Open Source Windows Agent Harnesses
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Agent Harnesses for Windows
View 23 business solutionsBrowse free open source Agent Harnesses and projects for Windows below. Use the toggles on the left to filter open source Agent Harnesses by OS, license, language, programming language, and project status.
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Voyager
The open harness for creative work
Voyager is an AI-powered creative automation platform that enables users to generate, edit, and produce visual, video, audio, and design content using AI agents. The software connects AI models with professional creative applications, including Adobe Photoshop, Illustrator, Premiere Pro, After Effects, DaVinci Resolve, Blender, and Ableton Live. Users can describe creative tasks in natural language, allowing AI agents to select tools, generate assets, edit content, and complete multistep production workflows. Voyager includes built-in Design, Video, and digital audio workstation (DAW) engines for creating graphics, editing videos, producing motion graphics, and composing music without requiring third-party creative software. The platform supports multiple AI models, including Claude, ChatGPT, DeepSeek, and specialized image, video, voice, and music generation models, with customizable agent skills and Model Context Protocol (MCP) integrations. -
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DeepSeek Harness
DeepSeek Harness: Everything is a Plugin
DeepSeek Harness is an open-source agent harness built around the idea that every major capability should be replaceable as a plugin. It uses Cordis to compose model adapters, tools, session handling, agent loops, persistence, sandboxing, approvals, and interface layers into configurable profiles. Users can run a browser-based interface, select a workspace, configure DeepSeek or other compatible model endpoints, and launch agent sessions against local projects. Agents can read and edit files, execute commands, delegate work, and maintain plans. Durable session events make work replayable and support persistence, forking, and resuming. The project is currently a fast-moving developer preview, so compatibility-breaking changes are expected. -
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ZCode
Z.ai's coding agent harness. Powerful, intelligent, extensible
ZCode is an open-source AI coding workspace that combines desktop, browser, and terminal interfaces around a shared agent runtime. The repository includes an Electron desktop app, web client, backend services, reusable UI packages, and the ZCode Agent CLI. Its terminal interface also serves as the agent runtime used by the other clients. Shared protocols, RPC components, provider integrations, persistence services, and client SDKs keep the different interfaces synchronized. Developers can run local desktop workspaces or prepare assets for remote workspace scenarios. The project uses a pnpm monorepo with React-based UI components and centralized build tooling. It is designed as a complete coding-agent environment rather than a standalone command-line assistant. -
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Pi Agent
AI agent toolkit: coding agent CLI, unified LLM API, TUI & web UI
Pi is an open-source AI agent toolkit and coding agent framework designed to help developers build, run, and extend intelligent AI-powered workflows. Developed by Earendil Works, the project includes a coding agent CLI, unified multi-provider LLM API, agent runtime, terminal UI library, and web UI components. Pi supports multiple AI providers such as OpenAI, Anthropic, and Google through a single consistent API layer, making it easier to integrate different models into applications and agents. The framework is built around extensibility, allowing developers to create custom tools, workflows, and autonomous coding experiences. Pi also encourages the sharing of real-world AI coding sessions to improve agent performance through practical usage data instead of synthetic benchmarks. With its modular architecture, active open-source community, and support for advanced agent capabilities, Pi provides a comprehensive foundation for building next-generation AI development tools and autonomous c -
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LobeHub
Workspace to find, build, and collaborate with AI agents
LobeHub is an all-in-one workspace designed to help humans and AI agents collaborate, grow, and evolve together. It treats AI agents as true teammates rather than one-off tools, enabling deeper context, continuity, and productivity. Users can build personalized agent teams that understand their workflows, preferences, and goals over time. LobeHub brings multiple models, tools, and modalities into a single unified environment under the user’s control. With built-in collaboration features, agents can work in parallel, share context, and support complex projects seamlessly. The platform is built around the idea of co-evolution, where both humans and agents continuously learn and improve together. -
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OpenClaw
Your own personal AI assistant. Any OS. Any Platform.
