dnrops/rig

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✨ If you would like to help spread the word about Rig, please consider starring the repo!

[!WARNING] Here be dragons! As we plan to ship a torrent of features in the following months, future updates will contain breaking changes. With Rig evolving, we’ll annotate changes and highlight migration paths as we encounter them.

Table of contents
What is Rig?
High-level features
Who’s using Rig in production?
Get Started
- Simple example
Integrations

What is Rig?

Rig is a Rust library for building scalable, modular, and ergonomic LLM-powered applications.

More information about this crate can be found in the official & crate (API Reference) documentations.

Features

Agentic workflows that can handle multi-turn streaming and prompting
Full GenAI Semantic Convention compatibility
20+ model providers, all under one singular unified interface
10+ vector store integrations, all under one singular unified interface
Full support for LLM completion and embedding workflows
Support for transcription, audio generation and image generation model capabilities
Integrate LLMs in your app with minimal boilerplate
Full WASM compatibility (core library only)

Who is using Rig?

Below is a non-exhaustive list of companies and people who are using Rig:

St Jude - Using Rig for a chatbot utility as part of proteinpaint, a genomics visualisation tool.
Coral Protocol - Using Rig extensively, both internally as well as part of the Coral Rust SDK.
VT Code - VT Code is a Rust-based terminal coding agent with semantic code intelligence via Tree-sitter and ast-grep. VT Code uses rig for simplifying LLM calls and implement model picker.
Dria - a decentralised AI network. Currently using Rig as part of their compute node.
Nethermind - Using Rig as part of their Neural Interconnected Nodes Engine framework.
Neon - Using Rig for their app.build V2 reboot in Rust.
Listen - A framework aiming to become the go-to framework for AI portfolio management agents. Powers the Listen app.

Are you also using Rig in production? Open an issue to have your name added!

Get Started

cargo add rig-core

Simple example

use rig::{client::CompletionClient, completion::Prompt, providers::openai};

#[tokio::main]
async fn main() {
    // Create OpenAI client and model
    // This requires the `OPENAI_API_KEY` environment variable to be set.
    let openai_client = openai::Client::from_env();

    let gpt4 = openai_client.agent("gpt-4").build();

    // Prompt the model and print its response
    let response = gpt4
        .prompt("Who are you?")
        .await
        .expect("Failed to prompt GPT-4");

    println!("GPT-4: {response}");
}

Note using #[tokio::main] requires you enable tokio’s macros and rt-multi-thread features or just full to enable all features (cargo add tokio --features macros,rt-multi-thread).

You can find more examples each crate’s examples (ie. rig-core/examples) directory. More detailed use cases walkthroughs are regularly published on our Dev.to Blog and added to Rig’s official documentation (docs.rig.rs).