Open Source Windows OpenClaw Tools
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OpenClaw Tools for Windows
View 23 business solutionsBrowse free open source OpenClaw Tools and projects for Windows below. Use the toggles on the left to filter open source OpenClaw Tools by OS, license, language, programming language, and project status.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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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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OpenMontage
World's first open-source, agentic video production system
OpenMontage is an open-source, agent-driven video production system that transforms AI coding assistants into fully automated multimedia creation pipelines. Instead of focusing on a single capability such as text-to-video generation, it treats video production as a structured, multi-stage workflow that mirrors how a real production team operates, including research, scripting, asset generation, editing, and final rendering. The system orchestrates a large collection of tools and models through coordinated pipelines, enabling an AI agent to autonomously gather information, write scripts, generate visuals, synthesize voiceovers, and assemble a complete video output. One of its defining characteristics is its modular and extensible architecture, which allows users to mix and match different providers, including both cloud APIs and local models, depending on performance, cost, or privacy needs. -
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AionUi
Free, local, open-source Cowork for Gemini CLI, Claude Code, Codex
AionUi is an open-source, cross-platform graphical interface that turns command-line AI tools into a unified coworking desktop for interacting with multiple local AI agents and CLI models like Gemini CLI, Claude Code, Codex, Qwen Code, and others. Instead of forcing users to work in separate terminals for each tool, AionUi automatically detects installed CLI tools and provides a central visual workspace where sessions can run in parallel, contexts are preserved, and conversations are saved locally without sending data to external servers. It enhances productivity by offering smart file management features like batch renaming, automatic organization, and intelligent file classification, thereby reducing manual overhead when working with large datasets or complex document structures. AionUi also supports a remote WebUI mode, allowing users to access their local AI tools securely over a network from other devices while keeping all processing and data on their own hardware. -
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OpenClaw Installer
ClawdBot one-click deployment tool
OpenClaw Installer is an open-source one-click deployment and configuration tool for installing OpenClaw — a personal AI assistant — onto systems with minimal manual setup, giving users a streamlined path to get their own AI assistant running quickly. The project provides shell scripts and configuration menus that detect the host environment, install dependencies, download OpenClaw, configure core settings like AI models and identity channels, and start the server automatically. It supports multiple platforms, including macOS, Linux distributions (Ubuntu, Debian, CentOS), and Windows environments via compatible shells, and simplifies otherwise complex installation steps into a guided, terminal-based experience. The tool also includes options to test API connections, validate channel integrations like Telegram or Discord bots, and launch persistent services that keep OpenClaw running in the background. -
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OpenClaw Windows Hub
Windows companion suite for OpenClaw
OpenClaw Windows Node is a Windows companion suite for OpenClaw, the AI-powered personal assistant platform. It allows a Windows PC to act as a controllable node that can be used by the OpenClaw agent. When Node Mode is enabled, the agent can interact with the Windows machine in a way similar to the OpenClaw Mac app. The project focuses on bringing local desktop control, automation, and assistant functionality to Windows users. It is intended for people who want their AI assistant to operate across devices rather than only through chat. Its usefulness comes from connecting the broader OpenClaw ecosystem with practical Windows-based actions and workflows. -
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ClawX
Desktop app that provides a graphical interface for OpenClaw AI
ClawX is a cross-platform desktop application that provides a graphical user interface for OpenClaw AI agents, transforming complex command-line orchestration into an accessible visual experience. Built with Electron, React, and TypeScript, the software embeds the OpenClaw runtime directly into the application to deliver a battery-included setup without requiring separate installations. The platform focuses on usability by offering a guided setup wizard, visual configuration panels, and real-time validation, enabling users to deploy AI agents without terminal expertise. ClawX includes a modern chat interface that supports multiple conversation contexts, Markdown rendering, and persistent message history. It also supports automation through cron-based scheduling and allows users to manage multiple AI channels simultaneously for different workflows. -
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llmfit
157 models, 30 providers, one command to find what runs on hardware
llmfit is a terminal-based utility that helps developers determine which large language models can realistically run on their local hardware by analyzing system resources and model requirements. The tool automatically detects CPU, RAM, GPU, and VRAM specifications, then ranks available models based on performance factors such as speed, quality, and memory fit. It provides both an interactive terminal user interface and a traditional CLI mode, enabling flexible workflows for different user preferences. llmfit also supports advanced configurations including multi-GPU setups, mixture-of-experts architectures, and dynamic quantization recommendations. By presenting clear performance estimates and compatibility guidance, the project reduces the trial-and-error typically involved in local LLM experimentation. Overall, llmfit serves as a practical decision assistant for developers who want to run language models efficiently on their own machines. -
