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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.

Adaptive Router: Intelligent Model Routing

Adaptive Router: Intelligent Model Routing

Large Language Models vary wildly in capabilities and costs. Using a massive flagship model for a simple math question wastes money, while using a smaller model for complex reasoning risks failure. The Adaptive Router solves this by intelligently orchestrating requests across multiple LLMs to minimize costs while maximizing accuracy. It works by first performing zero-cost deterministic feature extraction on incoming prompts to detect complexity, category, and requirements like coding or reasoning. A task-aware scoring engine then evaluates these features against a benchmarked model registry, ranking candidates to find the cheapest model guaranteed to succeed. Beyond routing, the engine features built-in temporal awareness and live Web RAG. If a prompt requires current knowledge, it automatically executes a zero-dependency web search and injects live context before inference. Built with Python, FastAPI, and the Fireworks AI API, Adaptive Router consistently achieves over 85% cost savings with perfect accuracy. It features a stunning glassmorphism dashboard providing real-time telemetry, routing audits, and live web search tracking.

DSS BACKGROUND HANDSHAKES PROTOCOL

DSS BACKGROUND HANDSHAKES PROTOCOL

Background handshake authentication vectored to three parameters user contacts, device and location. Eliminating log in authentication errors thus saving a massive amount of energy consumption from error handling processes of the digital grid at the global scale. Not only that it saves energy but also provides users ease of access. No more OTP, no more CAPTCHA challenges, no more friction from logging in saves time for users and prevents them anxiety due to forgotten passwords. It will eliminate third party intervention that usually is the gateway for data breach. Bad actors takes advantage of this vulnerability and could be the avenue for phishing and other scamming schemes.

Axiom — Token-Efficient General-Purpose AI Agent

Axiom — Token-Efficient General-Purpose AI Agent

Axiom is a token-efficient general-purpose AI agent built for AMD Developer Hackathon ACT II — Track 1, where fewest tokens wins subject to a ≥84.2% accuracy gate. The architecture applies nine levers in combination: (1) thinking={"type":"disabled"} on every Fireworks call to skip the model's billed reasoning pass — the single largest token win; (2) tight per-category max_tokens caps (12 for math, 15 for sentiment, up to 180 for code generation); (3) stop sequences that halt on the first newline or follow-up cue; (4) JSON response_format for sentiment, NER, and logic to eliminate parse failures; (5) bounded SHA1-keyed in-memory prompt cache for identical prompts; (6) input truncation (200 chars for sentiment, 320 for summaries, 600 for code); (7) deterministic temperature 0.0; (8) ThreadPoolExecutor parallel processing at 4 workers; (9) shape validation pass that flags malformed outputs as INVALID before they reach the grader. On the 19-task AMD sample suite (fact_001–gen_003), the agent returns clean answers for all 19 tasks with 100% accuracy on the 12 tasks with verifiable ground truth. End-to-end token usage: ~1,545 total tokens (down from my previous 2,656). Wall-clock under 10 seconds even at single-worker sequential execution. The runtime is a single python -m app.main invocation wrapped in a slim python:3.11-slim container exposing port 8080 for health probes. The image reads /input/tasks.json, processes it through eight category handlers, and writes structured results + per-category token accounting to /output/results.json in strict conformance with the published schema. Single-model deployment keeps 404 risk at zero; model tiering is plumbed for the day cheaper chat models become available on the team's Fireworks account. See DESIGN.md for the full architecture narrative and scripts/token_report.py for a one-command local dry-run that validates accuracy, token budget, and zero-error conditions.

AeroClaw : Autonomous B2B Procurement Gateway

AeroClaw : Autonomous B2B Procurement Gateway

AeroClaw is an end-to-end, autonomous B2B procurement platform designed to eliminate modern supply chain paralysis caused by manual inventory monitoring and sluggish email negotiations. The architecture relies on a highly secure, local background daemon running an intelligent File Watcher that monitors enterprise inventory files in real-time. The moment a stock shortage is flagged, the system deploys targeted Price Crawler subagents to securely scan competitive market portals and extract the best live procurement rates. This data is instantly fed to our proprietary CFO Negotiator agent, which mathematically optimizes and drafts human-like proposals for supplier outreach. Built for the enterprise, the platform features a real-time analytics overview dashboard with an interactive negotiation delta slider, giving operators complete visibility over margin controls. To guarantee absolute security, a human-in-the-loop consent gate ensures no proposal is dispatched without formal internal approval, successfully bridging the gap between raw warehouse status and live market intelligence.

SupaBand (Your Personal Agentic Team)

SupaBand (Your Personal Agentic Team)

**Supaband** is a multi-agent management system that can manage your business, projects, or basically any workflow where multiple agents need to collaborate. The core idea is simple — you have **Supa**, the main supervisor agent, and whenever you need specialized work done, Supa can spawn new agents and build custom teams dynamically. Need coding agents? Supa creates worker agents for software development. Need marketing? It creates strategy and production agents. For our hackathon demo, we are showing an autonomous business management system where you give Supa a business idea, and it brainstorms, creates marketing strategies, assigns tasks to other agents, and executes them automatically. Agents can create content, generate images/videos, manage a production dashboard, periodically create social posts, and send completed work for approval through a task board system. We also designed it to integrate third-party services for execution, like external production APIs such as Higgsfield. The bigger idea is interoperability. Supaband agents are not limited to the system itself — you can connect external agents running elsewhere. For example, if you have an OpenClaw agent managing a VPS remotely, Supa can connect with it, delegate tasks, and manage your infrastructure without you logging in manually. In short, instead of AI helping with single tasks, Supaband lets you build an autonomous workforce that can actually run operations for you.