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OpenClaw: Your Personal AI Assistant

OpenClaw is a personal AI assistant designed to automate tasks and interact with various applications and your local machine. It aims to streamline daily workflows by performing actions like managing emails, calendars, and flight check-ins directly through chat interfaces like WhatsApp, Telegram, and others. OpenClaw operates locally, offering flexibility with different AI models.

General
Release date2024
AuthorPeter Steinberger
Websitehttps://openclaw.ai/
Repositoryhttps://github.com/openclaw/openclaw
TypePersonal AI Assistant

Key Features of OpenClaw

  • Local Operation: Runs directly on your macOS, Windows, or Linux machine, keeping your data private.
  • Flexible AI Models: Supports various AI models including Anthropic, OpenAI, or local models.
  • Chat App Integration: Interact with OpenClaw through popular chat applications like WhatsApp, Telegram, Discord, Slack, Signal, and iMessage.
  • Persistent Memory: Learns and remembers your preferences and context over time to become a truly personalized assistant.
  • Browser and System Access: Capable of browsing the web, filling forms, extracting data, reading/writing files, and executing shell commands/scripts with optional sandboxed access.
  • Extensible with Skills & Plugins: Expand its capabilities with community-built skills, and it can even create new skills autonomously.
  • Proactive Task Management: Can perform scheduled tasks, set reminders, and manage background operations.

Start Building with OpenClaw

OpenClaw provides a powerful platform for personal automation and AI-driven task management. Its local-first approach combined with extensive integration capabilities makes it a versatile tool for enhancing productivity and privacy. Developers and users interested in leveraging autonomous AI agents can explore OpenClaw to build custom solutions or automate their daily digital lives.

While specific boilerplate or library examples are not yet widely available due to its nascent stage, the core functionality revolves around its API and integration points with chat applications and local system access.

OpenClaw Tutorials

OpenClaw Resources

Here are some valuable resources to help you get started with OpenClaw:

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Openclaw AI Technologies Hackathon projects

Discover innovative solutions crafted with Openclaw AI Technologies, developed by our community members during our engaging hackathons.

DroneOS - AI-Powered Autonomous Fleet Dispatch

DroneOS - AI-Powered Autonomous Fleet Dispatch

DroneOS is an autonomous drone control framework built on PX4 Autopilot and ROS2. At its core is drone_core, a custom C++ SDK that exposes high-level flight control as ROS2 services — arm, takeoff, position commands, land. An OpenClaw AI agent runs on a Vultr VPS and acts as the fleet dispatcher. When emergency incidents come in through the dispatch service, they're routed to the agent via a bridge over WebSocket. The agent evaluates incident priority, checks drone availability and location, then sends flight commands through ROS2 to dispatch drones autonomously. The architecture is two servers connected over Tailscale VPN. The Vultr VPS runs the OpenClaw gateway, dispatch service, communication bridge, and React frontend. A separate simulation server runs PX4 SITL with Gazebo, dual drone_core nodes, rosbridge, and camera feeds. This is the same split you'd have in production — cloud command center talking to drones over VPN, except the drones are simulated. The frontend is a real-time dashboard connected to rosbridge over WebSocket. It shows the incident queue with priority levels, a map with drone positions, live camera feeds from both drones with picture-in-picture toggle, and an AI activity log showing every decision the agent makes. Operators see what the AI is doing and can override with natural language commands through the same OpenClaw agent. The dispatch service simulates a 911 CAD system generating incidents — medical emergencies, fires, property damage — each with priority levels and coordinates. The AI doesn't follow scripts. It decides which drone to send based on priority, proximity, and availability. The framework supports real hardware. Production Docker configs exist for Raspberry Pi companion computers communicating with Pixhawk flight controllers over serial. The simulation runs the same software stack. Live demo: http://207.148.9.142:3000 Source: https://github.com/ortegarod/drone-os