RAG e conhecimento
WeKnora
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
★ 31,9 mil estrelasUnknown
Ver o guia →llmfit é um projeto de LLM local de código aberto de AlexsJones (AlexsJones/llmfit no GitHub). Tem 37.547 estrelas no GitHub e 2.406 forks, é publicado sob a licença MIT e foi atualizado pela última vez no GitHub em 3 de outubro de 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.
Os outros projetos de LLM local com mais estrelas no catálogo do LibreAI — a lista realista de alternativas se llmfit não for o ideal para você.
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.
Projetos de LLM local próximos no catálogo, os mais próximos em estrelas de llmfit: continue explorando dos dois lados dele.
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
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Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.