A compilation of the best multi-agent papers

Time-R1 is a two-stage reinforcement fine-tuning framework that trains large language models to perform slow-thinking, step-by-step reasoning for accurate and explainable time series forecasting.

Sell stuff and see what sticks

The Open Assistant API is a ready-to-use, open-source, self-hosted agent/gpts orchestration creation framework, supporting customized extensions for LLM, RAG, function call, and tools capabilities.…

Drop in replacement for the OpenAI Assistants API

The simplest, fastest repository for training/finetuning small-sized VLMs.

Run your own AI cluster at home with everyday devices

Using Mem0 with Agency Swarm

The leading agentic finance toolkit for AI agents

prime is a framework for efficient, globally distributed training of AI models over the internet.

UNIX-like reverse engineering framework and command-line toolset

AIHawk aims to easy job hunt process by automating the job application process. Utilizing artificial intelligence, it enables users to apply for multiple jobs in a tailored way.

The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices

Flexible and powerful multi-agent AI framework

An open source implementation of the gameNgen paper

Open-source implementation of the GameNGen paper.

Implementation of a framework for Genie2 in Pytorch

RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs

[ICLR 2025] Automated Design of Agentic Systems

The Open Assistant API is a ready-to-use, open-source, self-hosted agent/gpts orchestration creation framework, supporting customized extensions for LLM, RAG, function call, and tools capabilities.…

Python scraper based on AI

LLM inference in C/C++

structured outputs for llms

A minimal GPU design in Verilog to learn how GPUs work from the ground up

RL Environments in JAX 🌍

Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.