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NanoClaw
A lightweight alternative to Clawdbot / OpenClaw
Nanoclaw is a lightweight, security-focused personal agent runtime designed as a slimmer alternative to larger “personal assistant” agent stacks, with an emphasis on being easy to audit and safe by default. It runs agent execution inside Apple containers to provide strong isolation boundaries, so individual chats and actions can be sandboxed with tighter filesystem and process separation than a typical single-process bot. The project connects directly to WhatsApp, letting you deploy an assistant that can chat in a familiar interface while still supporting real agent behaviors instead of simple call-and-response prompts. It includes memory so the assistant can retain important context across interactions, enabling more consistent follow-through on ongoing tasks. It also supports scheduled jobs, making it suitable for recurring reminders, periodic automations, and timed workflows without needing an external orchestrator. -
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PicoClaw
Ultra-Efficient AI Assistant in Go
PicoClaw is an ultra-lightweight, open-source personal AI assistant written in Go, architected from the ground up to operate with extremely low memory usage (under 10 MB) and fast boot times, making it suitable for inexpensive hardware platforms and embedded devices. Inspired by earlier AI assistant projects like “nanobot,” it was refactored to emphasize resource efficiency while still supporting meaningful AI-driven interactions such as conversational workflows, planning tasks, and automation. PicoClaw can run on hardware costing as little as $10 and on resource-constrained environments like RISC-V or ARM boards, with cross-architecture portability achieved through a single self-contained binary. The project’s goals include broad platform support (including Linux, macOS, and multiple CPU architectures), rapid startup times that make the assistant feel responsive, and integration with popular messaging platforms via gateways or bots. -
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Antigravity Claude Proxy
Proxy that exposes Antigravity provided claude / gemini models
Antigravity Claude Proxy is a purpose-built proxy server that enables developers to interface with Claude models through a standardized RESTful API, allowing tools and workflows that expect generic HTTP APIs to operate on Anthropic’s Claude without native support. The project acts as a translation layer, receiving web requests in common formats (such as OpenAI-style endpoints) and forwarding them to Anthropic’s API in the required structure, while converting responses back into a familiar shape. This makes it easier to integrate Claude into existing toolchains, scripts, notebooks, or agent frameworks that do not have built-in support for Anthropic’s native SDKs. It abstracts away key differences like authentication choreography, request schema quirks, and streaming protocols so client code can remain unchanged when switching between models. -
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IronClaw
IronClaw is OpenClaw inspired but focused on privacy & security
IronClaw is a security-first, open-source personal AI assistant built in Rust and designed to keep your data fully under your control. It operates on the principle that your AI should work for you, not external vendors, ensuring all data is stored locally, encrypted, and never shared. The platform emphasizes transparency, offering auditable code with no hidden telemetry or data harvesting. IronClaw runs untrusted tools inside isolated WebAssembly (WASM) sandboxes with strict capability-based permissions. It supports multiple interaction channels, including REPL, HTTP webhooks, Telegram, Slack, and a real-time web gateway. With dynamic tool building, persistent memory, and background automation, IronClaw is built to securely expand and adapt to your personal and professional workflows. -
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OpenClaw Office
OpenClaw Office is the visual monitoring and management frontend
OpenClaw Office is a visual monitoring and management interface designed for the OpenClaw multi-agent system, providing an immersive and interactive way to observe and control autonomous AI agents. It presents agent activity through a virtual office environment, where each agent is represented as an animated entity within a 2D or 3D workspace. The platform enables real-time visualization of agent states, interactions, and workflows, making complex multi-agent coordination easier to understand and debug. Users can observe communication flows between agents through visual connections, track token usage and operational costs, and analyze performance through integrated dashboards and charts. The system also includes live chat capabilities, allowing users to monitor conversations and tool calls as they occur. -
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OpenClaw Opik Observability Plugin
Official plugin for OpenClaw that exports agent traces to Opik
OpenClaw Opik Observability Plugin is an open-source plugin designed to add observability and monitoring capabilities to OpenClaw autonomous AI agents by exporting operational traces to the Opik observability platform. The project integrates directly with OpenClaw’s plugin architecture so that developers can capture detailed runtime information about how their agents behave while executing tasks. Each time an AI agent performs an action—such as calling a large language model, invoking a tool, accessing memory, or delegating to a sub-agent—the plugin records the full interaction and sends it to Opik for analysis and visualization. This allows developers to inspect inputs, outputs, token usage, latency, and execution flow across complex multi-step agent workflows. The goal of the project is to provide transparency into the internal reasoning and operational pipeline of agent systems so developers can diagnose failures, control costs, and improve reliability. -
