Multi-Agent AI Systems
Discover 18 open source Multi Agent repositories from GitHub, each with an analysis of what it does, key features, use cases and alternatives. Multi Agent projects here are most often combined with AI Agents, Developer Tools and Agent Orchestration. Last updated October 3, 2026.
18 repositories · updated October 3, 2026
Understand-Anything: Explore Code Through Knowledge Graphs
Understand-Anything analyzes codebases, documentation, and knowledge bases, then presents their structure and relationships as interactive graphs. It is designed for developers and teams who want faster onboarding, code exploration, and change-impact analysis through AI coding platforms.
deliberation: Get Multi-Model Reviews and Consensus
Deliberation is an MCP server and coding-agent plugin that routes plans and code questions to external AI models for second opinions or arbiter-mediated consensus. It suits developers who want independent reviews before making consequential changes.
MiroFish: Simulate Agent Societies to Explore Possible Futures
MiroFish uses LLM-powered agents and a knowledge graph to simulate social scenarios from source material. It is aimed at teams and individuals exploring public opinion, policy, financial signals, or creative what-if scenarios before making decisions.
deer-flow: Orchestrate Long-Running AI Agent Tasks
DeerFlow is a self-hosted harness for AI agents that coordinate subagents, memory, tools, and sandboxes on tasks that can run from minutes to hours. It suits teams building research, coding, and content workflows that need an extensible agent runtime.
agent-zero: Give AI Agents a Linux Workbench
Agent Zero is a Python framework for agents that need a working environment, not just a chat interface. It combines a Dockerized Linux desktop, browser, document editing, projects, extensions, and optional access to host-machine files.
plexe: Build Machine Learning Models from Prompts
Plexe turns a natural-language description and tabular dataset into a trained, packaged machine learning model through a multi-agent workflow. It is aimed at developers and data teams who want to automate model selection and iteration while retaining control over configuration and deployment.