Lists (11)
Stars
The ultimate LLM/AI application development framework in Golang.
🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming
R1-searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
No fortress, purely open ground. OpenManus is Coming.
A live stream development of RL tunning for LLM agents
Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, Du…
A very simple GRPO implement for reproducing r1-like LLM thinking.
verl: Volcano Engine Reinforcement Learning for LLMs
Implementing DeepSeek R1's GRPO algorithm from scratch
🔥 Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API.
🌐 Make websites accessible for AI agents. Automate tasks online with ease.
This is a continuously updated handbook for readers to easily track the latest Text-to-SQL techniques in the literature and provide practical guidance for researchers and practitioners. If we misse…
A lightweight, powerful framework for multi-agent workflows
A curated list of of awesome UI agents resources, encompassing Web, App, OS, and beyond (continually updated)
The most reliable AI agent framework that supports MCP.
A python wrapper for Alpha Vantage API for financial data.
An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models. The goal of this repo is to provide the si…
SGLang is a fast serving framework for large language models and vision language models.
Pure Python from-scratch zero-dependency implementation of Bitcoin for educational purposes
The official Python SDK for Model Context Protocol servers and clients
[ICLR 2024] Efficient Streaming Language Models with Attention Sinks
Example models using DeepSpeed
本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）