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ANSHU9127 / repository
The Multi-Agent Workflow Orchestrator is an offline AI simulation system designed to demonstrate how multiple intelligent agents can collaborate to complete complex tasks autonomously. Built using Python and FastAPI, it mimics the functionality of modern Agentic AI frameworks like LangGraph or AutoGPT while operating entirely without external .
The Multi-Agent Workflow Orchestrator is an offline AI simulation system designed to demonstrate how multiple intelligent agents can collaborate to complete complex tasks autonomously. Built using Python and FastAPI, it mimics the functionality of modern Agentic AI frameworks like LangGraph or AutoGPT while operating entirely without external .
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ruvnet /
🌊 The leading agent meta-harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
open-multi-agent /
TypeScript AI agent orchestration framework with dynamic workflows. Describe the goal, not the graph: a coordinator plans the task DAG at runtime and runs it on any LLM (Claude, ChatGPT, Gemini, DeepSeek, or local models).
jnMetaCode /
🚀 One sentence → multi-AI-role collaboration → complete plan in minutes. Built on the agency-agents role library (216+ experts), zero-code YAML, web Studio + desktop app, 10 LLM providers (7 free). 基于 agency-agents 专家库，一句话调度多个 AI 专家自动协作，几分钟交付完整方案。
lofcz /
The .NET library to build AI agents with 30+ built-in connectors.
InfinitiBit /
GraphBit is the world’s first enterprise-grade Agentic AI framework, built on a Rust core with a Python wrapper for unmatched speed, security, and scalability. It enables reliable multi-agent workflows with minimal CPU and memory usage, making it production-ready for real-world enterprise environments.
ag2ai /
The fastest way to bring multi-agent workflows to production.