rig

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Rig is a Rust library and toolkit for developers who want to build scalable, modular, and ergonomic LLM-powered applications and agents. It provides a core crate and companion crates to make it straightforward to connect to LLM providers and vector stores, and to embed completion and retrieval workflows into larger applications. The project targets Rust developers building fullstack or backend components that rely on language models, offering examples, integration points, and production users to demonstrate real-world adoption. The README notes a fast-evolving project with potential breaking changes and directs users to official documentation and crate API references for detailed guidance. The repo emphasizes portability, minimal boilerplate, and modularity for integrating LLMs into apps or agent systems.

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4144

App URL

https://github.com/0xPlaygrounds/rig

Github Repository

https://github.com/0xPlaygrounds/rig/blob/main/README.md

Features

Full support for LLM completion and embedding workflows. Common abstractions over multiple LLM providers to simplify provider switching and model usage. Pluggable vector store integrations with companion crates for MongoDB, LanceDB, Neo4j, Qdrant, SQLite, SurrealDB, Milvus, ScyllaDB, and AWS S3Vectors. Provider companion crates for additional embedding or model backends. Minimal boilerplate APIs with examples in the core crate"s examples directory. Cargo-first consumption with a simple cargo add rig-core install. Notes about runtime requirements such as tokio feature flags for async usage. A growing ecosystem of companion crates and documented integration patterns for retrieval-augmented workflows.

Use Cases

Rig helps developers integrate language models into Rust applications by handling provider and vector store plumbing, reducing repetitive code and configuration. It provides ready-made adapters so teams can experiment with different LLMs and storage backends without rewriting core logic. Example code and crate examples show basic usage such as prompting an OpenAI model from an async Rust program. Companion crates make it easier to add vector databases and provider integrations, enabling retrieval-augmented generation and embedding pipelines for search, code search, and agent contexts. The README lists production adopters and related kits that demonstrate practical deployments and interoperability with onchain tooling, signaling maturity for production use while noting migration guidance for breaking changes.

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