Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

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:

Edit on GitHub

Openclaw AI Technologies Hackathon projects

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

YOLO ALPHA — AI Trading Agent

YOLO ALPHA — AI Trading Agent

YOLO ALPHA is a risk-first AI trading agent that goes beyond single-indicator bots. It runs 4 independent strategy models simultaneously and fuses their signals into composite trading decisions with confidence scoring. HOW IT WORKS: The agent collects real-time OHLC data across two timeframes (15-minute and 1-hour), order book depth, and ticker data from Kraken CLI. Four strategy engines analyze this data independently: 1. Multi-Timeframe Analysis (40% weight) — The 1-hour trend sets the direction while 15-minute candles confirm optimal entry timing. This "trend + pullback" approach is what separates institutional strategies from retail. 2. Mean Reversion (25%) — Bollinger Bands combined with RSI detect oversold/overbought extremes. The agent only takes these trades

APEX Trader Autonomous Multi-Agent Trading System

APEX Trader Autonomous Multi-Agent Trading System

APEX Trader is a production-grade autonomous AI trading system built on a multi-agent architecture where five specialized agents work in a coordinated pipeline to analyze, validate, and execute cryptocurrency trades autonomously. The system consists of five agents: the Fundamental Agent analyzes NVT ratios, exchange net flows, and fair value models; the Technical Agent processes EMA crossovers, RSI, MACD, Bollinger Bands, and volume confirmation signals; the Sentiment Agent evaluates Fear & Greed Index and social sentiment scores; the Risk Agent enforces position sizing rules, portfolio heat limits, and R:R ratio thresholds; and the Backtester Agent validates every signal against historical win rates and Sharpe ratios before approval. Each trade requires multi-agent consensus above a configurable confidence threshold (72% day trading / 78% swing trading) before execution. Both day trading (5m–15m timeframes) and swing trading (4h–1D timeframes) with dynamically adjusted parameters — risk per trade, stop-loss placement, take-profit scaling, and trailing stops — all tuned to expert-level specifications. The APEX self-learning mechanism (evaluate-agent.py) continuously trains on closed trade P&L data, adjusting confidence thresholds autonomously. A self-healing daemon runs 24/7 with automatic error recovery and cooldown logic. The real-time dashboard (built on React/Next.js at port 3201 with a FastAPI backend at port 3202) provides a fully redesigned Agent Analysis Log where every stakeholder — trader, risk manager, operator, executive — gets layered information: trade identity, agent pipeline status, per-agent reasoning, strategy prediction with entry/SL/TP targets, course of action, and contextual RSS news feed — all grouped by trade, pair, or date. The project demonstrates how agentic AI systems can move beyond single-model decision making into coordinated multi-agent architectures that are transparent, auditable, and continuously self-improving.

ForgeClaw x Kraken Autonomous Crypto Trading Agent

ForgeClaw x Kraken Autonomous Crypto Trading Agent

orgeClaw × Kraken is an autonomous crypto trading agent built on a production-grade stack that goes far beyond a simple trading bot. The agent pipeline executes 7 sequential Temporal activities: connecting to the Kraken CLI MCP server for market data, fetching AI signals from PrismaAPI, computing RSI(14) and VWAP deviation analysis with volume confirmation, gating each trade behind an ERC-8004 USDC micropayment for trustless execution, executing paper trades with 10% position limits and 2% stop loss enforcement, tracking realized PnL with a FINRA-style SQLite audit log, and delivering formatted trade summaries to Slack. ForgeClaw acts as the design-time layer — a BPMN agent composer (forgeclaw-app.vercel.app) that generates Temporal workflows from visual pipelines. VerifyClaw scans every agent skill against 25 SAFE-MCP threat patterns before deployment, with a max risk score of 3/100 on this agent. Redpanda streams all trade events across 5 Kafka-compatible topics in real time. The dashboard is a pixel-accurate Kraken Pro replica with live signal feed, agent workflow panel, executor, trade history, PnL analytics, open orders, Slack log, and ERC-8004 payment ledger — all backed by a FastAPI service proxied through nginx and pulling from SQLite on every cycle. Clicking Run Agent fires a real Temporal workflow end-to-end, not a simulation. Infrastructure: Temporal + PostgreSQL for durable orchestration, Redpanda for event streaming, nginx reverse proxy, Docker Compose for one-command deployment.

QuantAgent: Verifiable & Risk-Aware AI Trading

QuantAgent: Verifiable & Risk-Aware AI Trading

QuantAgent was developed to solve the "Black Box" problem in autonomous finance. While most trading bots offer zero visibility into their logic, QuantAgent utilizes a strictly modular 3-Role System to ensure every trade is deterministic and auditable. 1. Signal Pipeline: Processes raw market data, fusing RSI, MACD, and ADX indicators while identifying market regimes (Trending vs. Ranging) to adjust strategy sensitivity. 2. Strategy Engine: A mathematical core that calculates confidence scores and scales position sizes. It features a custom Momentum Filter that prevents "buy" signals during macro downtrends, protecting capital in volatile shifts. 3. Execution Agent: A robust paper-trading environment with 8-point pre-trade validation, automated circuit breakers, and a system-wide "Reset" for rapid iteration. The project includes a real-time dashboard with a Trust Panel that exposes the agent’s inner thoughts, confidence levels, and risk checks. Designed with ERC-8004 identity in mind, QuantAgent bridges the gap between complex AI logic and on-chain accountability.

Ghostchain AI+SafeFi OMEGA

Ghostchain AI+SafeFi OMEGA

GhostChain OMEGA introduces the Sovereign Veto, a hardware-software handshake. Unlike standard bots, OMEGA monitors its own physical compute environment. If thermal stress or metal resonance indicates potential logic degradation, the agent autonomously halts capital flow. Core Technology: Orchestration: Claude 4.6 (Adaptive Thinking) via Vercel AI SDK. Payments: Native x402 Machine-to-Machine protocol on Base. Execution: Trustless Rust-based Kraken CLI bridge. Identity: Registered under ERC-8004 with a 98.4 reputation score. Safety: Adversarial Shadowing with a 0.05\% drift tolerance. 📋 5. X (Twitter) Submission Post Template Mandatory for prize eligibility. You must tag both @lablabai and @Surgexyz_. Project: GhostChain OMEGA (Auth ID: 287F-930E) 🌑 Value Prop: Hardware-aware SafeFi agent gating trades via real-time thermal telemetry (58.7°C Veto). Integrating @Surgexyz_ x402 payments & @KrakenPro CLI for the next machine economy. 🚀 #LablabAI #SurgeXYZ #SafeFi #AI #Web3 @lablabai @Surgexyz_

Automatic Website Builder

Automatic Website Builder

Most founders don’t fail because they can’t build — they fail because they spend weeks researching trends, debating ideas, and hesitating before launching. That delay kills momentum and makes product-market fit feel distant and expensive. Our solution is an OpenClaw skill that autonomously discovers real B2B pain points, generates a focused website or MVP around the opportunity, and deploys it instantly to Vercel. Instead of spending 2–4 weeks validating an idea, founders can launch in minutes, test demand immediately, and iterate based on real user feedback. By dramatically shortening the cycle from idea to live product, this tool accelerates the path to product-market fit and unlocks faster revenue opportunities. From landing-page lead generation to micro-SaaS subscriptions that can scale into recurring monthly income.