Cut LLM API bills by cascade-routing simple prompts to cheap models first
Route private or sensitive prompts to a local model instead of the cloud
Add offline semantic checks and drift detection to a CI pipeline for AI outputs
| srkrz23/frugal | 0xustaz/streamgate | a-bissell/unleash-lite | |
|---|---|---|---|
| Stars | 1 | 1 | 1 |
| Language | Python | Python | Python |
| Setup difficulty | easy | hard | hard |
| Complexity | 3/5 | 4/5 | 4/5 |
| Audience | developer | developer | researcher |
Figures from each repo's GitHub metadata at analysis time.
Runs fully offline with a mock provider out of the box, no API keys needed to try it.
Frugal is a Python toolkit that helps developers control the cost of running AI agents and other apps built on large language models. Rather than being a single tool, it is a set of nine modules that work together around one shared cost meter, so every call your app makes, whether it is routed, cached, checked, or served, gets tracked in the same place. The core idea is cascade routing: send a prompt to a cheap or local model first, check if that answer looks trustworthy using a confidence check, and only send the request on to a more expensive frontier model when needed. If the price difference between the cheap and expensive model is too small to make this worthwhile, Frugal warns the developer instead of quietly wasting money on the check itself. Beyond routing, the toolkit includes a response cache so repeated prompts cost nothing the second time, offline tools for checking whether an AI's answer stayed factually consistent or drifted, checks for retrieval augmented generation setups, and a server using the Model Context Protocol that lets an AI agent read its own spending in real time. There is also a proxy server that mimics the OpenAI API while adding budget limits and routing behind the scenes. A notable design choice is that everything works offline with no API keys required, using a built in mock provider, so anyone can install it and try the demo immediately without signing up for anything. The project includes benchmarks anyone can run themselves to check its cost saving claims, along with a document explaining what those numbers do not prove. This project is aimed at developers building AI agents or LLM powered applications who want to reduce API spending and add reliability checks, and it is released under the Apache 2.0 license, which allows free commercial use as long as the license terms are kept.
A zero-dependency Python toolkit that routes AI requests to cheap or local models first, caching and verifying results to cut LLM API costs.
Mainly Python. The stack also includes Python, Ollama, vLLM.
Use freely for any purpose, including commercial use, as long as you keep the copyright notice and license terms.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
double-check against the repo, no cap.