Rig

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Rig is a Rust library for building LLM applications, and rig, a terminal coding agent built on it that you extend with Bevy plugins.

The coding agent

cargo install rig
rig

In the agent, /login signs in with a ChatGPT plan or saves a provider's API key, or export the key (such as OPENAI_API_KEY) before starting. /model picks the model and /help lists the commands. rig -p "fix the failing test" answers one prompt without the terminal view.

• Everything is a plugin. The agent is a Bevy app; its tools, commands, sessions, sign-in and terminal view are each a plugin listed in plugins.toml, so any of them can be removed, replaced or added to.
• It rebuilds itself. /reload rebuilds the agent with your plugin changes and restarts it in the same session, and keeps the old build running if the new one does not compile.
• Scriptable. rig -p --json and rig --json drive it over pipes; rig help-agents is the protocol.
• More when you want it. Subagents and checkpoints of what shell changed are optional plugins, and the tools work on remote hosts over ssh or any command.

The agent guide covers the keys, sessions, compaction, plugins and files, and the plugin guide shows how to write a tool, command or panel. The agent needs Rust 1.97.1 or newer and runs on Linux and macOS.

The library

cargo add rig

use rig::prelude::*;
use rig::providers::openai::{self, OpenAI};

#[tokio::main]
async fn main() -> Result<(), anyhow::Error> {
    // The client reads `OPENAI_API_KEY` and builds the models it serves.
    let model = OpenAI::from_env()?.completion(openai::GPT_6_1_SOL);
    let comedian = AgentBuilder::new(model)
        .preamble("You are a comedian here to entertain the user using humour and jokes.")
        .build();

    let response = comedian.prompt("Entertain me!").await?;
    println!("{}", response.output());
    Ok(())
}

#[tokio::main] needs tokio's macros and rt-multi-thread features (cargo add tokio --features macros,rt-multi-thread).

• 20+ model providers and 10+ vector stores, each under one interface
• Agents with tools, multi-turn streaming and typed hooks
• Completion, embedding, transcription, audio and image generation
• GenAI semantic conventions for OpenTelemetry
• Recording and replay of an agent's effects (rig-cassette)
• Browser WASM (wasm32-unknown-unknown) for the core and the agent runtime (target support)

More in examples/, the docs and the API reference.

rig-core no longer forces serde_json's preserve_order or float_roundtrip features. Without them, JSON object keys sort by name, including schema properties and JSON text embedded in prompts, embeddings and tool results. Parsing floating-point numbers can change their low bits. To restore the previous configuration, add this to your application's Cargo.toml dependencies:

serde_json = { version = "1", features = ["preserve_order", "float_roundtrip"] }

Cargo unifies features across dependencies. rig-cassette with traffic (and therefore rig-traffic) still enables both features. rig-claude-cloak, rig-mcp-client and rig-mongodb through BSON still enable preserve_order. These are application-level serde_json choices; Rig has no forwarding feature.

Crates

The rig crate re-exports the core and the agent runtime, and each companion crate behind a feature.

Crate	What it is
rig-core	Provider-neutral messages, completion and embedding models, tool contracts, the conversation type, vector-store contracts, and the providers
rig-agent	The agent builder, prompting and streaming, hooks, the tool registry and the serializable AgentRun
rig-memory	Windows, compaction, and saving, resuming and forking conversations
rig-cassette	Effect logs: recording, replay and checkpoints
rig-tools	Coding tools any rig agent can use: read, write, edit, search, shell
rig-ecs, rig-harness	The agent kernel on Bevy's ECS, and the harness the coding agent's plugins build on
rig-tui, rig-basics	The terminal view, and the coding agent's default plugins
rig-traffic	Opt-in provider HTTP recording in the session store, using the cassette engine

Integrations

rig = { version = "0.44.0", features = ["lancedb", "fastembed"] }

Integration	Crate	Feature	Module path
AWS Bedrock	rig-bedrock	bedrock	rig::bedrock
AWS S3Vectors	rig-s3vectors	s3vectors	rig::s3vectors
Candle (local Llama/SmolLM2/Qwen3 tools, YOLOv8 pose)	rig-candle	candle	rig::candle
Cloudflare Vectorize	rig-vectorize	vectorize	rig::vectorize
FastEmbed	rig-fastembed	fastembed	rig::fastembed
Google Gemini gRPC	rig-gemini-grpc	gemini-grpc	rig::gemini_grpc
Google Vertex AI	rig-vertexai	vertexai	rig::vertexai
HelixDB	rig-helixdb

Community

Projects built on Rig are listed in awesome-rig; open a pull request there to add yours. Questions and ideas are welcome on Discord, and CONTRIBUTING.md says how to contribute. Rig is MIT licensed.

[!WARNING] Rig is pre-1.0. Releases contain breaking changes; each release's changelog and migration notes say what changed and how to move.