Локальные LLM
shimmy
⚡ Pure-Rust WebGPU inference engine — OpenAI-API compatible, GGUF native, runs on any GPU. No Python. No llama.cpp. Single binary.
★ 5,9 тыс. звёздApache-2.0
Смотреть руководство →ragflow — проект с открытым исходным кодом из категории «RAG и базы знаний» от infiniflow (infiniflow/ragflow на GitHub). У него 91 633 звезды на GitHub и 10 881 форк, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 3 октября 2026.
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
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.
Swap between hundreds of open models freely.
Existing tools plug in by changing one URL.
Ask questions over your PDFs, notes, and codebases.
Pay nothing per token, ever.
Prompts and documents stay on your hardware, always.
RAG, tools, and agents build on the same base.
Download the source and move into the project folder.
git clone https://github.com/infiniflow/ragflow.git
cd ragflowEvery 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 infiniflow/ragflow with 10,881 forks. Check the repo's contributors and recent commits to see how active maintenance is.
Apache 2.0 licensed — commercial use allowed with attribution.
8B models run on 16GB RAM; bigger models want a real GPU.
A recent 8–32B instruct model is the sweet spot for most machines.
Yes — with no network calls, there's nothing to leak.
⚡ 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/
🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.
Incremental engine that keeps RAG indexes and agent context fresh by processing only the delta.
Local knowledge-base RAG and agent app built on LangChain with ChatGLM, Qwen and Llama models
Ragent (容纳智能体): a production-grade Java Agentic RAG platform with hybrid retrieval and an admin console
Другие проекты из категории «RAG и базы знаний» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если ragflow вам не подходит.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Ready-to-run templates for RAG, AI pipelines and enterprise search that stay in sync with live data
Соседние проекты из категории «RAG и базы знаний» в каталоге, ближайшие по числу звёзд к ragflow — продолжайте изучать полку по обе стороны от него.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
Private document chat for attorneys: local LLMs, verifiable citations.
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Chat with PDFs and see exactly where each answer was found.
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.