langchain-rust: Build LLM apps with Ollama + local models in pure Rust — no Python needed

If you're running local models through Ollama and tired of Python's overhead, check out langchain-rust. It's a full LLM framework in pure Rust that works great with local models:

Ollama support — first-class integration with tool calling, vision, and streaming #

9 vector store backends — InMemory, SQLite, Qdrant, ChromaDB, Redis, PGVector, MongoDB, Pinecone, FileVectorStore #

BM25 keyword search — with Chinese/English tokenization, no external dependency #

Hybrid retrieval — BM25 + Vector with RRF fusion for better recall #

GraphRAG — Knowledge graph construction + community detection, all local #

CorrectiveRAG — Self-correcting retrieval with hallucination detection #

Code Interpreter — LocalSandbox (subprocess), E2B cloud, or WASM sandbox #

LocalEmbeddings — Run embeddings without calling an API

Plus: LangGraph workflows, MCP client/server, 7 memory types, guardrails, and 12+ built-in tools.

Single binary, no virtualenv, no pip conflicts. Just cargo add langchainrust

and go.

GitHub: [https://github.com/atliliw/langchainrust](https://github.com/atliliw/langchainrust)

Docs: [https://docs.rs/langchainrust](https://docs.rs/langchainrust)