Open Source Windows AI Agent Frameworks
Sort By:
AI Agent Frameworks for Windows
View 54 business solutionsBrowse free open source AI Agent Frameworks and projects for Windows below. Use the toggles on the left to filter open source AI Agent Frameworks by OS, license, language, programming language, and project status.
-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
-
1
n8n
Free and source-available fair-code licensed workflow automation tool
n8n is an extendable workflow automation tool. With a fair-code distribution model, n8n will always have visible source code, be available to self-host, and allow you to add your own custom functions, logic and apps. n8n's node-based approach makes it highly versatile, enabling you to connect anything to everything. n8n has 200+ different nodes to automate workflows. -
2
DeerFlow
Deep Research framework, combining language models with tools
DeerFlow is an open-source, community-driven “deep research” framework / multi-agent orchestration platform developed by ByteDance. It aims to combine the reasoning power of large language models (LLMs) with automated tool-use — such as web search, web crawling, Python execution, and data processing — to enable complex, end-to-end research workflows. Instead of a monolithic AI assistant, DeerFlow defines multiple specialized agents (e.g. “planner,” “searcher,” “coder,” “report generator”) that collaborate in a structured workflow, allowing tasks like literature reviews, data gathering, data analysis, code execution, and final report generation to be largely automated. It supports asynchronous task coordination, modular tool integration, and orchestrates the data flow between agents — making it suitable for large-scale or multi-stage research pipelines. Users can deploy it locally or on server infrastructure, integrate custom tools, and benefit from its flexible configuration. -
3
AnythingLLM
The all-in-one Desktop & Docker AI application with full RAG and AI
A full-stack application that enables you to turn any document, resource, or piece of content into a context that any LLM can use as references during chatting. This application allows you to pick and choose which LLM or Vector Database you want to use as well as supporting multi-user management and permissions. AnythingLLM is a full-stack application where you can use commercial off-the-shelf LLMs or popular open-source LLMs and vectorDB solutions to build a private ChatGPT with no compromises that you can run locally as well as host remotely and be able to chat intelligently with any documents you provide it. AnythingLLM divides your documents into objects called workspaces. A Workspace functions a lot like a thread, but with the addition of containerization of your documents. Workspaces can share documents, but they do not talk to each other so you can keep your context for each workspace clean. -
4
UI-TARS Desktop
A GUI Agent app based on UI-TARS to control your computer using AI
UI-TARS Desktop is a graphical user interface (GUI) agent application that leverages the UI-TARS vision-language model to enable natural language control of computers. This cross-platform tool supports both Windows and macOS, allowing users to perform tasks through intuitive commands. Key features include screenshot-based visual recognition, precise mouse and keyboard control, and real-time feedback on actions. Provides immediate responses and visual feedback on actions performed. The application facilitates seamless interaction with the computer, enhancing user experience by simplifying complex operations into straightforward language instructions. Leverages advanced AI to bridge the gap between visual elements and language commands. UI-TARS Desktop is open-source and licensed under the Apache License 2.0. -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
-
5
Flowise
Drag & drop UI to build your customized LLM flow
Open source UI visual tool to build your customized LLM flow using LangchainJS, written in Node Typescript/Javascript. Conversational agent for a chat model which utilizes chat-specific prompts and buffer memory. Open source is the core of Flowise, and it will always be free for commercial and personal usage. Flowise support different environment variables to configure your instance. You can specify the following variables in the .env file inside the packages/server folder. -
6
OpenManus
Open-source AI agent framework
OpenManus is an open-source AI agent framework designed to autonomously execute complex, multi-step tasks by combining reasoning, planning, and tool use. It enables developers to build agents that can think, act, and iterate toward goals rather than simply responding to prompts. The platform emphasizes task decomposition, allowing agents to break down objectives into smaller steps and execute them sequentially or recursively. OpenManus supports integration with external tools, APIs, and environments, making it suitable for real-world automation workflows. It is built to be flexible and extensible, enabling customization of agent behaviors, tools, and reasoning strategies. Overall, OpenManus provides a foundation for creating more capable, autonomous AI systems that can handle dynamic and goal-driven tasks. -
7
LangGraph Studio
Desktop app for prototyping and debugging LangGraph applications
LangGraph Studio offers a new way to develop LLM applications by providing a specialized agent IDE that enables visualization, interaction, and debugging of complex agentic applications. With visual graphs and the ability to edit state, you can better understand agent workflows and iterate faster. LangGraph Studio integrates with LangSmith so you can collaborate with teammates to debug failure modes. While in Beta, LangGraph Studio is available for free to all LangSmith users on any plan tier. LangGraph Studio requires docker-compose version 2.22.0+ or higher. Please make sure you have Docker installed and running before continuing. When you open LangGraph Studio desktop app for the first time, you need to login via LangSmith. Once you have successfully authenticated, you can choose the LangGraph application folder to use, you can either drag and drop or manually select it in the file picker. -
