peva3

peva3 / SmarterRouter

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SmarterRouter: An intelligent LLM gateway and VRAM-aware router for Ollama, llama.cpp, and OpenAI. Features semantic caching, model profiling, and automatic failover for local AI labs.

ai-cache ai-gateway docker fastapi gpu-monitoring
58
2
100% credibility
Found Feb 21, 2026 at 28 stars 2x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

SmarterRouter is an intelligent local proxy that automatically selects the optimal AI model for each user query from available local models, handling profiling, routing, caching, and memory management.

How It Works

1
🔍 Discover SmarterRouter

You hear about a helpful tool that automatically picks the best AI helper for whatever question you ask, saving you time and hassle.

2
📥 Get it set up

Download the ready-to-go package and start it with a few simple clicks on your computer.

3
🧠 It finds your AI helpers

The tool scans your computer and discovers all the AI models you already have, no manual hunting needed.

4
⏱️ Tests each one

It runs quick tests on your hardware to learn which AI is fastest and smartest for different jobs, like math or stories.

5
🔗 Connect to your chat app

Link it to your favorite chat interface, like OpenWebUI, so it works just like before but smarter.

6
💬 Start chatting smarter

Ask any question and watch it instantly choose the perfect AI helper, giving quicker and better answers every time.

🎉 Enjoy automatic magic

Your AI chats now feel supercharged, always using the right helper without you lifting a finger, all free and local.

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AI-Generated Review

What is SmarterRouter?

SmarterRouter is a Python-based intelligent LLM gateway that automatically routes prompts to the best local model from Ollama, llama.cpp, or OpenAI-compatible backends. It profiles models on your hardware for capabilities like reasoning and coding, adds semantic caching for repeat queries, and handles automatic failover if a model fails. VRAM-aware management unloads models dynamically across NVIDIA, AMD, Intel, or Apple GPUs, making it a drop-in OpenAI API proxy for local setups.

Why is it gaining traction?

Unlike manual-config proxies, it delivers zero-touch model selection based on prompt analysis, benchmarks, and real-time profiling, with continuous learning from feedback. Key features like semantic caching cut latency on repeats, while VRAM monitoring prevents crashes in constrained labs—standing out for local-first users over cloud-heavy alternatives. Docker Compose setup gets you routing in minutes to OpenWebUI or any OpenAI client.

Who should use this?

Local AI hobbyists or small teams running Ollama model zoos who hate picking the right one per prompt. Multi-GPU workstation users needing automatic failover and VRAM juggling for stable inference. Python devs building local LLM apps wanting production metrics, caching, and OpenAI compatibility without backend tweaks.

Verdict

Try it if you're deep in local Ollama/llama.cpp workflows—features like profiling and intelligent routing punch above its 12 stars and 1.0% credibility score. Early-stage with solid docs and Docker, but watch for stability as it matures.

(198 words)

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