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ypollak2/llm-router

by Various

Universal LLM router for AI coding tools. Works with Claude Code, Cursor, Codex, Gemini CLI, Copilot and more. Free-first fallback chain keeps costs 70–85% lower.

Y

MCP

ypollak2/llm-router

Added 1 June 2026

#ai-routing #anthropic #claude #claude-code #cost-optimization #gemini #litellm #llm

Overview

LLM Router is a Python-based tool that routes requests from AI coding assistants such as Claude Code, Cursor, and Copilot to different language models. It uses a free-first fallback chain that prioritizes cost-free options, reducing overall API costs by 70–85%.

Best for

Best for
Developers using multiple AI coding assistants who want to minimize API costs.

Use cases

  • Directing coding queries to the cheapest available LLM
  • Fallback routing when the primary model is unavailable or rate-limited
  • Unifying multiple AI coding tools under a single routing layer

Notes

LLM Router is a Python-based tool that routes requests from AI coding assistants such as Claude Code, Cursor, and Copilot to different language models. It uses a free-first fallback chain that prioritizes cost-free options, reducing overall API costs by 70–85%.

27 stars on GitHub. Last updated 2026-06-01. Licensed MIT.

Use cases

  • Directing coding queries to the cheapest available LLM
  • Fallback routing when the primary model is unavailable or rate-limited
  • Unifying multiple AI coding tools under a single routing layer

Pros

  • Significant cost reduction with free-first fallback chain
  • Compatible with a wide range of popular AI coding tools
  • Open source and easy to integrate into existing workflows

Cons

  • Low GitHub star count indicates limited community adoption and testing
  • Requires manual configuration of fallback chains and API keys
  • Performance depends on the availability and speed of free LLM endpoints

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Significant cost reduction with free-first fallback chain
  • Compatible with a wide range of popular AI coding tools
  • Open source and easy to integrate into existing workflows

Cons

  • Low GitHub star count indicates limited community adoption and testing
  • Requires manual configuration of fallback chains and API keys
  • Performance depends on the availability and speed of free LLM endpoints