rig

Build type-safe LLM applications in Rust with modular agents and tool calling.

3|Updated Jan 10, 2026

Search Tags:#llm #agents #rust #tool-calling #vector-store #dynamic-context #rig

Ready to run

npx skills add https://github.com/yankeeinlondon/rusty-biscuit --skill rig-yankeeinlondon

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rig
Source: https://github.com/yankeeinlondon/rusty-biscuit/tree/main/.claude/skills/rig
Command: npx skills add https://github.com/yankeeinlondon/rusty-biscuit --skill rig-yankeeinlondon

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Rig provides a comprehensive, type-safe foundation for building robust, deterministic LLM-powered applications in Rust, enabling reliable tool calling, agent orchestration, and seamless integration with vector stores and multiple providers.

Core Features & Use Cases

Tool calling with schema-driven, type-safe definitions for reliable LLM-Tool interactions
Modular agent orchestration to compose complex workflows across multiple tools
Retrieval-Augmented Generation (RAG) with vector stores and embeddings
Multi-provider support and extensible provider architecture for OpenAI-compatible APIs

Quick Start

Install the Rig ecosystem, create a client, build an agent, and prompt it to perform a multi-tool workflow.

Frequently Asked Questions about rig

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build type-safe LLM agents in Rust?▼

Build type-safe LLM agents in Rust by implementing a trait-driven architecture with schema-driven tool definitions and modular agent orchestration to compose complex, deterministic workflows.

How do I implement tool calling in a Rust LLM application?▼

Implement tool calling in a Rust LLM application using schema-driven, type-safe definitions for tool interactions. This ensures reliable execution when the LLM agent invokes external functions.

Can I use Rust for Retrieval-Augmented Generation with vector stores?▼

Use Rust for Retrieval-Augmented Generation by integrating vector stores and embedding models. This allows you to store and retrieve contextual data for dynamic LLM prompts.

Does this Rust LLM framework support OpenAI-compatible APIs?▼

Yes, the framework supports OpenAI-compatible APIs through its multi-provider architecture. This extensible design allows you to connect to various LLM providers seamlessly.

What is the best way to orchestrate multiple LLM tools in Rust?▼

Orchestrate multiple LLM tools in Rust using modular agent composition. This approach allows you to build complex workflows across multiple tools deterministically.

Why use a trait-driven architecture for Rust LLM apps?▼

Use a trait-driven architecture for Rust LLM apps to enforce type safety across tool definitions, embedding models, and vector stores, ensuring robust and deterministic task execution.