merken/pi-local-models — explained in plain English
Analysis updated 2026-05-18
Use a locally running AI model like Ollama or llama.cpp inside the PI Coding Agent.
Switch between different local model servers using PI's /models command.
Keep separate local model configs for different projects.
| merken/pi-local-models | 0xkinno/neuralvault | 0xmayurrr/ai-contractauditor | |
|---|---|---|---|
| Stars | 1 | 1 | 1 |
| Language | TypeScript | TypeScript | TypeScript |
| Setup difficulty | moderate | hard | easy |
| Complexity | 2/5 | 4/5 | 2/5 |
| Audience | developer | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Requires already having a local model server like Ollama or llama.cpp running.
pi-local-models is a small add-on for the PI Coding Agent that helps it find and use AI models running locally on your own computer instead of only relying on cloud based models. It works with any local model server that speaks the same API style as OpenAI, which covers several popular local tools such as llama.cpp, Ollama, oMLX, llama-swap, and MTPLX. Once installed through PI's own package installer, you set it up by creating a small configuration file that points to your local model server, giving it the web address it is running at and an API key. This config file can be placed either for your whole user account or just for a single project, so you can have different local model setups for different projects if you want. After the configuration is in place, you run PI and use its built in /models command, which will then discover and list any local models it finds through this tool, letting you pick one to use inside PI. The readme is intentionally brief. It does not describe internal architecture, testing, or license terms, and it points to a separate example config file in the repository for a fuller reference.
A PI Coding Agent add-on that discovers and connects to AI models running locally on your own machine.
Mainly TypeScript. The stack also includes TypeScript, OpenAI API spec, llama.cpp.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
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