OpenClaw (formerly Clawdbot/Moltbot) is an open-source, self-hosted autonomous AI assistant designed to run on user-controlled hardware and bridge conversational natural language with real-world task execution, effectively acting as a proactive digital assistant rather than a reactive chatbot. It lets you send instructions through familiar messaging platforms like WhatsApp, Telegram, Discord, Slack, Signal, iMessage, and more, and then interprets those instructions to carry out actions such as managing calendars, sending emails or messages, browsing the web, executing system commands, and coordinating workflows across services — all while maintaining long-term memory and context across sessions. Because it runs locally or on infrastructure you choose (like a personal computer, VPS, or Raspberry Pi), OpenClaw emphasizes data ownership, privacy, and full transparency into how your instructions are handled and what actions are taken, giving users autonomy over their AI workflows. -
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DeepSeek Harness Tauri
Better DSH desktop, zero environment setup, preset plugins
DeepSeek Harness Desktop is a free, open-source desktop application for running and managing the DeepSeek Harness AI agent environment. Built with Tauri and React, it provides a graphical interface without requiring manual Node.js, pnpm, or Docker setup. The application manages runtime dependencies, engine versions, and software updates. Its plugin system includes built-in extensions for scheduled tasks, model configuration, session archiving, and Git worktree management. Users can connect mobile devices through local networks or Cloudflare tunnels to access desktop sessions remotely. It also provides command-line integration, customizable themes, and animated desktop companions that display session activity. Available for Windows, macOS, and Linux, it simplifies extensible AI agent workflows. -
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Caprock
Free desktop app for Claude Code, Codex and OpenCode
Caprock is a free, open-source desktop app for AI coding agents, for macOS, Windows and Linux. It puts Claude Code, Codex and OpenCode sessions — including the ones you start in your own terminal — and the Gemini CLI sessions it starts into one window: terminals and projects in tabs and split panes, with activity, cost, plan limits and loop alerts for every session. Cost is per repository and per model, priced at API list prices from the tokens the agents already record, next to the 5-hour and weekly plan-limit windows. Everything the agent wrote is searchable across sessions and kept after Claude Code's 30-day transcript cleanup. A phone can carry on the work. It works with the subscription you already have. A daemon on loopback records sessions into a local SQLite database: no account, no hosted service, no telemetry. Free under Apache-2.0; an optional Premium licence adds paid features on top. -
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Raven
Self-evolving multi-agent ecosystem for all-domain collaboration
Raven is a pre-alpha multi-agent orchestration system that coordinates specialized agents through one persistent workspace. It ships with built-in agents for research, coding, visual design, and supervision of long-running jobs. Raven can also orchestrate third-party agents such as Claude Code, Codex, OpenCode, GitHub Copilot, Pi, and others. Its runtime is powered by EverOS, allowing user context, agent experience, and reusable knowledge to persist across sessions. The system can generate task graphs, delegate work, combine results, and evolve tools, skills, and workflows over time. Raven supports a terminal interface, web UI, plugins, provider routing, and self-hosted deployment. It is designed for complex, long-horizon work that benefits from collaboration among agents with different strengths. -
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Oh My OpenAgent
The best agent harness
Oh My OpenAgent is a large-scale, open-source agent orchestration framework that aims to provide a fully unified and extensible environment for AI-powered software development and automation. It builds on the idea that no single model is sufficient, instead enabling coordinated use of multiple models for reasoning, creativity, speed, and cost efficiency within a single workflow. The system is designed as a comprehensive agent harness where tasks are automatically decomposed, delegated, and executed across a network of specialized agents. It emphasizes openness and flexibility, allowing developers to integrate different providers and avoid dependency on any single ecosystem or vendor. The framework includes robust tooling for managing agent workflows, monitoring execution, and integrating external tools, making it suitable for complex, production-level use cases. It also fosters a strong community-driven development approach, with features evolving in real time. -
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Claw Code
AI agent harness for AI coding agents
Claw Code is an open-source AI agent harness project focused on building better tools for orchestrating and managing autonomous coding agents. It originated as a clean-room reimplementation inspired by the architecture of Claude Code, aiming to replicate core concepts without using proprietary code. The project provides a Python-based foundation for experimenting with agent workflows, tool integration, and task execution pipelines. It emphasizes harness engineering—how agents are structured, how they interact with tools, and how they maintain context during execution. The system is being actively expanded, with a Rust-based runtime in development to improve performance and memory safety. Overall, Claw Code serves as a research-driven platform for advancing agent-based software development systems. -
