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Products that do what gline-rs – an inference engine for GLiNER models, in Rust does

Hi everyone, wanted to share about gline-rs, an inference engine for GLiNER models written in Rust. This family of lightweight language models proved to be efficient at zero-shot Named Entity Recognition (NER) and other tasks such as Relation Extraction, while consuming less resources than large generative models (LLMs). This implementation has been written from the ground up in Rust, and supports both span- and token-oriented variants (for inference only). The goal is to provide a production-grade and user-friendly API in a modern and safe programming language, including a clean and…

1. 1CA
   Cannoli – A compiler for a subset of Python written in Rust▲292

   2018 · github.com

2. 2SA
   Sauron – A web framework in Rust that adheres to the Elm architecture▲305

   2019 · github.com

3. 3AT
   Askama – Type-safe, compiled Jinja-like templates for Rust▲82

   2017 · github.com

4. 4HR
   Haml-rs – Haml parser in Rust▲40

   2018 · github.com

5. 5FA
   fcp – A significantly faster alternative to cp(1), written in Rust▲88

   2021 · github.com

6. 6AF
   A first project in Rust – in-memory order book▲91

   2020 · github.com

7. 7AS
   A syntax-diagram generator for rust's macro_rules()▲36

   2018 · lukaslueg.github.io

8. 8ML
   Machine learning (entirely) in Rust▲20

   2016 · github.com

9. 9RL
   Rust-Like Traits in Python▲50

   2021 · github.com

10. 10JA
    JQL – A JSON Query Language CLI Tool Built with Rust▲25

    2020 · crates.io

11. 11JA
    Jql – A JSON Query Language CLI Tool Written in Rust▲9

    2018 · github.com

12. 12AO
    An open souce graph database written in Prolog and Rust▲29

    2020 · github.com

13. 13LT
    LLGTRT: TensorRT-LLM+Rust server w/ OpenAI-compat and Structured Output▲6

    I wanted to share a project I've been working on for the past few weeks: llgtrt. It's a Rust implementation of a HTTP REST server for hosting Large Language Models using llguidance library for constrained output with NVIDIA TensorRT-LLM. The server is compatible with the OpenAI REST API and supports structured JSON schema enforcement as well as full context-free grammars (via Guidance). It's similar in spirit to the Python-based TensorRT-LLM OpenAI server example but written entirely in Rust and built with constraints in mind. No Triton Inference Server involved. This also serves as a demo…

    2024 · github.com

14. 14R
    Rusdantic▲5

    A unified, high-performance data validation and serialization framework for Rust, inspired by Pydantic's ergonomics and powered by Serde.

    Mar 2026 · github.com

15. 15IB
    I Built a Groth16 ZK-Proof Verifier from Scratch Using Rust▲6

    I've always been fascinated by cryptographic principles, so I decided to dive deep into zero-knowledge proofs and implement a Groth16 proof verifier entirely in Rust. This project validates cryptographic proofs and demonstrates how succinct, verifiable computations can be achieved. Using Rust's powerful libraries like arkworks, I implemented pairing checks, public input commitments, and proof validation logic while ensuring the code remains modular and efficient. I handled the complexities of affine group operations, scalar multiplications, and optimized public input verification to make…

    2024 · github.com

16. 16CM
    Composable middleware for LLM inference Optimization Passes▲7

    Hey HN, I've been building AutoAgents, an AI agent framework in Rust. Today I'm sharing a feature I haven't seen done well elsewhere: composable middleware layers for LLM inference pipelines. The problem Every agent framework lets you swap LLM providers. Almost none of them give you a structured way to enforce safety, caching, or data sanitization in the inference path itself. You end up with guardrails as application-level if-statements, caching bolted on as a separate service, and PII handling as a "we'll add it later" TODO that never ships. This gets worse with local models. Cloud APIs…

    Mar 2026 · github.com

17. 17FM
    Flow Matching model inference in C▲5

    As the title and description of the GitHub repo suggest, I’m working on a small project for purely educational purposes, with the goal of implementing generative model inference (small models capable of modeling 2D distributions) based on the Flow Matching paradigm in C. I’ve worked on generative AI models based on Flow Matching from a more “abstract” perspective, using frameworks like PyTorch, and I wanted to understand what goes on behind the scenes. The repository is still a work in progress and is also one of my first "serious" projects in C.

    Jul 2026 · github.com

18. 18LR
    Learning Rust with Neovim, Rust-Analyzer and LLMs▲5

    2025 · lorentz.app

19. 19OB
    Offeryn – Build Tools for LLMs in Rust, with Model Context Protocol▲7

    2025 · github.com

20. 20TF
    Torchbear – fast, safe, simple, and complete(R) Rust scripting engine▲6

    2020 · github.com

21. 21RA
    rtoml – a TOML library for Python written in rust▲5

    2020 · github.com

22. 22RA
    rustorm – A simple ORM for rust▲5

    2015 · github.com

23. 23RA
    Rustacean AI – Tracking Rust's Expanding Role in AI▲8

    Rustacean AI, a weekly newsletter exploring how Rust is shaping the future of AI and Machine Learning. Issue #1: From Models to Data — Rust’s Expanding Role in AI Covers the growing Rust ecosystem around AI frameworks (Burn), data systems (Polars, Qdrant, Daft), and emerging tools that power safe, high-performance pipelines. The goal is to curate news, releases, and experiments from across the Rust + AI community — helping developers follow how the language is influencing the next generation of infrastructure. Read first issue here →…

    Oct 2025 · rustacean.ai

24. 24TA
    Tysm, a Rust crate for using ChatGPT with Structured Outputs▲7

    Hi HN! This Christmas I made `tysm`, a rust crates which simplifies using ChatGPT with Structured Outputs. Essentially you just tell it the rust type you want the output to conform to, and it handles creating the JSON Schema, passing it to ChatGPT's chat-completions API, and deserializing the response. Structured Outputs are very useful because they guarantee that the response will conform to whatever type you expect. I'm already finding my crate very useful since I love using Structured Outputs but I hate writing JSON Schemas myself. Technical details for rust heads: To generate the JSON…

    2024 · github.com

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