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[ARTICLE · art-115135] src=github.com ↗ pub= topic=artificial-intelligence verified=true sentiment=↑ positive

Rig – Agentic Workflows in Rust

Rig, a Rust library for building LLM-powered applications, has been released with support for agentic workflows, multi-turn streaming, and 20+ model providers under a unified interface. The library, developed by 0xPlaygrounds, includes a classic agent runtime, 10+ vector store integrations, and browser-WASM support, with users including St. Jude, Coral Protocol, and Nethermind.

read5 min views1 publishedAug 29, 2026
Rig – Agentic Workflows in Rust
Image: Michielbdejong (auto-discovered)

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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.

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 and crate API reference documentation.

  • Agentic workflows that can handle multi-turn streaming and prompting
  • A classic agent runtime enabled by default
  • Full GenAI Semantic Conventioncompatibility - 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
  • Browser-WASM ( wasm32-unknown-unknown

) support for the portable core and classic runtime — seetarget supportfor the full matrix (WASI is not supported;rig-rmcp

/MCP is native-only)

Rig separates portable provider/backend contracts from agent orchestration:

rig-core

contains provider-neutral messages, completion models, portable tools, memory and vector-store contracts, and built-in provider mappings.rig-agent

contains the classic builder, prompt/streaming traits, typed hooks, contextual tools, extraction, and the serializableAgentRun

state machine. It remains enabled by default.

The root rig

facade re-exports both at their familiar paths, so most code depends only on 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, a genomics visualisation tool.proteinpaint

Coral Protocol- Using Rig extensively, both internally as well as part of theCoral 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 usesrig

for simplifying LLM calls and implementing the model picker.Con- Con is a GPU-accelerated terminal emulator with a built-in AI agent harness. It uses Rig as the provider abstraction layer for its integrated coding agents.Dria- a decentralised AI network. Currently using Rig as part of theircompute node.Nethermind- Using Rig as part of theirNeural Interconnected Nodes Engineframework.Neon- Using Rig for theirapp.buildV2 reboot in Rust.Listen- A framework aiming to become the go-to framework for AI portfolio management agents. Powersthe Listen app.Cairnify- helps users find documents, links, and information instantly through an intelligent search bar. Rig provides the agentic foundation behind Cairnify’s AI search experience, enabling tool-calling, reasoning, and retrieval workflows.Ryzome- Ryzome is a visual AI workspace that lets you build interconnected canvases of thoughts, research, and AI agents to orchestrate complex knowledge work.deepwiki-rs- Turn code into clarity. Generate accurate technical docs and AI-ready context in minutes—perfectly structured for human teams and intelligent agents.Cortex Memory- The production-ready memory system for intelligent agents. A complete solution for memory management, from extraction and vector search to automated optimization, with a REST API, MCP, CLI, and insights dashboard out-of-the-box.Ironclaw- A secure personal AI assistantilert- Incident management & alerting platform. Uses Rig as the multi-provider abstraction in its agentic LLM proxy powering ilert AI.Archestra- MCP-native secure AI platform. Uses Rig in its agentic benchmark.

For a curated list of Rig projects, libraries, tools, articles, and production users, check out awesome-rig.

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

Use the root rig

facade when you want feature-gated access to companion crates, or use rig-core

directly when you only need the core provider abstractions.

cargo add rig
use rig::prelude::*;
use rig::providers::openai;

#[tokio::main]
async fn main() -> Result<(), anyhow::Error> {
    // Create OpenAI client
    let client = openai::Client::from_env()?;

    // Create agent with a single context prompt
    let comedian_agent = client
        .agent(openai::GPT_5_2)
        .preamble("You are a comedian here to entertain the user using humour and jokes.")
        .build();

    // Prompt the agent and print the response
    let response = comedian_agent.prompt("Entertain me!").await?;

    println!("{response}");

    Ok(())
}

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 in each crate's examples

directory (for example, examples). Provider-specific integration coverage lives under

, with cassette-backed tests that replay offline by default and live-only tests kept separate when real provider APIs are still required. See

tests/providers

for test target, replay, record, and cassette safety commands. More detailed use case walkthroughs are regularly published on our

tests/README.md

Dev.to Blogand added to Rig's official documentation at

rig.rs/docs.

The root rig

facade exposes companion crates behind one feature per integration:

rig = { version = "0.36.0", features = ["lancedb", "fastembed"] }
Integration Crate Feature Module path
AWS Bedrock
rig-bedrock

bedrock

rig::bedrock

rig-s3vectors

s3vectors

rig::s3vectors

rig-candle

candle

rig::candle

rig-vectorize

vectorize

rig::vectorize

rig-fastembed

fastembed

rig::fastembed

rig-gemini-grpc

gemini-grpc

rig::gemini_grpc

rig-vertexai

vertexai

rig::vertexai

rig-helixdb

helixdb

rig::helixdb

rig-lancedb

lancedb

rig::lancedb

rig-memory

memory

rig::memory

rig-milvus

milvus

rig::milvus

rig-mongodb

mongodb

rig::mongodb

rig-neo4j

neo4j

rig::neo4j

rig-postgres

postgres

rig::postgres

rig-qdrant

qdrant

rig::qdrant

rig-scylladb

scylladb

rig::scylladb

rig-sqlite

sqlite

rig::sqlite

rig-surrealdb

surrealdb

rig::surrealdb

rig::memory

is available without the memory

feature; it contains the core conversation memory traits and in-memory backend re-exported from rig-core

. Enabling features = ["memory"]

adds reusable history-shaping policy types from the rig-memory

companion crate to the same module.

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

rig-onchain-kit

  • theRig Onchain Kit.Intended to make interactions between Solana/EVM and Rig much easier to implement.
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