LLM 模型排行

The Complete AI Development Toolkit for Claude Code. 106 skills, 36 agents, 171 hooks. Install `ork` for stable (v9.x), or `ork-alpha` for the v10 line, which ships daily.

Universal, model-agnostic operating harness for AI agents (Claude, Codex, Gemini, …) — a lean core + work-type profiles assembled by one setup script.

Awesome LLM Papers and repos on very comprehensive topics.

Tiny toolkit for air-gapped LLMs on consumer-grade hardware

A lightweight, production-ready RAG (Retrieval Augmented Generation) library in Go.

Open source, terminal-first AI coding agent with fully transparent multi-model routing. Local (Ollama, LM Studio, MLX, llama.cpp) or cloud, your keys, one TUI. No black box.

Python SDK for healthcare AI — typed, validated FHIR tools for agents, real-time EHR connectivity, production deployment ✨ 🏥

OpenAI compatible API for TensorRT LLM triton backend

This repository collects papers on VLLM applications. We will update new papers irregularly.

Python library for building with LLMs. One interface across 11 providers (OpenAI, Anthropic, Gemini, DeepSeek, Ollama and more), automatic failover, Pydantic-validated structured output, embeddings with vector search, and an AI-powered router. pip install simplerllm

Provider-pluggable orchestration runtime for multi-model AI inference. ( Sakana Fugu style )

Simplify and improve the job hunting experience by integrating LLMs to automate tasks such as resume and cover letter generation, as well as application submission, saving users time and effort.

A simple and declarative way to control LLMs

Backend library for conversational AI in biomedicine

可同时输入多个初版提示词，开启多轮自动优化，连续得到高分后得到最终结果。

Load local LLMs effortlessly in a Jupyter notebook for testing purposes alongside Langchain or other agents. Contains Oobagooga and KoboldAI versions of the langchain notebooks with examples.

AgenticX is a unified, production-ready multi-agent platform — Python SDK + CLI (agx) + Studio server + Machi desktop app. Features Meta-Agent orchestration, 15+ LLM providers, MCP Hub, hierarchical memory, avatar & group chat, skill ecosystem, safety sandbox, and IM gateway (Feishu/WeChat).

Local proxy that compresses your LLM API requests so you pay less, with no change to the answers. Trims wasted tokens from prompts, history, tool output, and code before they're sent: -31% input / -74% output, measured live. Any provider, no extra model calls. Also an MCP server and embeddable library (Rust, Python, Ruby, Kotlin, Swift, JS/TS).

Private on-device AI chat for Android — runs any GGUF model locally via llama.cpp with ARM-optimised SIMD. Zero network permissions, encrypted settings, biometric lock, tamper detection. + GPU Acceleration

[NeurIPS 2024] This repo contains evaluation code for the paper "Are We on the Right Way for Evaluating Large Vision-Language Models"