A Rust library for building scalable, modular, and ergonomic LLM-powered applications.
Rig is a Rust library for building scalable, modular, and ergonomic LLM-powered applications. It provides a unified interface to integrate multiple AI model providers and vector stores, simplifying the development of agentic workflows and AI features. The library aims to reduce boilerplate and offer flexibility across different backends and capabilities.
Rust developers building AI-powered applications, agents, or services that require integration with various LLM providers and vector databases. It's also suitable for teams needing observability via OpenTelemetry or WebAssembly compatibility.
Developers choose Rig for its ergonomic Rust API, unified interfaces across many providers, and support for complex agentic workflows with minimal boilerplate. Its modular design and broad integration ecosystem make it a versatile choice for production LLM applications.
⚙️🦀 Build modular and scalable LLM Applications in Rust
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Integrates with 20+ LLM providers through a single, consistent API, reducing code complexity when switching or combining models, as evidenced by the broad provider support listed.
Offers a unified interface for 10+ vector databases like Qdrant and LanceDB, enabling flexible retrieval-augmented generation (RAG) systems without vendor lock-in.
Enables multi-turn streaming, prompting, and tool-calling for building intelligent agents with minimal boilerplate, as highlighted in the features for scalable LLM applications.
Fully compatible with OpenTelemetry's GenAI Semantic Conventions, facilitating production monitoring and debugging of AI workflows out of the box.
The README explicitly warns that future updates will contain breaking changes, making it risky for projects requiring stable APIs without frequent migrations.
Requires managing multiple companion crates for different integrations (e.g., rig-qdrant, rig-bedrock), which can increase setup and maintenance overhead compared to all-in-one libraries.
Exclusively targets Rust developers, so it's unsuitable for teams using other languages or needing cross-platform SDKs, limiting its adoption in polyglot environments.