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.
MCP
ypollak2/llm-router
Added 1 June 2026
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
How to use
Install
pip install llm-routing # installs the Tools exposed
llm-routingllm-routerclaude-code-llm-router
Tested with
Claude Code, Cursor, VS Code
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
Open-source & AI alternatives
Swap-in tools that solve the same job. Weigh the trade-offs before you commit.
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