Open Source Windows Multi-Agent Frameworks
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Multi-Agent Frameworks for Windows
View 22 business solutionsBrowse free open source Multi-Agent Frameworks and projects for Windows below. Use the toggles on the left to filter open source Multi-Agent Frameworks by OS, license, language, programming language, and project status.
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1
Open Autonomy
A framework for the creation of autonomous agent services
Open Autonomy is a framework that enables the development of autonomous economic agents (AEAs) capable of operating independently in various economic contexts. -
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MetaGPT
The Multi-Agent Framework
The Multi-Agent Framework: Given one line Requirement, return PRD, Design, Tasks, Repo. Assign different roles to GPTs to form a collaborative software entity for complex tasks. MetaGPT takes a one-line requirement as input and outputs user stories / competitive analysis/requirements/data structures / APIs / documents, etc. Internally, MetaGPT includes product managers/architects/project managers/engineers. It provides the entire process of a software company along with carefully orchestrated SOPs. -
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Open AEA Framework
A framework for open autonomous economic agent (AEA) development
open-aea is an open-source framework for building autonomous software agents that can operate and interact independently on decentralized networks. Developed by Valory, it facilitates creating agents capable of economic transactions, communication, and smart contract interactions in Web3 ecosystems. -
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AgentUniverse
agentUniverse is a LLM multi-agent framework
AgentUniverse is a multi-agent AI framework that enables coordination between multiple intelligent agents for complex task execution and automation. -
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Open Multi-Agent
One runTeam() call from goal to result
Open Multi-Agent is a flexible framework designed to enable the creation and coordination of multiple AI agents working together to solve complex tasks through collaboration. It focuses on distributing responsibilities across specialized agents, each handling a specific part of a problem, such as planning, execution, or validation. The system emphasizes modularity, allowing developers to define agent roles, communication protocols, and workflows. It supports iterative collaboration, where agents exchange information and refine outputs collectively. The architecture is designed to be extensible, enabling integration with external tools and APIs to expand agent capabilities. It is particularly useful for research, automation, and development workflows that require multiple perspectives or stages of processing. Overall, open-multi-agent provides a foundation for building scalable and cooperative AI systems. -
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AgentForge
Extensible AGI Framework
AgentForge is a framework for creating and deploying AI agents that can perform autonomous decision-making and task execution. It enables developers to define agent behaviors, train models, and integrate AI-powered automation into various applications. -
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AG2
Framework for building and orchestrating multi-agent AI systems
AG2 is an open source framework designed to support the creation and coordination of multiple AI agents working together to solve complex tasks. It provides abstractions that allow developers to define agents with distinct roles, responsibilities, and communication patterns, enabling collaborative problem-solving workflows. AG2 focuses on making multi-agent systems more accessible by simplifying how agents are configured, connected, and executed. It includes mechanisms for agent-to-agent interaction, task delegation, and iterative reasoning, which are essential for building advanced AI-driven applications. AG2 is intended for developers experimenting with autonomous systems, research prototypes, or production-grade agent pipelines. AG2 emphasizes flexibility, allowing users to integrate different models and customize behaviors depending on their use case. Overall, it serves as a foundation for building scalable and modular AI agent ecosystems. -
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CAMEL AI
Finding the Scaling Law of Agents. A multi-agent framework
The rapid advancement of conversational and chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents and provide insight into their "cognitive" processes. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named role-playing. Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of chat agents, providing a valuable resource for investigating conversational language models. -
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OpenAI Agents SDK
A lightweight, powerful framework for multi-agent workflows
The OpenAI Agents Python SDK is a powerful yet lightweight framework for developing multi-agent workflows. This framework enables developers to create and manage agents that can coordinate tasks autonomously, using a set of instructions, tools, guardrails, and handoffs. The SDK allows users to configure workflows in which agents can pass control to other agents as necessary, ensuring dynamic task management. It also includes a built-in tracing system for tracking, debugging, and optimizing agent activities. -
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10
SwarmZero
SwarmZero's SDK for building AI agents, swarms of agents and much more
SwarmZero is an open-source platform designed for deploying and managing autonomous robot swarms. It enables collective coordination, decentralized decision-making, and real-time collaboration among large groups of autonomous agents, focusing on multi-robot systems and research in swarm robotics. -
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VectorizedMultiAgentSimulator (VMAS)
VMAS is a vectorized differentiable simulator
VectorizedMultiAgentSimulator is a high-performance, vectorized simulator for multi-agent systems, focusing on large-scale agent interactions in shared environments. It is designed for research in multi-agent reinforcement learning, robotics, and autonomous systems where thousands of agents need to be simulated efficiently. -
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AEA Framework
A framework for autonomous economic agent (AEA) development
agents-aea by Fetch.ai is a framework for building autonomous economic agents (AEAs) that can act independently, communicate, and transact on decentralized networks. It focuses on enabling AI-driven agents to participate in digital marketplaces and ecosystems. -
13
BotSharp
AI Multi-Agent Framework in .NET
Conversation as a platform (CaaP) is the future, so it's perfect that we're already offering the whole toolkits to our .NET developers using the BotSharp AI BOT Platform Builder to build a CaaP. It opens up as much learning power as possible for your own robots and precisely control every step of the AI processing pipeline. BotSharp is an open source machine learning framework for AI Bot platform builder. This project involves natural language understanding, computer vision and audio processing technologies, and aims to promote the development and application of intelligent robot assistants in information systems. Out-of-the-box machine learning algorithms allow ordinary programmers to develop artificial intelligence applications faster and easier. It's written in C# running on .Net Core that is full cross-platform framework. C# is a enterprise-grade programming language which is widely used to code business logic in information management-related system. -
