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.

trading ai agent

Most trading bots do the obvious — NVIDIA earnings beat, buy NVIDIA. By the time they act, the edge is gone. This agent thinks differently. It uses second-order reasoning — when NVIDIA beats earnings, it asks who else benefits? and trades ARM (chip architecture) and TSMC (manufacturer) before the crowd catches up. What it does: Monitors 6 live RSS news feeds continuously Uses Groq LLaMA 3.3 70B to reason about market implications Passes every trade through 10 intelligence layers: sentiment scoring, confidence thresholds, volatility checks, position sizing, stop-loss, brokerage fee simulation, portfolio awareness, and more Executes trades autonomously on Kraken xStocks Logs every decision with full reasoning — why it bought, why it skipped, what sentiment score triggered it Tech Stack: Groq LLaMA 3.3 70B · Kraken xStocks · Yahoo Finance (live prices) · Flask + JS dashboard · Persistent JSON storage Live results at time of recording: Portfolio $10,058 · P&L +$58.84 after fees · 4 active positions (NVDA, ARM, AAPL, AMD) Built solo by Varun — AI Agent Olympics · Milan AI Week 2026 🔗 github.com/AVarun3204/trading-agent

CouncilOS

CouncilOS is a collaborative multi-agent reasoning platform that explores a more research-oriented alternative to traditional single-model AI workflows. Instead of generating a single isolated response, multiple AI models engage in human-like discussions where they critique, challenge, verify, and refine each other’s reasoning dynamically in real time. The system creates a live collaborative reasoning loop where models continuously inspect the same codebase, respond to each other’s observations, re-check execution logic, and progressively refine the analysis as the discussion evolves. To reduce repetitive or weak reasoning patterns, CouncilOS also applies semantic embedding-based filtering and execution-path validation to stabilize conversations dynamically. The current implementation focuses on code analysis, but the long-term vision extends toward a general-purpose collaborative reasoning infrastructure where multiple AI systems can work together to produce more reliable, multi-perspective, and transparent reasoning workflows.

Agentic OS - autonomous agent collective

AgentiCos is a high-performance multi-agent AI orchestration platform designed to transform complex conceptual goals into autonomous, high-quality outputs. Unlike simple chat interfaces, AgentiCos utilizes a specialized "Neural Bridge" architecture where diverse agents—Planners, Executors, Reviewers, and Optimizers—collaborate in real-time. The platform's core strength lies in its Semantic Memory Bank, which captures every success pattern and user correction to build a persistent cognitive foundation that improves with every interaction. With features like self-correcting feedback loops, automated quality assurance, and deep integration with external tools via Webhooks, AgentiCos acts as a "cockpit" for cognitive infrastructure. It provides developers and researchers with professional-grade tools for managing prompt versioning, monitoring real-time agent chains, and deploying autonomous workflows that learn, adapt, and scale across any industry from marketing intelligence to core software engineering.

SENTINEL

SENTINEL is an enterprise AI safety platform that intercepts dangerous AI responses before they reach users, detects sensitive data leakage using peer-reviewed machine unlearning research, and auto-generates HIPAA compliance reports in one click. The platform sits between any AI agent and its users as a transparent proxy. Every response is intercepted, evaluated by a secondary AI judge for hallucination, missing contraindications, and policy violations, then logged with a full audit trail — in under 1.5 seconds. Three core features: Real-time safety interception catches dangerous clinical recommendations, highlights specific flagged claims inline, and explains the exact clinical risk using pharmacological reasoning and guideline citations. The Unlearning Trilemma Probe — built on 198 published experiments across 20 LLM unlearning algorithms — detects whether AI models have truly forgotten sensitive data before it becomes a GDPR or HIPAA violation. Automated compliance reporting generates board-ready audit documents covering every flagged incident, regulation implicated, and remediation recommendation. Stack: FastAPI, React, Groq Llama 3.3 70B, deployed on Render and Vercel. Three lines of code to integrate with any existing AI agent. Built solo in under 24 hours on Track 1: Agent Security & AI Governance.

Forge

Forge is an autonomous software-engineering agent for teams that want AI to do more than autocomplete code. It turns GitHub issues and pull requests into structured, reviewable workflows: planning changes, inspecting repositories, editing code in an isolated sandbox, running validation commands, generating verified patches, and returning clear review output to developers. Unlike a chat assistant, Forge operates as an execution system. Maintainers can label an issue, trigger a plan, approve execution, and let Forge carry out bounded tasks in a controlled environment. It can clone a repository, reason through the request, make targeted edits, validate results, and prepare a branch or patch for human review. Forge also supports native pull request review, so teams can comment `/forge review` on a PR and receive automated feedback grounded in the repository diff and change context. Forge is deployed here: https://149.28.121.155.sslip.io/ For the Vultr prize, Forge is production-shaped and deployment-friendly: containerized, API-ready, and structured to run as a web service with `forge-api` behind a reverse proxy, making it a strong fit for deployment on a Vultr VM. For the Gemini prize, Forge demonstrates multi-step reasoning across real software workflows: understanding issue context, reading repository state, deciding what to change, adapting after tool output, validating results, and evaluating risk. For the Featherless prize, Forge uses an OpenAI-compatible model interface, making it easy to route the same orchestration layer to open-source coding or reasoning models for portability, cost control, and domain tuning. Forge is not just an AI that suggests code. It is an autonomous engineering workflow system that can plan issues, implement changes, review pull requests, validate outputs, and return evidence developers can act on.