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oh-my-agent
Portable multi-agent harness for .agents-based skills, workflows
oh-my-agent is a flexible and extensible framework designed to simplify the creation, management, and orchestration of AI agents across various tasks and environments. It builds on the idea of modular agent systems, allowing developers to define specialized roles and capabilities that can be combined into larger workflows. The framework emphasizes usability, making it easier to configure agents, assign tasks, and manage interactions without requiring deep expertise in AI system design. It likely includes support for plugins or skills, enabling agents to extend their functionality through integrations with external tools. The system also focuses on coordination, allowing multiple agents to collaborate on complex tasks in a structured manner. Its architecture supports experimentation, making it suitable for both prototyping and iterative development. Overall, oh-my-agent provides a practical foundation for building and managing multi-agent systems. -
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Deep Agents
The batteries-included agent harness
Deep Agents is an open-source, batteries-included agent harness designed for long-running, multi-step AI work. It provides an opinionated agent setup while allowing developers to override or replace individual pieces without forking the project. The framework is model-agnostic and works with tool-calling models from hosted providers, open-weight deployments, or local runtimes. Built on LangGraph, it includes persistence, checkpointing, streaming, and production-oriented orchestration. Agents can delegate to sub-agents, work with files, run shell commands, manage long contexts, and retain memory across sessions. It also supports human approval of tool calls, reusable skills, custom tools, and MCP servers. -
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Desloppify
Agent harness to make your slop code well-engineered and beautiful
Desloppify is a utility-focused project aimed at improving the quality, structure, and clarity of generated or written text by removing redundancy, noise, and unnecessary verbosity. It is designed to “clean up” outputs, particularly those produced by AI systems, making them more concise, readable, and professional. The system likely applies heuristics or transformation rules to identify repetitive patterns, filler content, and stylistic inconsistencies. This makes it especially useful in workflows where AI-generated text needs to be refined before publication or use in production. It may also support integration into pipelines, allowing automatic post-processing of outputs. The project reflects a growing need to manage and optimize AI-generated content rather than simply produce it. Overall, desloppify acts as a refinement layer that enhances clarity and usability of textual outputs. -
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Calabrass
Calabrass Open-source desktop personal AI agent for local workflows
Calabrass: Open Source Personal AI Agent Calabrass is a free, open-source personal AI assistant built to run locally on your Windows desktop. Chat with AI models, organize files in your local folders, and connect external tools via the Model Context Protocol (MCP) 🔑 Key Features 🔒 100% Private & Local: Runs entirely on your PC. No cloud account required; your data never leaves your device. 📂 Local Workspace Access: Let your agent inspect, read, and manage files directly inside your local folders. 🔌 MCP Integration: Connect external tools, custom functions, and automated workflows using MCP. 🤖 Multi-Model Support: Modern UI designed to interact with flexible AI models for coding and research. ⚡ Easy Setup: Lightweight 64-bit Windows installer with no admin rights needed. 📄 License: MIT 🌐 Official Site: https://calabrass.vercel.app/ 💻 GitHub: https://github.com/CodewithMubasher/Calabrass -
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Null-CLi
Secure The Unknown
NullSquare is an AI-powered security platform that can do both penetration testing and compliance auditing. -
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Egregor
Egregor — Local-first desktop Multi-AI Consilium
Egregor — это локальное десктопное приложение, которое объединяет несколько моделей ИИ (Claude, GPT, Gemini, DeepSeek и 300+ через OpenRouter) в структурированный «консилиум», где они обсуждают и сверяют ответы друг друга. Включает защиту от группового мышления, карту доверия и конвейер аудита смарт-контрактов. Все данные проекта остаются на собственном компьютере пользователя -
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Excalibur
Excalibur is a highly opinionated agent harness
Excalibur is an experimental or utility-oriented project that appears to focus on enabling structured execution, control, or enhancement of workflows within AI or development environments. The system likely provides tools for managing tasks, orchestrating processes, or enhancing decision-making capabilities in automated systems. Its design suggests an emphasis on control and precision, allowing users to define how tasks are executed and monitored. It may include abstractions for handling inputs, outputs, and intermediate steps, enabling more predictable behavior in complex workflows. The architecture is likely modular, supporting customization and extension for different use cases. This makes it suitable for experimentation as well as integration into larger systems. Overall, excalibur represents a flexible tool for managing structured processes in AI-driven or automated environments. -
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GELab-Zero
