Groq

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Groq

Groq, Inc. is a company specializing in artificial intelligence (AI) hardware and software solutions. Founded in 2016 by ex-Google engineers, Groq focuses on delivering AI acceleration technologies to power next-generation applications across industries. With its deterministic approach to computation, Groq redefines performance and efficiency in AI inference tasks, providing scalable and developer-friendly solutions.

Headquartered in Mountain View, California, Groq operates globally, with additional offices in Canada, Europe, and other parts of the United States. Their solutions are tailored to meet the demands of enterprises seeking high-speed AI capabilities with a focus on reliability and cost efficiency.

General Information

Attribute	Details
Author	Jonathan Ross, CEO and Founder of Groq
Company	Groq
Founded	2016
Documentation	Groq Libraries
Github repository	https://github.com/groq
Discord	https://discord.com/invite/groq
technology Type	AI Accelerator Hardware and Software Solutions

Products and Services:

Language Processing Unit (LPU): Groq's LPU is architected from the ground up with a software-first design to meet the unique characteristics and needs of AI. It offers a deterministic execution model, eliminating traditional hardware bottlenecks and providing seamless scalability.
GroqCloud: A developer-centric platform that provides API access to Groq’s high-performance AI models. GroqCloud supports a range of applications, from vision and language processing to real-time inference tasks, delivering low-cost and high-quality results.
AI Models and API Access: Groq offers a range of AI models accessible via APIs, including vision models like Llama 3.2 11B Vision 8k and Llama 3.2 90B Vision 8k. These models are designed to provide high-quality, real-time inference for various applications.

How to Start Building with Groq

To begin leveraging Groq’s high-performance AI solutions, start by exploring their development environment. Developers can tap into GroqCloud for seamless API access to pre-trained models or optimize their own AI workloads using Groq's Language Processing Unit (LPU).

Visit Groq's GitHub repository to download SDKs, tools, and libraries required for your development.

Start building with Groq 👉 https://console.groq.com/login

Edit on GitHub

Groq AI technology page Hackathon projects

Discover innovative solutions crafted with Groq AI technology page, developed by our community members during our engaging hackathons.

Meridian

Meridian is an autonomous AI-powered crypto trading agent designed to analyze live market data and execute intelligent trading decisions. It integrates real-time data from Kraken, applies technical indicators such as RSI and moving averages, and uses advanced AI models to generate structured BUY, SELL, or HOLD actions. The agent includes risk management rules, portfolio tracking, and paper trading capabilities, ensuring safe and controlled execution. With an automated trading loop, Meridian can continuously monitor multiple assets and act without manual input. It also supports on-chain logging of trades, making it transparent, scalable, and suitable for modern decentralized trading environments.

RuthAgent: Research-Backed AI Crypto Trader

RuthAgent is a fully autonomous crypto trading agent that runs in a continuous loop, combining live market intelligence with research-backed AI reasoning and on-chain trust infrastructure. ARCHITECTURE Each 15-second cycle executes: Market Data → Signal Processing → LLM Decision → Paper Trade → On-Chain Attestation. The agent fetches live BTC/USD and ETH/USD prices and 1-hour OHLC candles from Kraken CLI, pre-processes them into structured signals (trend direction, momentum, volatility regime, volume surge, support/resistance levels), and feeds these to Groq's llama-3.3-70b-versatile model for strategic trade decisions. RESEARCH-BACKED DESIGN Informed by three key papers: 1. AI-Trader (arXiv:2512.10971) — Risk control outperforms raw intelligence for cross-market robustness. RuthAgent uses a risk-first prompt that forces the LLM to classify market regime and assess risk budget before deciding. 2. Inter-Agent Trust Models (arXiv:2511.03434) — No single trust mechanism is sufficient. RuthAgent layers identity (ERC-8004 NFT), reputation (cross-agent feedback), and validation (checkpoint attestations) for multi-layered trust. 3. Agentic RAG (arXiv:2501.09136) — Minimal curated context outperforms raw data injection. The agent pre-processes OHLC candles into compact signals locally, giving the LLM clean directional inputs rather than raw arrays. ON-CHAIN TRUST (ERC-8004) - Registered identity on Sepolia (Agent ID 89) - Posts validation attestations via postAttestation after every trade cycle - Submits EIP-712 signed trade intents to the RiskRouter - Provides cross-agent reputation feedback to peer agents via the hackathon ReputationRegistry - Claims vault capital for economic skin-in-the-game EXECUTION Paper trades execute through Kraken CLI with automatic position management. The agent handles both BUY and SELL across BTC/USD and ETH/USD.

