xAI

Top Builders

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

xAI

xAI is an innovative AI technology company founded by Elon Musk in July 2023. The company is dedicated to pushing the boundaries of artificial intelligence by creating tools and platforms to advance scientific discovery, foster understanding, and simplify complex problem-solving. With cutting-edge technology and a user-focused approach, xAI provides developers and organizations with the tools they need to unlock the potential of AI in everyday applications.

General Information

Attribute	Details
Company	xAI
Founded	2023
Documentation	https://docs.x.ai/docs
API Reference	https://docs.x.ai/api#introduction

Products

Grok AI Models

xAI's flagship product line consists of the Grok series of conversational AI and vision-enhanced models:

Grok-1: xAI's foundational model offering robust reasoning and conversational capabilities.
Grok-1.5: Improved reasoning and extended context for handling large datasets.
Grok-1.5V: Introduced vision capabilities for analyzing images and diagrams.
Grok-2: Enhanced performance with advanced image generation and multimodal abilities.

xAI API

An accessible platform for developers to integrate Grok models into their applications. Fully compatible with existing OpenAI and Anthropic SDKs.

PromptIDE

A powerful integrated development environment for crafting, testing, and refining AI prompts. Designed to optimize prompt engineering workflows for large language models.

Start Building with xAI

👉 Visit x.ai to create a developer account and get started.

👉 Sign in to the xAI Developer Portal to obtain API keys, access Grok models, and explore documentation.

Edit on GitHub

xAI AI Technologies Hackathon projects

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

GameScrapers: real-time mobile games intel agent

Mobile gaming is a $100B+ industry with tens of thousands of live games, yet intelligence on SDK adoption, ad-monetization stacks, and direct ad-inventory relationships sits behind $30K/year tools refreshed monthly. GameScrapers is an autonomous AI-agent platform . It continuously crawls every game on iOS and Google Play, decompiles binaries to detect 115+ SDKs (AppLovin, Unity/ironSource, Pangle, Mintegral, Vungle/Liftoff, AdMob), parses every app-ads.txt for direct inventory ties, captures ad creatives, and joins it all into a real-time graph of who-uses-what across mobile gaming. Incumbents like data.ai, Sensor Tower, and AppMagic ship monthly reports. GameScrapers ships real-time agentic deltas at a fraction of the cost. Demo workflows unbuildable without Bright Data: (1) every Top-100 puzzle game that added an ad SDK this week, (2) real-time alerts when a competitor reshapes its mediation waterfall, (3) an M&A target list of hyper-casual studios on a single mediation partner.

A-JEPA AUTOMATA

High Level Overview Automata is a production-grade AI agent platform that combines live web intelligence with formal verification — making it the first system where AI-driven business decisions are mathematically auditable before execution. The core problem: enterprise teams can't trust AI agents acting on web data because there's no proof the reasoning is sound. Automata solves this with a three-layer stack. Layer 1 — Web Intelligence Intake (Bright Data): The Bright Data MCP Server and Web Scraper API feed structured live data — competitor pricing, regulatory filings, LinkedIn hiring signals, SERP trends — directly into the ingestion pipeline. Web Unlocker handles bot-protected sources. All intakes logged to a Blake2b-hashed append-only audit trail from the first byte. Layer 2 — Agentic Orchestration: A FastAPI backend with async workers processes ingested signals. The Go CLI harness runs named analysis flows — sorry scan, interconnect map, signal diff — and exposes structured JSON for downstream AI agents. A proof watcher tracks theorem and proof-completion metrics per file in real time, ensuring that the logic layer never silently regresses. Layer 3 — Formal Verification : Every intelligence claim that triggers an action passes through an Automata state machine. The proof_completion metric — theorems minus sorry-count divided by theorem-count — gates whether a decision is certified or flagged for human review. No sorry-equivalent proof, no downstream action. This is provable trust, not probabilistic trust. Infrastructure: Docker Compose stack with Postgres, Redis, Alembic migrations, Grafana/Loki observability, nginx reverse proxy, and an inotify-based file watcher. Deployable on ROCm hardware. Track coverage: GTM Intelligence (competitor and buying-signal monitoring), Finance & Market Intelligence (pricing and filing pipelines), Security & Compliance (regulatory change detection with proof-gated alerts). A single coherent system spanning all three tracks.

SmartLearn AI

SmartLearn AI is a modern AI-powered learning assistant designed to provide intelligent, context-aware educational support through conversational AI and document-based learning. The platform combines a high-performance FastAPI backend with a responsive React frontend to deliver a seamless ChatGPT-like experience for students and learners. The system allows users to upload PDF documents and ask questions directly from their content. Using a Retrieval-Augmented Generation (RAG) pipeline, the application extracts text from uploaded PDFs, splits the content into chunks, generates embeddings using Sentence Transformers, and stores them in a FAISS vector index for semantic search. When a user asks a question, the most relevant context is retrieved and sent to the Groq LLaMA 3 large language model to generate accurate and context-aware responses. SmartLearn AI also supports persistent multi-chat history using PostgreSQL and SQLAlchemy, enabling users to manage conversations efficiently with features like chat storage, retrieval, and deletion. The project is deployed using Vercel for the frontend and Railway for the backend and database services. The frontend is built with React and Vite, offering a fast and modern user interface, while the backend uses FastAPI for scalable API performance. The project demonstrates practical implementation of modern AI engineering concepts including semantic search, vector databases, LLM integration, RESTful APIs, and full-stack deployment workflows. SmartLearn AI aims to improve digital learning experiences by making educational content interactive, searchable, and AI-assisted through real-time intelligent conversations.

StoryTrace - Git for News

StoryTrace is a "Git for News" — a tool that tracks how a story mutates as it travels across global media outlets. You paste any article URL or type a topic (e.g. "Iran nuclear deal"). StoryTrace's 7-agent LangGraph pipeline springs into action: the Seed Agent finds the original wire story via GDELT, the Crawler Agent scrapes 15 RSS feeds across 8 countries, a Translator Agent localizes non-English coverage to English, the DNA Extractor (Featherless/Qwen2.5) pulls structured facts from each article, the Drift Scorer measures how many key facts each outlet dropped or distorted, the Geo Builder organizes coverage by country into a D3-ready tree, and an Alert Agent fires webhooks when drift exceeds a threshold. The results render as an interactive D3.js tree — nodes colored green (faithful), amber (moderate drift), red (high drift) — alongside a 3D globe showing which countries covered the story and how differently. Clicking any node reveals a DiffPanel showing exactly which facts were kept, dropped, or altered. The backend is FastAPI + LangGraph + PostgreSQL + Redis. The frontend is Next.js 16 with D3 v7 and react-globe.gl. AI layers: Featherless API (Qwen2.5-7B) for structured JSON fact extraction, Google Gemini for translation and optional world-impact forecasting, spaCy for local NER (zero tokens). The entire pipeline runs on ~4,000–6,000 tokens per story — lean by design. StoryTrace addresses a real problem: media consumers have no tool to see how coverage diverges from the original facts. We make that drift visible, quantified, and interactive.

AutoPenAI — Autonomous Penetration Testing Agent

Summary Modern security teams cannot keep up with expanding corporate network targets. Traditional human security testing only gives a single snapshot in time and fails to match fast software updates. This autonomous agent solves the problem by continuously scanning, mapping, and testing network defense barriers. Moving through a closed loop from discovery to reporting, the system uses machine learning alongside traditional hacking tools to find open routes, map connections, filter out fake alerts, and safely test threats inside an isolated sandbox. It converts chaotic, raw threat data into clear instructions, moving companies from slow defense reactions to continuous, proactive protection against hackers.