Rust AI in Practice: Building LLM Applications With Rig

Rust AI is moving from the experimentation stage toward more practical usage. We recently kicked off a new livestream series with the Rust Foundation to explore how Rust and AI are coming together in real-world applications. In the first session, our Developer Advocate Orhun Parmaksız spoke with Stephen Korzeniewski, Lead Maintainer of Rig at 0xPlaygrounds.

Orhun demonstrated a small coding agent he had created with Rig and Ratatui. Then he and Stephen reviewed the code together during the session. The demo gives us a practical starting point for looking at how AI agents work in Rust and what Rig makes possible.

TL;DR

Rig gives Rust developers a common interface for working with LLM providers such as OpenAI, Anthropic, and Gemini. It supports AI agents, tools, RAG, database integrations, streaming, and local models. During the livestream, Orhun demonstrated a small coding agent built with Rig and Ratatui, while Stephen walked through the agent’s setup, tools, and supporting abstractions.

What is Rig?

Rig is an open-source Rust library for working with large language models from various providers. OpenAI, Anthropic, Gemini, and other providers each offer their own API. And while many providers claim to be OpenAI-compatible, each service behaves a little differently in reality.

Rig handles those differences by using a single Rust interface for all providers, so developers can switch providers without rewriting the parts of their application that depend on the LLM.