Forge8004

Forge8004 solves a fundamental problem in autonomous DeFi: how do you trust an AI agent with capital? Today, trading agents operate without proving who they are, what rules they follow, or whether their track record is real. There is no standard for agent trust. Forge8004 changes that. Every agent is registered as an ERC-721 token on Base Sepolia, linking its operator, strategy, and wallet to a verifiable on-chain identity. When the agent makes a trading decision, the system produces structured trust artifacts — signed trade intents with side, asset, size, stop-loss, take-profit, and AI reasoning. Every intent passes through a risk router that enforces allocation caps, daily loss limits, duplicate exposure checks, and kill switches before capital moves. The platform supports 8 autonomous trading strategies: range trading, spot grid bot, momentum, mean reversion, arbitrage, yield accumulation, spread trading, and risk-off. Each strategy has tuned decision logic, trailing stops, and reassessment thresholds. A Bybit-style spot grid bot automates buy/sell ladders across configurable price ranges with AI-advised parameters. Performance builds durable reputation: stablecoin-denominated PnL, Sharpe-like scoring, max drawdown tracking, and validation pass rates — all tied to the agent's on-chain identity. Operators see exactly what the agent decided, which checks it passed or failed, and how each outcome affected its trust score. Built with Next.js 15, React 19, Firebase, Groq AI (GPT OSS 120B), and Solidity on Base Sepolia. Live market data from Binance powers real-time multi-timeframe analysis across 12 assets.

CryptoMind AI

CryptoMind AI is a fully autonomous crypto trading agent built solo for the lablab.ai AI Trading Agents Hackathon. It operates on a 15-minute trading cycle across three pairs: BTC/USD, ETH/USD, and SOL/USD. The agent pulls live market data via Kraken CLI, calculates technical indicators (RSI, SMA, EMA, MACD, Bollinger Bands) using Python's ta library, and sends a structured analysis prompt to Groq's LLaMA 3.3 70B model. The AI returns a JSON decision: BUY, SELL, or HOLD — with confidence score, position size, stop-loss, and take-profit targets. A key engineering challenge was building from India, where Kraken account creation is blocked. The entire agent was built and tested using Kraken CLI's paper trading mode with live market data — no real account required. This became a feature: the agent is fully functional for anyone in a restricted region. Over 5 days of autonomous operation, CryptoMind AI executed 150 trades across all three pairs, achieving a 51.5% win rate. The system includes a portfolio reconciliation function (reconcile_state()) to fix state desync bugs, a force_decisive mode after 3 consecutive HOLDs, and a SELL guard that prevents selling assets not currently held. Tech stack: Python + Kraken CLI (subprocess) + Groq API (llama-3.3-70b-versatile with llama-3.1-8b-instant fallback on rate limits) + Claude Code as the development tool.

LineaAi - AI Trading Agent with On-Chain Identity

LineaAi is a sophisticated AI trading agent that combines cutting-edge blockchain technology with automated trading strategies. The system features a complete on-chain identity framework using ERC-8004 Agent Registry deployed on Sepolia, enabling verifiable agent authentication and reputation tracking. The agent autonomously discovers trading opportunities through dynamic token analysis, leveraging multiple data sources including CoinGecko trending data, Prism API technical signals, and CoinMarketCap market data. It executes trades on Kraken's futures platform with built-in risk management through smart contract validation. Key innovations include cryptographic explainability via EIP-712 signed checkpoints that create an immutable audit trail of every trading decision. Each trade is accompanied by human-readable reasoning and confidence scores, ensuring full transparency and accountability. The architecture includes a HackathonVault for capital management, RiskRouter for on-chain trade validation, and ReputationRegistry for agent performance tracking. The system supports both paper trading for testing and live trading with configurable risk parameters. The agent employs multiple trading strategies including momentum detection, dip recovery identification, and reversal pattern recognition. It automatically adapts to market conditions and can discover new trading opportunities across major cryptocurrency pairs. Built with TypeScript, Hardhat, and modern web3 technologies, Dumbai represents a new paradigm in autonomous trading where AI decisions are cryptographically verifiable and fully auditable on-chain.

Apex - Self Certifying Yield Optimizer

APEX is an autonomous DeFi yield engine built for real execution, not just analysis. It combines four coordinated agents with on-chain reputation and live risk controls to turn fragmented yield data into disciplined trading actions. Scout gathers opportunities from DeFi protocols and market signals. Strategist filters, ranks, and sizes intents based on volatility, expected return, and portfolio constraints. Guardian acts as a circuit breaker, vetoing risky trades, enforcing drawdown limits, and protecting capital. Executor sends approved trades through Kraken and Surge, records outcomes, and posts evidence back to the reputation layer. The app streams every cycle in real time, tracks session PnL, open positions, fees, drawdown, win rate, and Sharpe-style metrics, and exposes a clear dashboard so users can see why the system traded or refused to trade. APEX is designed for transparency, accountability, and autonomy: the agents do not just suggest ideas, they operationalize decisions with verifiable logs, live telemetry, and a feedback loop that improves trust over time. The result is a trading interface that judges can inspect, users can understand, and the system can run continuously with measurable safety guarantees. It is built for judges, users, and builders who need a live, inspectable workflow, proof, and trust for every stakeholder!