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AI · June 18, 2026

RA

Run Agent Skills with mistral.rs v0.8.10: /v1/skills support and more

Hey all! I'm the maintainer of mistral.rs. I just landed support for OpenAI-compatible Agent Skills via a /v1/skills endpoint, and it works with local open models. Until now Skills have basically been locked to closed models, and with the ability to have private, local intelligence becoming increasingly important, but this feature allows you to do XYZ with local models. It's fully compatible with OpenAI's /v1/skills API, so you can drop mistral.rs into your existing code with minimal difficulty. We support the accompanying tools too: /v1/files or input_file for…

Alternativestop 4% of June 2026

In plain words

Mistral.rs v0.8.10 is a runtime that enables agent skills on local open-source models through an OpenAI-compatible /v1/skills endpoint. Previously, agent skills were limited to closed models, but this release brings that functionality to private, self-hosted deployments. It supports file handling via /v1/files and allows models to return generated files. The software is fully compatible with OpenAI's skills API, making it a drop-in replacement for existing code. Prebuilt binaries are available for NVIDIA CUDA, Apple Silicon, and CPU.

written from the facts on this page · September 2026

From the sources

In the maker’s words, at launch

Hey all! I'm the maintainer of mistral.rs. I just landed support for OpenAI-compatible Agent Skills via a /v1/skills endpoint, and it works with local open models. Until now Skills have basically been locked to closed models, and with the ability to have private, local intelligence becoming increasingly important, but this feature allows you to do XYZ with local models. It's fully compatible with OpenAI's /v1/skills API, so you can drop mistral.rs into your existing code with minimal difficulty. We support the accompanying tools too: /v1/files or input_file for attaching files to your prompts, and mistral.rs also allows models to send generated files back using the OpenAI-compatible method. It's also easier than ever to try mistral.rs: we are including prebuilt binaries for NVIDIA CUDA, Apple Silicon, and CPU! # Linux/Mac > curl --proto '=https' --tlsv1.2 -sSf https://raw.githubusercontent.com/EricLBuehler/mistral.rs/ma... | sh # Windows > irm https://raw.githubusercontent.com/EricLBuehler/mistral.rs/ma... | iex Then: mistralrs serve --agent --isq 4 -m google/gemma-4-E4B-it Super excited for you to try this out and any feedback! Do you have any suggestions for what you would like to see in the next releases? Check out the GitHub: https://github.com/EricLBuehler/mistral.rs Docs & Quickstart: https://ericlbuehler.github.io/mistral.rs/

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