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nanobot
🐈 nanobot: The Ultra-Lightweight Clawdbot / OpenClaw
nanobot is an ultra-lightweight personal AI assistant designed to deliver powerful agent capabilities without unnecessary complexity. Built in just ~4,000 lines of clean, readable code, it offers a minimalist alternative to heavyweight agent frameworks while retaining core intelligence and extensibility. nanobot is optimized for speed and efficiency, enabling fast startup times and low resource usage across environments. Its research-ready architecture makes it easy for developers to understand, customize, and extend for experimentation or production use. With simple one-click deployment and a straightforward CLI, users can get a working AI assistant running in minutes. Inspired by Clawdbot but radically simplified, nanobot proves that capable AI agents don’t need massive codebases. -
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MimiClaw
Run OpenClaw on a $5 chip
MimiClaw (from the mimiclaw project) is an edge-AI personal assistant that runs directly on extremely low-cost hardware like an ESP32-S3 microcontroller without a full operating system, Node.js, or cloud backend. By running pure C on a bare-metal chip, MimiClaw brings AI interactions and persistent memory to a tiny USB-powered device you can carry in your pocket. You connect the device to Wi-Fi and chat with it using Telegram, making it a convenient always-on assistant for tasks like reminders, quick lookups, or custom AI interactions. Even though it’s running on minimal hardware, MimiClaw maintains local memory that persists across power cycles, enabling context continuity over time without relying on cloud services. Its architecture emphasizes privacy, low power, and portability, ideal for personal or hobbyist use cases where privacy and local control matter. -
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openclaw-kapso-whatsapp
Give your OpenClaw AI agent a WhatsApp number
openclaw-kapso-whatsapp is a plugin repository designed to extend the OpenClaw AI agent by giving it a dedicated WhatsApp phone number using the official Meta Cloud API via Kapso, enabling direct interaction through one of the most widely used messaging platforms. This integration allows the autonomous AI assistant to send and receive messages on WhatsApp, turning the agent into a real-world task performer accessible through text conversations. The plugin is built in Go and handles communication entirely through cloud APIs, avoiding the risk of bans that come with unofficial or reverse-engineered interfaces. Projects like this make it possible for OpenClaw users to automate tasks, interact with personal contacts, or provide AI-driven services without building a custom bot infrastructure from scratch. Because OpenClaw itself runs on the user’s own hardware and can access external services, this WhatsApp extension serves as a bridge between the AI agent and daily messaging workflows. -
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Claw Compactor
14-stage Fusion Pipeline for LLM token compression
Claw Compactor is a utility designed to optimize and manage the context limitations inherent in AI agent systems, particularly those built on OpenClaw-like architectures. It addresses the challenge of finite context windows in language models by compressing or summarizing historical interactions while preserving essential information. The system works by transforming older conversation data into condensed representations that maintain continuity without exceeding token limits. This approach allows long-running agent sessions to continue operating efficiently without losing critical context. It is especially useful in autonomous workflows where agents accumulate large volumes of interaction history over time. The project aligns with broader strategies in AI systems that balance memory retention with computational constraints. Overall, claw-compactor functions as an infrastructure component that enhances scalability and stability in persistent AI agent environments. -
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Claw3D
Claw3D is an open source 3D engine built on OpenClaw
Claw3D is an experimental open-source platform that combines elements of 3D simulation, developer tooling, and AI orchestration by creating an interactive virtual workspace where AI agents can be visualized as active participants in a shared environment. It is designed as a 3D “virtual office” where users can observe, manage, and interact with multiple AI agents performing tasks such as coding, reviewing pull requests, and coordinating workflows in real time. Instead of relying on traditional dashboards or logs, Claw3D introduces a spatial interface that allows users to navigate through a simulated office and watch agents collaborate, effectively turning abstract processes into tangible visual interactions. The system supports task assignment, progress tracking, and communication between agents, creating a representation of autonomous or semi-autonomous workflows. It can be self-hosted, giving users full control over deployment, customization, and scaling of their AI workspace. -
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AutoResearchClaw
Autonomous research from idea to paper. Chat an Idea. Get a Paper 🦞
AutoResearchClaw is an open-source framework designed to automatically generate full academic research papers from a single idea or topic. Built in Python, it orchestrates a multi-stage research pipeline that gathers literature, formulates hypotheses, runs experiments, analyzes results, and writes the final paper. The system retrieves real academic references from sources such as arXiv and Semantic Scholar to ensure credible citations. It can automatically generate code for experiments, run them in a sandbox environment, and analyze the results with statistical methods. The platform also uses multi-agent debate and automated peer review processes to refine research findings and improve paper quality. By combining literature discovery, experimentation, and writing automation, AutoResearchClaw aims to turn research ideas into conference-ready papers with minimal human intervention. -
