Analysis updated 2026-05-18
Run AI chat completions on your own desktops or servers instead of paying a cloud API for every request.
Fall back automatically to a cloud provider like OpenAI or Anthropic when your local machines can't handle a request.
Point any existing OpenAI-compatible app or SDK at your own hardware pool without changing its code.
| gysho/llmrpro | anil-matcha/open-poe-ai | arata-ae/purupurupngtuber | |
|---|---|---|---|
| Stars | 4 | 4 | 4 |
| Language | JavaScript | JavaScript | JavaScript |
| Last pushed | — | 2026-06-25 | — |
| Maintenance | — | Active | — |
| Setup difficulty | hard | moderate | moderate |
| Complexity | 4/5 | 3/5 | 3/5 |
| Audience | developer | developer | general |
Figures from each repo's GitHub metadata at analysis time.
Requires a server with Node.js, MongoDB, and Meteor for the balancer, plus a local inference engine on each worker machine.
LLMrPro is a self hosted router that lets you use your own computers to run AI language models instead of always paying a cloud provider. It gives you one address to send requests to, shaped exactly like the format OpenAI uses, so any tool or app built for OpenAI can point at LLMrPro instead without changes. Behind that one address, LLMrPro spreads the actual work across a pool of machines you own, such as desktops, workstations, or servers. Each of your machines runs a small agent program that connects outward to the central router over a websocket connection, so you do not need to open any firewall ports. That agent then runs the request on a local AI engine already installed on the machine, such as LM Studio, Ollama, vLLM, llama.cpp, or a bundled engine for Mac computers. Requests are grouped into three tiers named frontier, mini, and nano, and each tier can be linked to a specific cloud provider, like OpenAI, Anthropic, Google, or Azure OpenAI, as a backup. If none of your own machines can handle a request for a given tier, LLMrPro automatically sends it to that tier's cloud provider instead, so a request still gets answered even when your hardware pool is busy or offline. The project includes a web based admin panel for managing which devices are paired to the pool, revoking access from an agent, configuring tiers, and viewing a log of dispatched requests. It also builds in security features such as rate limiting per source, protection against requests being redirected to internal addresses, encrypted storage of provider API keys, and signed tokens with expiration for each connected agent. Setting it up involves running a central balancer program on a server, which needs Node.js, MongoDB, and Meteor, along with a reverse proxy for secure connections in production. Each worker machine then installs the desktop agent, either through a one line installer script or a downloadable app for macOS or Linux, and pairs itself to the balancer using a code generated by an administrator. The README does not state a software license for this project.
A self-hosted router that lets your own computers run AI language models behind one OpenAI-compatible address, falling back to the cloud only when needed.
Mainly JavaScript. The stack also includes JavaScript, Node.js, Meteor.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.