Awesome GitHub RepositoriesMulti-Agent Frameworks

MetaGPT is an agentic workflow orchestrator and multi-agent framework designed to transform natural language requirements into complete software deliverables. It functions as an AI software engineering suite that automates the creation of technical documentation, data structures, and source code by treating natural language as a programming environment. The system distinguishes itself by assigning professional roles to large language models, creating specialized agent teams that collaborate through a shared communication structure. It utilizes standard operating procedures to convert organiza

CrewAI is a multi-agent orchestration framework designed for building autonomous systems that execute complex, multi-step workflows. It provides a development platform where specialized agents are defined with specific roles, goals, and tool sets to perform tasks collaboratively. By leveraging a declarative workflow engine, the system manages task dependencies, state transitions, and execution logic, allowing for the creation of structured, stateful sequences of operations. The framework distinguishes itself through its hierarchical management capabilities, which utilize manager agents to coo

ChatDev is an automated software engineering platform that orchestrates the end-to-end development lifecycle through a multi-agent framework. It functions as a programmable engine that coordinates specialized autonomous agents to handle design, coding, testing, and documentation tasks by transitioning through predefined phases of a software project. The system distinguishes itself by using role-based agent specialization to simulate a professional engineering team, assigning distinct personas and knowledge bases to individual agents. It employs prompt-driven task decomposition to break high-l

AgentScope is a multi-agent framework and orchestration platform designed for building and coordinating teams of language model agents. It provides a system for managing multiple agents that collaborate to solve complex tasks through structured communication and state sharing. The project distinguishes itself with a focus on production-ready deployment and security, featuring a multi-tenant hosting service that ensures session isolation between different users. It includes a sandboxed tool execution environment and fine-grained permission controls to manage how agents access system resources

This project is a reinforcement learning toolkit and simulation-based AI trainer for creating intelligent agents within Unity simulations. It provides a multi-agent simulation framework for configuring cooperative or competitive scenarios and includes an environment wrapper that bridges simulations with standard machine learning libraries using gym-style interfaces. The system features a native cross-platform inference engine that executes trained neural network models for real-time decision making without external dependencies. It enables the acceleration of the learning process by running m

Qwen-Agent is a development framework for building autonomous software applications that leverage large language models to plan, reason, and execute complex tasks. It functions as an orchestration engine that enables models to interact with external APIs, manage persistent memory, and maintain context across multi-step workflows. The framework distinguishes itself through a multi-agent collaboration platform that allows independent agent instances to exchange structured messages and delegate sub-tasks to one another. By utilizing iterative reasoning loops and dynamic prompt injection, the sys

Superset is an agentic development environment designed to orchestrate autonomous AI coding agents. It functions as a workspace where multiple command-line based agents can run in parallel, utilizing a persistent terminal multiplexer to maintain long-lived shell sessions and state. The project distinguishes itself through the use of Git worktrees to provide physical directory isolation for each task, preventing merge conflicts during concurrent agent operations. It incorporates a Model Context Protocol client to extend agent capabilities via external tools and data, while keeping execution en

adk-go is an agent orchestration engine and multi-agent framework for building, coordinating, and scaling systems of large language model agents. It provides a tool integration kit to connect agents with external APIs, custom functions, and diverse data sources. The project utilizes graph-based workflow orchestration to blend deterministic logic with adaptive reasoning. It supports modular multi-agent composition, allowing specialized agents to be organized into hierarchical structures to manage complex tasks through coordinated workflows. The framework includes tools for performance evaluat

ROMA este un motor de flux de lucru agentic și un orchestrator recursiv de sarcini conceput pentru a coordona agenți autonomi în execuția fluxurilor de lucru complexe. Acesta funcționează ca un framework multi-agent care descompune obiectivele de nivel înalt în sub-sarcini atomice și gestionează execuția acestora printr-un graf de dependențe. Sistemul se distinge printr-o buclă ierarhică de planificare-execuție care descompune recursiv obiectivele și sintetizează rezultatele de la sarcinile de tip „frunză” în sus. Asigură puritatea execuției prin izolarea sarcinilor atomice, alocând directoare de stocare dedicate sarcinilor individuale pentru a preveni interferența datelor. Platforma acoperă domenii largi de capabilități, inclusiv observabilitatea agenților, care urmărește urmele de execuție și metricile de performanță, și execuția augmentată cu instrumente folosind cod sandbox și seturi de instrumente externe. De asemenea, oferă o interfață programatică pentru orchestrarea gestionată de server și persistența stării prin logare bazată pe puncte de control (checkpointing).

Malmo este o platformă de simulare bazată pe voxeli, concepută pentru cercetarea în domeniul inteligenței artificiale și studiul comportamentelor agenților autonomi. Construit ca un mediu sandbox folosind Minecraft, acesta servește drept framework pentru simularea multi-agent și cercetarea în învățarea prin consolidare (reinforcement learning) într-o grilă 3D de blocuri. Proiectul se distinge printr-un framework de simulare multi-agent care coordonează și sincronizează mai mulți agenți autonomi pentru a îndeplini misiuni colaborative. Oferă o interfață standardizată care respectă specificațiile de învățare prin consolidare, permițându-i să funcționeze ca un mediu pentru antrenarea agenților prin încercare și eroare. Platforma acoperă o gamă largă de capabilități, inclusiv generarea de medii de cercetare cu definiții de sarcini reproductibile și integrarea backend-urilor de joc externe. Suportă agenți scriși în mai multe limbaje de programare printr-un strat de comunicare agnostică față de limbaj și oferă instrumente pentru vizualizarea stării la distanță a simulărilor. Motorul de simulare și dependențele serverului sunt furnizate ca implementări containerizate pentru a asigura o instalare consistentă pe diferite sisteme.