Rig

Rig is a Rust library for building LLM-powered applications that focuses on ergonomics and modularity.

More information about this crate can be found in the crate documentation.

Table of contents

• Rig
  • Table of contents
  • High-level features
  • Installation
  • 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, SQLite, in-memory)
• Integrate LLMs in your app with minimal boilerplate

Installation

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.model("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).

Integrations

Rig supports the following LLM providers out of the box:

• Anthropic
• Azure
• Cohere
• Deepseek
• Galadriel
• Gemini
• Groq
• Huggingface
• Hyperbolic
• Mira
• Mistral
• Moonshot
• Ollama
• Openai
• OpenRouter
• Perplexity
• Together
• Voyage AI
• xAI

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

The following providers are available as separate companion-crates:

• Fastembed: rig-fastembed
• Eternal AI: rig-eternalai