RAG и базы знаний
WeKnora
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
★ 31,9 тыс. звёздUnknown
Смотреть руководство →llmfit — проект с открытым исходным кодом из категории «Локальные LLM» от AlexsJones (AlexsJones/llmfit на GitHub). У него 37 547 звёзд на GitHub и 2 406 форков, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 3 октября 2026.
Hundreds of models & providers. One command to find what runs on your hardware.
One binary or app gets you a working local model setup.
Pick a size that fits your RAM/VRAM; smaller models run on laptops.
Use the built-in UI, or point OpenAI-compatible clients at it.
Airplane mode works — your prompts never leave the device.
Large language models drive its smartest features, locally or via API.
Prompts and documents stay on your hardware, always.
RAG, tools, and agents build on the same base.
Smart compression runs big models on modest hardware.
Works on planes, in basements, behind firewalls.
Swap between hundreds of open models freely.
Download the source and move into the project folder.
git clone https://github.com/AlexsJones/llmfit.git
cd llmfitEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project's launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandIt is an open-source project at AlexsJones/llmfit with 2,406 forks. Check the repo's contributors and recent commits to see how active maintenance is.
MIT licensed — use it commercially, no strings attached.
A recent 8–32B instruct model is the sweet spot for most machines.
Yes — with no network calls, there's nothing to leak.
Close on many tasks now; frontier cloud models still lead on hard reasoning.
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
⚡ Pure-Rust WebGPU inference engine — OpenAI-API compatible, GGUF native, runs on any GPU. No Python. No llama.cpp. Single binary.
动手学Ollama，CPU玩转大模型部署，在线阅读地址：https://datawhalechina.github.io/handy-ollama/
Find the best local models for your hardware — and how to run them.
Strata — one-click Qwen3.8-Flash-Next inference on consumer hardware: chat, code and images, locally, no account, MIT
One-command installer for a full self-hosted AI stack on your server.
Другие проекты из категории «Локальные LLM» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если llmfit вам не подходит.
The simplest way to run open language models on your own machine.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
LLM inference in C/C++
A high-throughput and memory-efficient inference and serving engine for LLMs
🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.
Соседние проекты из категории «Локальные LLM» в каталоге, ближайшие по числу звёзд к llmfit — продолжайте изучать полку по обе стороны от него.
An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
Run a 70B LLM on a single 4GB GPU — layer-by-layer inference with almost no memory tricks skipped
BitNet — Microsoft's official inference framework for 1-bit large language models, running fast on ordinary CPUs, MIT
Strata — one-click Qwen3.8-Flash-Next inference on consumer hardware: chat, code and images, locally, no account, MIT
Fabric is an open-source framework for augmenting humans using AI. It provides a modular system for solving specific problems using a crowdsourced set of AI …
Open-source 24/7 Cowork app for OpenClaw, Hermes, Claude Code, Codex, OpenCode and 20+ more CLI Agent | Customize your assistants | Team them up｜Star if you …
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.