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

A developer has released langchain-rust, a pure Rust framework for building LLM applications with local models via Ollama, eliminating the need for Python. The framework includes first-class Ollama support, nine vector store backends, BM25 keyword search, hybrid retrieval, GraphRAG, CorrectiveRAG, and a code interpreter, all packaged as a single binary.

If you're running local models through Ollama and tired of Python's overhead, check out langchain-rust https://github.com/atliliw/langchainrust . 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