GUI Exploration Lab. One of the best GUI agent solutions
GELab-Zero is an open-source “GUI Agent” framework aiming to automate interactions with graphical user interfaces (GUIs), combining both the agent model and all supporting infrastructure — including inference, input orchestration, and GUI automation logic — in a plug-and-play package that runs locally, without cloud dependencies. The idea is to let developers or users harness an AI agent that can simulate clicking, typing, reading UI elements, and interacting with apps in a human-like way via the GUI, which can enable tasks like automated testing, scriptable workflows, or even autonomous usage of GUI-based applications. Because GELab-Zero is fully open-source and doesn’t require external services, it offers privacy and control: everything runs locally under your control. The project provides a lightweight base model (4B parameters in its public release) that can run on modest hardware (depending on quantization), making it more accessible than many large-scale AI solutions. -
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Griptape
Python framework for AI workflows and pipelines with chain of thought
The Griptape framework provides developers with the ability to create AI systems that operate across two dimensions: predictability and creativity. For predictability, Griptape enforces structures like sequential pipelines, DAG-based workflows, and long-term memory. To facilitate creativity, Griptape safely prompts LLMs with tools (keeping output data off prompt by using short-term memory), which connects them to external APIs and data stores. The framework allows developers to transition between those two dimensions effortlessly based on their use case. Griptape not only helps developers harness the potential of LLMs but also enforces trust boundaries, schema validation, and tool activity-level permissions. By doing so, Griptape maximizes LLMs’ reasoning while adhering to strict policies regarding their capabilities. -
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Harness-1
Ultra Recipe for Training Long-Horizon Search Agents
Harness-1 is a 20B search agent trained with reinforcement learning inside a stateful retrieval harness. It is designed for long-horizon search tasks where the model must search, inspect documents, curate evidence, verify claims, and decide when enough evidence has been gathered. The harness externalizes search state, including candidate documents, evidence links, verification records, and budget-aware context. This lets the policy focus on higher-level decisions instead of trying to keep every detail inside the model context. The repository includes inference utilities, training scripts, evaluation runners, dataset tools, and documentation for running the released checkpoint. Its main value is showing how a smaller open model can approach advanced search-agent behavior through structured retrieval state and reinforcement learning. -
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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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Langroid
Harness LLMs with Multi-Agent Programming
Given the remarkable abilities of recent Large Language Models (LLMs), there is an unprecedented opportunity to build intelligent applications powered by this transformative technology. The top question for any enterprise is: how best to harness the power of LLMs for complex applications? For technical and practical reasons, building LLM-powered applications is not as simple as throwing a task at an LLM system and expecting it to do it. Effectively leveraging LLMs at scale requires a principled programming framework. In particular, there is often a need to maintain multiple LLM conversations, each instructed in different ways, and "responsible" for different aspects of a task. -
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LazyCodex
The one and only agent harness for complex codebases
LazyCodex is an agent harness for using Codex on complex software projects. It is designed to add structure around AI coding sessions through memory, planning, execution, verification, skills, hooks, routing, and diagnostics. The project helps developers move beyond one-off prompts by giving the agent a more organized workflow inside a codebase. It supports project memory so context can persist across sessions and decisions do not need to be repeatedly reintroduced. LazyCodex also emphasizes verified completion, which means the workflow is built around checking whether tasks are actually finished rather than only generating code. Its main value is turning Codex into a more disciplined coding agent environment for larger and more demanding repositories. -
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OSS-Fuzz Gen
LLM powered fuzzing via OSS-Fuzz
OSS-Fuzz-Gen is a companion project that helps automatically create or improve fuzz targets for open-source codebases, aiming to increase coverage in OSS-Fuzz with minimal maintainer effort. It analyses a library’s APIs, examples, and tests to propose harnesses that exercise parsers, decoders, or protocol handlers—precisely the code where fuzzing pays off. The system integrates with modern LLM-assisted workflows to draft harness code and then iterates based on build errors or low coverage signals. Importantly, it aligns with OSS-Fuzz conventions, generating corpus seeds, build rules, and sanitizer settings so projects can plug in quickly. Reports highlight what functions were targeted, how coverage evolved, and where manual hints could unlock more paths. The goal is pragmatic: shrink the gap between “we should fuzz this” and “we have robust fuzzing running in CI,” especially for understaffed maintainers.
