maslul
Smart LLM router — one call, the right model.
What it does
Smart LLM router — one call, the right model. Async and fully typed, across Anthropic, Gemini, xAI Grok, and OpenAI — routing each request to the right model tier by difficulty. Stop hardcoding model choices and stop re-writing the tool-use / structured-output / web-search / retry plumbing for every provider. Contribute to iliatankelevich/maslul development by creating an account on GitHub.
Does a similar job
all alternatives →- ALAny-LLM – Lightweight router to access any LLM Provider2025 · github.com · ▲125
We built any-llm because we needed a lightweight router for LLM providers with minimal overhead. Switching between models is just a string change : update "openai/gpt-4" to "anthropic/claude-3" and you're done. It uses official provider SDKs when available, which helps since providers handle their own compatibility updates. No proxy or gateway service needed either, so getting started is pretty straightforward - just pip install and import. Currently supports 20+ providers including OpenAI, Anthropic, Google, Mistral, and AWS Bedrock. Would love to hear what you think!
- ARArch-Router – 1.5B model for LLM routing by preferences, not benchmarks2025 · ▲66
Hi HN — we're the team behind Arch (https://github.com/katanemo/archgw), an open-source proxy for LLMs written in Rust. Today we're releasing Arch-Router (https://huggingface.co/katanemo/Arch-Router-1.5B), a 1.5B router model for preference-based routing, now integrated into the proxy. As teams integrate multiple LLMs - each with different strengths, styles, or cost/latency profiles — routing the right prompt to the right model becomes a critical part of the application design. But it's still an open problem. Most routing systems fall into two…
- RYRoute your prompts to the best LLM2024 · unify.ai · ▲298
Hey HN, we've just finished building a dynamic router for LLMs, which takes each prompt and sends it to the most appropriate model and provider. We'd love to know what you think! Here is a quick(ish) screen-recroding explaining how it works: https://youtu.be/ZpY6SIkBosE Best results when training a custom router on your own prompt data: https://youtu.be/9JYqNbIEac0 The router balances user preferences for quality, speed and cost. The end result is higher quality and faster LLM responses at lower cost. The quality for each candidate LLM is predicted ahead of time…
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