Popular GitHub Repositories for Swarms

A compilation of the best multi-agent papers

An open source implementation of LFMs from Liquid AI: Liquid Foundation Models

Implementation of MoE Mamba from the paper: "MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts" in Pytorch and Zeta

My implementation of "Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models"

Robot communication and coordination network.

Implementation of the text to video model LUMIERE from the paper: "A Space-Time Diffusion Model for Video Generation" by Google Research

The next evolution of Agents

tickr-agent is an enterprise-ready, scalable Python library for building swarms of financial agents that conduct comprehensive stock analysis and produce insights.

Implementation of AutoRT: "AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents"

A production-grade multi-agent system for comprehensive medical diagnosis and coding using specialized AI agents.

A radically simple, reliable, and high performance template to enable you to quickly get set up building multi-agent applications

This template demonstrates how to create a collaborative team of AI agents that work together to process, analyze, and generate insights from documents.

Welcome to **FluidAPI**, a framework that allows you to interact with APIs using **natural language**. No more JSON, headers, or complex formats—simply describe your request in plain English, and FluidAPI will do the rest.

Implementation of the "the first large-scale multimodal mixture of experts models." from the paper: "Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts"

AthenaOS is a next generation AI-native operating system managed by Swarms of AI Agents

Terraform framework for deploying [elizaos/eliza, swarms] ai agents

Mamba R1 represents a novel architecture that combines the efficiency of Mamba's state space models with the scalability of Mixture of Experts (MoE).

An all-new OS that orchestrates autonomous agents as workers to execute tasks.

Train a production grade GPT in less than 400 lines of code. Better than Karpathy's verison and GIGAGPT

This project aims to compare different Retrieval-Augmented Generation (RAG) frameworks in terms of speed and performance.