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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.

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 crate documentation.

Help us improve Rig by contributing to our Feedback form.

Table of contents

• What is Rig?
• Table of contents
• High-level features
• Get Started
  • Simple example:
• Integrations

High-level features

• Full support for LLM completion and embedding workflows
• Simple but powerful common abstractions over LLM providers (e.g. OpenAI, Cohere) and vector stores (e.g. MongoDB, in-memory)
• Integrate LLMs in your app with minimal boilerplate

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. src/examples) directory. More detailed use cases walkthroughs are regularly published on our Dev.to Blog.

Supported Integrations

Model Providers	Vector Stores

Vector stores are available as separate companion-crates:

• MongoDB vector store: rig-mongodb
• LanceDB vector store: rig-lancedb
• Neo4j vector store: rig-neo4j
• Qdrant vector store: rig-qdrant