8
Goose
AI coding agent that's more than suggestions - install, execute, edit+
Goose is an open-source, extensible AI agent that enhances the software development process by going beyond traditional code suggestions. It allows developers to install, execute, edit, and test code with any large language model (LLM), facilitating a more efficient and streamlined workflow. Designed to operate locally within a developer's environment, Goose integrates seamlessly with various tools and platforms, providing a customizable and powerful assistant for coding tasks. Its architecture supports extensibility, enabling users to tailor the agent to their specific needs and preferences. By leveraging Goose, developers can improve productivity and code quality through advanced AI-driven assistance. -
9
Agent Zero
Agent Zero AI framework
Agent Zero is not a predefined agentic framework. It is designed to be dynamic, organically growing, and learning as you use it. Agent Zero is fully transparent, readable, comprehensible, customizable and interactive. Agent Zero uses the computer as a tool to accomplish its (your) tasks. Agents can communicate with their superiors and subordinates, asking questions, giving instructions, and providing guidance. Instruct your agents in the system prompt on how to communicate effectively. The terminal interface is real-time streamed and interactive. You can stop and intervene at any point. If you see your agent heading in the wrong direction, just stop and tell it right away. There is a lot of freedom in this framework. You can instruct your agents to regularly report back to superiors asking for permission to continue. You can instruct them to use point-scoring systems when deciding when to delegate subtasks. Superiors can double-check subordinates' results and disputes. -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
-
10
AutoGPT
Powerful tool that lets you create and run intelligent agents
AutoGPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set. As one of the first examples of GPT-4 running fully autonomously, AutoGPT pushes the boundaries of what is possible with AI. -
11
Agentic
AI agent stdlib that works with any LLM and TypeScript AI SDK
Agentic is an open source, TypeScript, AI agent standard library that works with any LLM and TS AI SDK. Agentic’s standard library of TypeScript AI tools are optimized for both TS-usage as well as LLM-based usage, which is really important for testing and debugging. -
12
Dify
One API for plugins and datasets, one interface for prompt engineering
Dify is an easy-to-use LLMOps platform designed to empower more people to create sustainable, AI-native applications. With visual orchestration for various application types, Dify offers out-of-the-box, ready-to-use applications that can also serve as Backend-as-a-Service APIs. Unify your development process with one API for plugins and datasets integration, and streamline your operations using a single interface for prompt engineering, visual analytics, and continuous improvement. Out-of-the-box web sites supporting form mode and chat conversation mode A single API encompassing plugin capabilities, context enhancement, and more, saving you backend coding effort Visual data analysis, log review, and annotation for applications -
13
GaiaNet
Install and run your own AI agent service
Gaia is building an active, intelligent ecosystem that supports applications that learn, improve and grow over time. Put your knowledge to work and watch it evolve by creating a node on Gaia or by contributing to a domain supporting an existing knowledge base. Gaia’s decentralized platform ensures robust protection for user data and IP. Gaia allows secure ownership and monetization of IP without compromising privacy. Gaia’s living knowledge organisms continuously adapt and grow in real-time, keeping solutions relevant and cutting-edge. Developers can build applications that evolve and improve over time. -
14
Haystack
Haystack is an open source NLP framework to interact with your data
Apply the latest NLP technology to your own data with the use of Haystack's pipeline architecture. Implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications. Evaluate components and fine-tune models. Ask questions in natural language and find granular answers in your documents using the latest QA models with the help of Haystack pipelines. Perform semantic search and retrieve ranked documents according to meaning, not just keywords! Make use of and compare the latest pre-trained transformer-based languages models like OpenAI’s GPT-3, BERT, RoBERTa, DPR, and more. Pick any Transformer model from Hugging Face's Model Hub, experiment, find the one that works. Use Haystack NLP components on top of Elasticsearch, OpenSearch, or plain SQL. Boost search performance with Pinecone, Milvus, FAISS, or Weaviate vector databases, and dense passage retrieval. -
15
CrewAI
Framework for orchestrating role-playing, autonomous AI agents
Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. The power of AI collaboration has too much to offer. CrewAI is designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions. -
16
LangGraph
Build resilient language agents as graphs
LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features. -
17
Open-AutoGLM
An open phone agent model & framework