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DenchClaw
Fully Managed OpenClaw Framework for all knowledge work ever
DenchClaw is a local-first AI-powered CRM and productivity platform built on top of the OpenClaw framework, designed to transform a user’s entire computer into a programmable, agent-driven workspace. Unlike traditional cloud-based CRMs or AI tools, it runs entirely on the user’s machine and exposes a web interface locally, allowing full control over data, workflows, and automation without relying on external servers. The system combines database management, browser automation, and AI reasoning into a unified interface where users can interact with their data and tools using natural language commands. It can ingest data from sources such as Google Drive, Notion, Gmail, and CRM platforms, consolidating everything into a centralized workspace for analysis and action. One of its most distinctive capabilities is its ability to use the user’s existing browser session, enabling it to log into services, scrape data, and perform actions like outreach or research as if it were the user. -
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Lossless Claw
LCM (Lossless Context Management) plugin for OpenClaw
Lossless Claw is an advanced context management plugin for the OpenClaw agent ecosystem that redefines how conversational memory is handled in large language model systems. Instead of relying on traditional sliding-window truncation or lossy summarization, it introduces a lossless architecture that preserves all historical messages while maintaining usable context within token limits. The system stores every interaction in a persistent database and incrementally summarizes older content into a hierarchical directed acyclic graph, allowing efficient compression without discarding information. This structure enables agents to dynamically reconstruct detailed context by expanding summaries when needed, effectively simulating perfect long-term memory. -
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ClawRouter
Smart LLM router
ClawRouter is a flexible networking and routing framework designed to support AI-oriented distributed systems and agent ecosystems by managing how messages, requests, and responses are routed between components. It provides a programmable router abstraction that can handle complex traffic patterns, enabling dynamic message forwarding, load balancing, and custom routing logic based on content, context, or policy rules. Because distributed AI systems often involve many services, agents, and runtime components interacting with each other and with external APIs, ClawRouter helps ensure that communication paths remain clear, efficient, and adaptable as systems scale. The framework supports plugin-based extensions so developers can define custom protocols, transformation hooks, and monitoring handlers without modifying core routing logic. It also offers operational features like health checking, metrics reporting, and failure handling that make production deployments more reliable. -
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Obsidian Skills
Agent skills for Obsidian
Obsidian-Skills is a repository of agent skills tailored for use with Obsidian and any Claude-compatible agent that follows the standard Agent Skills specification, enabling AI assistants to better understand and interact with Obsidian content. These skills are markdown-driven specifications that teach Claude Code (or similar agents) how to perform context-aware tasks within Obsidian’s unique environment, such as interpreting different file types and workflows, automating workflows tied to notes, or enhancing agent responses with structured knowledge. By providing formal descriptions of patterns, conventions, and workflows common to Obsidian users, the skills empower AI tools to give more relevant suggestions, generate content that adheres to user conventions, or execute complex multi-step operations that respect the knowledge graph and file relationships. -
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OpenClaw CN
The Chinese version of OpenClaw
OpenClaw-CN is a Chinese language community adaptation and localization of the OpenClaw project, focused on making a powerful open-source agent framework usable and understandable for Chinese-speaking developers. It includes translated documentation, localized examples, and language-specific nuances so that developers in the Chinese ecosystem can adopt and contribute without a language barrier. The repository mirrors the structure of the upstream project but adds Chinese translations of core workflows, prompts, guidelines, and best practices for building multi-agent systems or AI applications. Beyond simple translation, the project often curates region-specific integrations or tooling recommendations that resonate with local developer environments and platforms. It helps accelerate adoption by providing readable guides, sample configurations, and annotated code that aligns with Chinese developer preferences and tooling conventions. -
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Skill Scanner
Security Scanner for Agent Skills
This repository is a public security-focused scanning tool intended to analyze and assess AI agent skills for potential issues, quality concerns, and vulnerabilities. It acts as a scanner that inspects Agent Skills packages to flag structural problems, inconsistencies, or security flaws before they are deployed or integrated into agent workflows. Because agent skills can contain executable instructions and logic, scanning them for risky patterns is essential to prevent inadvertent exploitation when used by intelligent systems. The tool supports maintainers and community contributors in automating quality checks and enforcing conventions across skill sets in a standardized way. While still evolving with contributions and issue discussions, it shows the community’s interest in building safer AI ecosystems around reusable capabilities. The scanner also serves as a foundation for more sophisticated vetting frameworks that might be incorporated into CI/CD pipelines.
