Multi-Agent AI Systems
Multi-agent AI systems coordinate multiple software agents to work toward shared goals. Agents may divide a task into smaller parts, use different tools or roles, and exchange results before producing an outcome. This approach can help manage complex workflows that benefit from parallel work, specialized capabilities, or repeated review. It also introduces challenges such as coordinating actions, tracking shared context, handling errors, and keeping costs and execution time under control.
Open source tools in this area include frameworks for defining agents and workflows, orchestration layers for assigning and monitoring tasks, and interfaces for human review or collaboration. When choosing one, consider its maturity, license, maintenance activity, model and tool integrations, deployment requirements, and support for debugging and oversight. These tools are useful to developers and teams building research, automation, or software development workflows that involve several coordinated AI agents.
17 repositories · updated October 3, 2026
OpenCompany: Run a Hive Mind of AI Agents for Your Business
OpenCompany is a Rust-powered platform that enables a single operator to run an entire business using a "hive mind" of AI agents. It orchestrates specialized agents to handle various company functions, allowing the human operator to focus on vision and critical decisions. This innovative approach moves beyond traditional multi-agent systems, fostering concurrent deliberation and task completion.
Codeg: Collaborative Multi-Agent AI Coding Workspace for Developers
Codeg (Code Generation) is an innovative multi-agent AI coding workspace built in Rust, designed to unify and enhance the developer experience. It aggregates sessions from various AI coding agents like Claude Code, Codex, and Grok Build into one searchable environment, facilitating seamless collaboration and task management. Available as a desktop app, self-hosted server, or Docker container, Codeg also offers native iOS and Android clients for on-the-go productivity.
CCCC: Coordinate Your Coding Agents Like a Group Chat
CCCC is a production-minded orchestrator designed to coordinate coding agents like a group chat, offering features such as read receipts, delivery tracking, and remote operations from your phone. It enables 24/7 workflow for multi-agent teams with a single `pip install` and zero infrastructure. This tool helps manage diverse AI runtimes, ensuring persistent collaboration across different machines and trusted working groups.
Agent Orchestrator: Supervise Teams of Coding Agents from Planning to Merge
Agent Orchestrator is a powerful tool designed to run and supervise teams of coding agents throughout the entire development lifecycle, from initial planning to code merge. It supports a wide array of agent harnesses, including Claude Code and Codex, and operates across desktop, web, mobile, and cloud environments. This platform offers a unified workspace to manage and coordinate multiple agents, ensuring efficient and organized agent-driven development.
AIWG: Reusable Context & Workflows for AI-Augmented Development
AIWG is a cognitive architecture designed to enhance AI-augmented software development. It provides reusable project context and specialist workflows, enabling structured development, review, and operational tasks across various AI tools and platforms.
AgentBridge: Local Bridge for Claude Code and Codex Collaboration
AgentBridge is an innovative local tool designed to facilitate real-time, bidirectional collaboration between Claude Code and Codex AI agents. It enables seamless cross-review, task splitting, and quota relay, allowing these powerful models to work together efficiently within a single session without manual intervention. Notably, the tool itself was largely built by Claude Code and Codex collaborating through AgentBridge.
swarmclaw: Run and Coordinate Self-Hosted AI Agents
SwarmClaw is a self-hosted runtime and control plane for building AI agents and coordinating them as teams. It combines persistent memory, tools, schedules, and delegation with support for many model providers and external runtimes.
nanobot: Run a Self-Hosted Personal AI Agent
nanobot is a Python framework for running a personal AI agent through a browser, terminal, or chat apps. It combines model integrations with tools, memory, MCP, and scheduled workflows for people who want a customizable agent they can host themselves.
lobehub: Organize and Run Teams of AI Agents
LobeHub is a web app for building, coordinating, and scheduling AI agents as a team. It suits individuals and teams who want shared agent workflows, model choice, and self-hosting, but requires configuring an AI model provider.
AgentTeams: Collaborative Multi-Agent OS for Human-in-the-Loop Task Coordination
AgentTeams is an innovative open-source Collaborative Multi-Agent Operating System designed for transparent, human-in-the-loop task coordination. It leverages Matrix rooms to facilitate seamless interaction between human users and AI agents. This platform empowers teams to manage complex tasks efficiently, ensuring full visibility and control over agent activities.
LazyLLM: Low-Code Development for Multi-Agent LLM Applications
LazyLLM offers a low-code development tool designed for building multi-agent LLM applications with ease. It simplifies the creation of complex AI applications, providing a streamlined workflow for rapid prototyping, data feedback, and iterative optimization. Developers can leverage its extensive features for deployment, cross-platform compatibility, and efficient model fine-tuning.
my-virtual-world: Host a 3D World for AI Agents
My Virtual World is a self-hosted 3D environment where AI agents can move, interact with objects, and show activity from local agent systems. It suits people who want a visual workspace for OpenClaw, Hermes, or other agent harnesses.