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DevOpsGPT
Multi agent system for AI-driven software development
Welcome to the AI Driven Software Development Automation Solution, abbreviated as DevOpsGPT. We combine LLM (Large Language Model) with DevOps tools to convert natural language requirements into working software. This innovative feature greatly improves development efficiency, shortens development cycles, and reduces communication costs, resulting in higher-quality software delivery. The automated software development process significantly reduces delivery time, accelerating software deployment and iterations. By accurately understanding user requirements, DevOpsGPT minimizes the risk of communication errors and misunderstandings, enhancing collaboration efficiency between development and business teams. DevOpsGPT generates code and performs validation, ensuring the quality and reliability of the delivered software. -
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KaibanJS
JS-native framework for building and managing multi-agent systems
JavaScript-native framework for building multi-agent AI systems. Multi-agent AI systems promise to revolutionize how we build interactive and intelligent applications. However, most AI frameworks cater to Python, leaving JavaScript developers at a disadvantage. KaibanJS fills this void by providing a first-of-its-kind, JavaScript-native framework designed specifically for building and integrating AI Agents. Harness the power of specialization by configuring AI agents to excel in distinct, critical functions within your projects. This approach enhances the effectiveness and efficiency of each task, moving beyond the limitations of generic AI. Just as professionals use specific tools to excel in their tasks, enable your AI agents to utilize tools like search engines, calculators, and more to perform specialized tasks with greater precision and efficiency. -
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Zeta
Build high-performance AI models with modular building blocks
zeta is a deep learning library focused on providing cutting-edge AI and neural network models with a strong emphasis on research-grade architectures. It includes state-of-the-art implementations for rapid experimentation and model building. -
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uAgents
A fast and lightweight framework for creating decentralized agents
uAgents is a library developed by Fetch.ai that allows for creating autonomous AI agents in Python. With simple and expressive decorators, you can have an agent that performs various tasks on a schedule or takes action on various events. -
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Jason is a fully-fledged interpreter for an extended version of AgentSpeak, a BDI agent-oriented logic programming language, and is implemented in Java. Using JADE a multi-agent system can be distributed over a network effortlessly. This project was moved to https://jason-lang.github.io
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masmt
A frame work for Multi agent system development
MaSMT is a java based multi-agent system development framework, especially designed for development of English to Sinhala machine translation system. MaSMT also capable to develop any multi-agent based system through its architecture. Reference: B. Hettige, A. S. Karunananda, G. Rzevski, Multi-agent solution for managing complexity in English to Sinhala Machine Translation, International Journal of Design & Nature and Ecodynamics, Volume 11, Issue 2, 2016, 88 – 96. B. Hettige, A. S. Karunananda, G. Rzevski, ” MaSMT: A Multi-agent System Development Framework for English-Sinhala Machine Translation”, International Journal of Computational Linguistics and Natural Language Processing (IJCLNLP), Volume 2 Issue 7 July 2013. -
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cordum
Enterprise AI Agent Orchestration & Governance Platform.
Cordum is the infrastructure layer for the Agentic Era. Unlike standard "agent builders," Cordum is an enterprise-grade platform designed to run, manage, and govern AI agents in production at scale. At its core lies the Cordum Agent Protocol (CAP) a high-performance, open standard (NATS/Redis) that decouples agent logic from control. This architecture ensures "Zero-Copy" security (keeping PII off the wire) and provides a centralized Safety Kernel to intercept hallucinations and unauthorized actions before execution. Key Features: Protocol-First: Language-agnostic orchestration (Python, Go, Node, Rust). Safety Kernel: Deterministic guardrails enforced at the infrastructure level. Human-in-the-Loop: Native approval workflows for critical agent actions. Observability: Real-time tracing of agent thoughts, decisions, and tool usage. Stop building fragile scripts. Start engineering governed agent fleets with Cordum. -
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NodeTool
Visual AI Workflow Builder
NodeTool is an open‑source, visual AI workflow builder that lets you connect nodes for text, images, audio, video, data, and automation—then run them locally or on the cloud. Build multi‑step agents, RAG systems, and creative media pipelines without coding, inspect execution in real time, and deploy anywhere: home server, private VPC, RunPod, or Cloud Run. With a local‑first design, NodeTool keeps models and data under your control while still supporting providers like OpenAI, Anthropic, Replicate, and HuggingFace. Use templates to get started fast, customize every step, and share workflows as simple apps across desktop and mobile via secure connections. -
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SpiLLI
Decentralized AI Inference
SpiLLI provides infrastructure to manage, host, deploy and run Decentralized AI inference -
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AgentVerse
Designed to facilitate the deployment of multiple LLM-based agents
AgentVerse is designed to facilitate the deployment of multiple LLM-based agents in various applications, which primarily provides two frameworks: task-solving and simulation. -
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BindAI
Open-source Python framework for AI agents and agentic workflows.
BindAI is an open-source, opinionated Python framework for building, composing, and running AI agents and agentic workflows. It provides a structured application architecture for tools, memory, knowledge and RAG, model providers, integrations, APIs, and project-based organization. BindAI is designed for Python developers building AI applications, agent systems, and multi-step workflows. -
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Blackboard implements a lightweight, portable tuple space suitable for multi-agent system and distributed component design. Supports implicit invocation via content-filtered asynchronous events, blocking call semantics, and shared memory messaging.