Open-AutoGLM is an open-source framework and model designed to empower autonomous mobile intelligent assistants by enabling AI agents to understand and interact with phone screens in a multimodal manner, blending vision and language capability to control real devices. It aims to create an “AI phone agent” that can perceive on-screen content, reason about user goals, and execute sequences of taps, swipes, and text input via automated device control interfaces like ADB, enabling hands-off completion of multi-step tasks such as navigating apps, filling forms, and more. Unlike traditional automation scripts that depend on brittle heuristics, Open-AutoGLM uses pretrained large language and vision-language models to interpret visual context and natural language instructions, giving the agent robust adaptability across apps and interfaces. -
18
Self-Operating Computer
A framework to enable multimodal models to operate a computer
The Self-Operating Computer Framework is an innovative system that enables multimodal models to autonomously operate a computer by interpreting the screen and executing mouse and keyboard actions to achieve specified objectives. This framework is compatible with various multimodal models and currently integrates with GPT-4o, o1, Gemini Pro Vision, Claude 3, and LLaVa. Notably, it was the first known project to implement a multimodal model capable of viewing and controlling a computer screen. The framework supports features like Optical Character Recognition (OCR) and Set-of-Mark (SoM) prompting to enhance visual grounding capabilities. It is designed to be compatible with macOS, Windows, and Linux (with X server installed), and is released under the MIT license. -
19
AgentField
Build and run AI agents like microservices
AgentField is an open-source control plane designed to run AI agents as production-grade backend services, applying cloud-native principles similar to Kubernetes to the world of autonomous software. Instead of treating agents as isolated scripts or prototypes, the system elevates them to first-class infrastructure components that can be deployed, orchestrated, and managed at scale across distributed environments. Developers define agents as typed functions, and the platform automatically handles orchestration, communication, identity, and execution, allowing agents to behave like APIs within a broader system architecture. The framework includes built-in support for asynchronous execution, long-running processes, and multi-agent coordination, enabling complex workflows that go far beyond simple prompt-response interactions. It also introduces strong identity and governance mechanisms, such as cryptographic identities and policy enforcement, ensuring that agents can operate securely. -
20
Alan AI
In-App assistant SDK to build a multimodal conversational UX websites
Quickly add voice to your app with the Alan Platform. Create an in-app voice assistant to enable human-like conversations and provide a personalized voice experience for every user. Alan is a conversational voice AI platform that lets you create an intelligent voice assistant for your app. It offers all the necessary tools to design, embed, and host your voice solutions. A powerful web-based IDE where you can write, test and debug dialog scenarios for your voice assistant or chatbot. Alan's AI-backend powered by the industry’s best Automatic Speech Recognition (ASR), Natural Language Understanding (NLU) and Speech Synthesis. The Alan Cloud provisions and handles the infrastructure required to maintain your voice deployments and perform all the voice processing tasks. To voice enable your app, you only need to get the Alan Client SDK and drop it to your app. No need to plan for, deploy and maintain any infrastructure or speech components - the Alan Platform does the bulk of the work. -
21
Browser Use
Make websites accessible for AI agents
Browser Use is an AI-powered browser automation framework designed to let agents interact with websites just like humans do. It enables developers and AI systems to perform complex online tasks such as form filling, data extraction, and navigation through natural language instructions. Built with Python and compatible with modern LLMs, it integrates seamlessly with tools like ChatBrowserUse, Google Gemini, and Anthropic models. The platform supports both open-source deployment and a fully hosted cloud version for enhanced scalability and performance. Its cloud offering includes advanced capabilities like stealth browsing, CAPTCHA solving, and proxy rotation for reliable automation. Overall, Browser Use transforms web interaction into an intelligent, programmable workflow driven by AI agents. -
22
IntentKit
An open and fair framework for everyone to build AI agents
IntentKit is a natural language understanding (NLU) library focused on intent recognition and entity extraction, enabling developers to build conversational AI applications. -
23
Kheish
Kheish: A multi-role LLM agent for tasks like code auditing
Kheish is a framework designed for cybersecurity professionals to automate penetration testing tasks, providing tools to streamline security assessments. -
24
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. -
25
Agent Stack
Deploy and share agents with open infrastructure
Agent Stack is an open infrastructure platform designed to take AI agents from prototype to production, no matter how they were built. It includes a runtime environment, multi-tenant web UI, catalog of agents, and deployment flow that seeks to remove vendor lock-in and provide greater autonomy. Under the hood it’s built on the “Agent2Agent” (A2A) protocol, enabling interoperability between different agent ecosystems, runtime services, and frameworks. The platform supports agents built in frameworks like LangChain, CrewAI, etc., enabling them to be hosted, managed and shared through a unified interface. It also offers multi-model, multi-provider support (OpenAI, Anthropic, Gemini, IBM WatsonX, Ollama etc.), letting users compare performance and cost across models. For developers and organizations building AI-agent products or automations, Agent Stack gives a scaffold that handles the “plumbing”, so they can focus on logic and domain.
