools, offering additional customization options and dynamic model switching.
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Key Features AI
Core Features
Multi-model Routing: Route queries to any configured provider or model, with dynamic switching using commands like `/model provider,model`.
Specialized Model Roles: Designate specific models for background tasks, high-reasoning tasks (think mode), and handling long contexts.
Extensibility: Supports integration with image and web-search tools, detailed logging, and GitHub Actions.
Flexible Configuration: Fully customizable via a JSON config file (`~/.claude-code-router/config.json`) for providers and routing rules.
Cost Optimization: Route requests to more cost-effective models for specific tasks to reduce overall AI coding assistance costs.
Advantages
Provides greater flexibility and customization for AI coding workflows.
Enables routing coding requests to various AI models from different providers.
Supports dynamic model switching during runtime.
Allows for specialized model roles to optimize performance, cost, and capability for different tasks.
Integrates with GitHub Actions for streamlined development workflows and automation.
Can significantly reduce AI coding costs by utilizing cost-effective models for specific tasks.
Open-source and free to use and modify under the MIT License.
Supports local models via Ollama.
缺点：Requires initial setup and configuration via a JSON file.
缺点：Relies on external AI model providers, requiring API keys and managing their respective costs.
缺点：Requires Node.js and npm as prerequisites.
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