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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

• 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 in production?

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

• 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.
• 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::{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).

Supported Integrations

Vector stores are available as separate companion-crates:

• MongoDB: rig-mongodb
• LanceDB: rig-lancedb
• Neo4j: rig-neo4j
• Qdrant: rig-qdrant
• SQLite: rig-sqlite
• SurrealDB: rig-surrealdb
• Milvus: rig-milvus
• ScyllaDB: rig-scylladb
• AWS S3Vectors: rig-s3vectors
• HelixDB: rig-helixdb

The following providers are available as separate companion-crates:

• AWS Bedrock: rig-bedrock
• Fastembed: rig-fastembed
• Eternal AI: rig-eternalai

We also have some other associated crates that have additional functionality you may find helpful when using Rig:

• rig-onchain-kit - the Rig Onchain Kit. Intended to make interactions between Solana/EVM and Rig much easier to implement.