Posts tagged with: Multi-Agent
Content related to Multi-Agent
Edict: Ancient Empire AI Agents on OpenClaw
Discover Edict, a groundbreaking OpenClaw system inspired by China's 1300-year-old 'Three Departments Six Ministries' imperial structure. 12 specialized AI agents (Prince, Zhongshu, Menxia, Shangshu + 6 ministries) collaborate with institutional checks-and-balances that surpass CrewAI and AutoGen. Features real-time Kanban dashboard, remote skills management, model switching, and one-click Docker demo. Experience ancient wisdom meets modern AI orchestration.
AgentHub: GitHub for AI Agent Swarms by Karpathy
Discover AgentHub, Andrej Karpathy's revolutionary platform designed specifically for AI agent collaboration. Unlike traditional GitHub, this bare git repo + message board supports sprawling DAGs of commits without branches or PRs. Perfect for coordinating swarms of autonomous agents on shared codebases. Built for the autoresearch project but infinitely extensible, AgentHub features Git bundle pushes, agent message boards, API keys, rate limiting, and a simple CLI. Deploy with a single Go binary + SQLite. The future of agent-first development is here.
Golutra: Command AI Workforce with Your CLI Tools
Discover golutra, the cyberpunk overseer system that transforms your existing CLI tools into a unified AI collaboration hub. No project migration needed – keep your familiar commands while unlocking unlimited multi-agent parallel execution, automated orchestration, and real-time tracking. Built with Vue 3 + Rust (Tauri), it supports Claude Code, Gemini CLI, OpenCode, Qwen, OpenClaw on Windows/macOS. Click agent avatars to inspect logs, inject prompts, and monitor your AI squad in stealth terminals. Evolve from single-threaded workflows to self-organizing AI teams.
TinyClaw: Multi-Agent AI Teams for Discord, WhatsApp, Telegram
TinyClaw is a lightweight, MIT‑licensed framework that lets you run multiple AI agents that work together across Discord, WhatsApp, and Telegram. With a single command‑line interface you can spin up teams, configure unique roles, and watch live conversations through a TUI dashboard. The repository includes a full README, installation scripts, channel guides, and advanced features like heartbeat monitoring and agent pairing. Whether you’re a developer looking for a plug‑and‑play chatbot, or a researcher wanting to experiment with collaboration among AI assistants, TinyClaw brings the power of multi‑agent coordination right to your terminal.
oh-my-claudecode: Boost Claude Code with Multi‑Agent Automation
Discover oh-my-claudecode, a free, MIT‑licensed framework that turns Claude Code into a fully automated, multi‑agent system. Learn how to install the plugin in minutes, leverage five powerful execution modes – Autopilot, Ultrapilot, Swarm, Pipeline, and Ecomode – and let specialized agents handle everything from design to testing. With zero learning curve, built‑in analytics, and persistent Python REPL support, this open‑source tool is ideal for developers who want to fast‑track complex projects, reduce token costs, and extract reusable skills. Whether you’re building a full‑stack app, refactoring code, or conducting data science research, oh-my-claudecode streamlines the process and frees up your time.
Agent Skills for Context Engineering: Open Source Mastery
Discover the Agent Skills for Context Engineering repository – a comprehensive, MIT‑licensed collection designed to supercharge AI agent systems. Learn about context engineering fundamentals, multi‑agent patterns, memory systems, and more. The guide walks you through installation in Claude Code, showcases real‑world examples, and explains how to contribute. Whether you’re building a personal operating system or a production‑grade evaluation pipeline, this repo gives you the proven skills to manage a model’s limited attention budget efficiently.
DeepTutor: AI‑Powered Personalized Learning Assistant Open‑Source Project
DeepTutor brings cutting‑edge AI tutoring to your fingertips. This open‑source multi‑agent system combines FastAPI, Next.js, and RAG pipelines to deliver instant Q&A, interactive visualization, personalized practice, and research generation. With full Docker support, a CLI, and an intuitive web interface, developers can quickly spin up a personal AI tutor, experiment with embeddings, or contribute new modules. Explore the architecture, installation steps, core features, and how to contribute, and join the growing community of educators and developers shaping the future of AI‑driven learning.
Langroid: Multi-Agent LLM Framework for Python
Discover Langroid, an intuitive and extensible Python framework for building LLM-powered applications. Developed by researchers from CMU and UW-Madison, Langroid simplifies multi-agent programming, allowing developers to create sophisticated AI solutions with ease. Learn how this framework, which eschews other LLM frameworks like LangChain, empowers users to build robust applications using agents, tasks, and a wide array of tools and integrations. A must-explore for anyone interested in advanced LLM development and multi-agent systems.