Swarms
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- This comprehensive course teaches students how to build, deploy, and manage autonomous agents for enterprise workflows using the Swarms library. Students will learn to create robust, scalable agent systems that can handle complex business processes, integrate with existing tools, and maintain long-term memory.
auto-orgs
Public- A production-grade multi-agent system for comprehensive medical diagnosis and coding using specialized AI agents.
Xray-Bench
PublicXRayBench is a state-of-the-art evaluation platform designed specifically for assessing the performance of large language models (LLMs) in the domain of medical X-ray image analysis.swarms-examples
PublicInsuranceSwarm
Public- An all-encompassing API for all swarm architectures
auto-ai-research-team
Publicautomated-crypto-fund
PublicA fully automated crypto fund that leverages swarms of llm agents to trade with real-moneypharma-swarm
PublicViTI
PublicDarkCircuit
Publicswarms-models
PublicMulti-Agent-Template-App
Public templateA radically simple, reliable, and high performance template to enable you to quickly get set up building multi-agent applicationsClusterOps
PublicClusterOps is an enterprise-grade Python library developed and maintained by the Swarms Team to help you manage and execute agents on specific CPUs and GPUs across clusters. This tool enables advanced CPU and GPU selection, dynamic task allocation, and resource monitoring, making it ideal for high-performance distributed computing environments.CryptoTaxSwarm
Publicxray_swin_large_patch4
Publicswarm-shield
PublicSwarmShield is an enterprise-grade security system for swarm-based multi-agent communications, providing military-grade encryption, secure conversation management, and comprehensive audit capabilities.AgentAPI
Publicswarms-evals
PublicResearch-Paper-Hive
Publicskynet
PublicAutoHedge
PublicBuild your autonomous hedge fund in minutes. AutoHedge harnesses the power of swarm intelligence and AI agents to automate market analysis, risk management, and trade execution.DPO-MCTS-ToT-Training
PublicThis module implements a post-training mechanism that allows a language model to explore various reasoning branches (chain-of-thoughts) using a Monte Carlo Tree Search (MCTS) framework. It selects the branch with the best answer using a cosine similarity evaluator that compares the candidate answer to a known correct answer.