Kraken

Top Builders

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

Kraken

Kraken, operated primarily under the corporate umbrella of Payward Inc., represents a highly evolved shared financial infrastructure layer and digital asset exchange. Founded in 2011, the platform provides unified liquidity, centralized risk management, and a globally compliant infrastructure to serve diverse client segments.

General
Provider / Author	Kraken
Founded	2011
Type	High-Performance Digital Asset Exchange & Financial Infrastructure
Primary Architecture	Polyglot Microservices (Rust gRPC backend, C++ matching engine)
Website	https://www.kraken.com/en-gb/lp/platform
Developer Documentation	https://docs.kraken.com/
Open-Source Repository	https://github.com/krakenfx

Start building with Kraken products

Kraken provides an institutional-grade trading ecosystem capable of sub-2 millisecond round-trip latencies and handling up to 15,000 algorithmic orders per second. The underlying architecture utilizes a highly optimized microservices topology, with the ultra-hot path matching engine engineered in C++ for deterministic execution and the backend primarily built in Rust. Furthermore, Kraken offers developers the "Ink" Layer-2 blockchain, a scaling solution built on the Optimism OP Stack that seamlessly bridges centralized exchange liquidity with decentralized finance (DeFi). We highly encourage you to check out the innovative Web3 apps and algorithmic trading bots created with this technology during lablab.ai hackathons!

Key Platform Features

Polyglot Microservices: Utilizes Rust and the Tokio asynchronous runtime for highly efficient network I/O and routing, while relying on Python (Django) with strict static type checking (mypy) for API layer logic.
Deterministic Latency & Colocation: Offers dedicated colocation services through partnerships with the Beeks Group, allowing institutional traders to physically host their servers in the same data centers as the matching engine.
Dynamic Rate Limiting: Employs a sophisticated mathematical decay model that assigns penalty points based on computational burden and order resting time, rewarding efficient algorithmic behavior and detering malicious spoofing.
Cryptographic Proof of Reserves: Uses a mathematically verifiable Merkle Tree data structure, independently attested by a CPA firm, enabling clients to cryptographically verify their included balances without compromising privacy.
The Ink Layer-2 Blockchain: An optimistic rollup anchored to Ethereum that provides deep integration with Kraken's centralized infrastructure, empowering frictionless transitions to decentralized applications (dApps) without relinquishing private key custody.

Official Links and Resources

Kraken Developer Documentation: The central gateway for algorithmic integration and REST/WebSocket documentation.
Kraken GitHub Organization: The primary open-source repository for the developer community, offering maintained SDKs and tools under the MIT license.
Kraken Engineering Blog: Technical deep dives spanning architecture, performance, and security.

Libraries and SDKs

API Specifications (api-specs): Machine-readable open-source specifications utilizing OpenAPI for REST and AsyncAPI for WebSocket channels.
Golang Implementation (api-go): An officially maintained, highly concurrent Go module designed for rapid interaction with Spot and Derivatives API endpoints.
Python WebSocket Client (kraken-wsclient-py): A native Python client optimized for establishing robust, persistent WebSocket connections for real-time order book feeds.
python-kraken-sdk: A community-driven Python SDK featuring built-in handlers for automated nonce calculation, REST retries, and asynchronous handling of WebSocket feeds.
KrakenTools: A public utility toolkit offering mathematical helper functions for cryptographic payload signing and data structuring.

Boilerplates

Decentralized Web Wallet (wallet): A self-custodial wallet framework written entirely in TypeScript, foundational for Web3 integration.
Futures Trading Bot Template: A deeply documented template demonstrating complex algorithmic execution and advanced error handling.
Freqtrade Integration: An open-source crypto trading bot allowing developers to test and deploy machine learning models and statistical arbitrage strategies live against the Kraken API.

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kraken AI Technologies Hackathon projects

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

ResearchOS

ResearchOS ResearchOS is an autonomous multi-agent research platform designed to accelerate academic and technical research. Instead of acting as a traditional chatbot, ResearchOS coordinates a team of specialist AI agents that work together to perform an end-to-end research investigation. Given a research question, the system: -plans the investigation -retrieves relevant academic papers from arXiv -ranks and analyzes the strongest evidence -synthesizes findings across multiple papers -identifies evidence-grounded research gaps -generates a structured research report -stores completed investigations in notebook memory The platform presents the entire workflow through an interactive interface that visualizes each stage of the investigation, making the research process transparent and easy to follow. Key features include: -Multi-agent orchestration -arXiv evidence retrieval -Cross-paper literature synthesis -AI-generated research reports -Automated research gap discovery -Persistent notebook memory -Interactive investigation timeline -Deterministic fallbacks for reliable execution Although ResearchOS supports any research domain, it was developed with AMD-focused technical research in mind and can investigate topics such as ROCm, MI300X, GPU optimization, and LLM inference.

AI Classroom Edge Intelligence

AI Classroom Edge Intelligence is a privacy-first classroom AI platform built for schools that need useful AI without sending every piece of student information to the cloud. The platform evaluates each task by privacy level, connectivity, and complexity, then routes it to Offline Edge Mode, a Local Classroom Server, or Fireworks AI Cloud Assist. Sensitive or restricted information stays local. Eligible anonymized, high-complexity tasks are sent through a secure Express backend to Fireworks Serverless using Qwen3.7 Plus. The project includes an AMD Model Router, Edge Runtime Monitor, Rural Connectivity Simulator, Privacy and Local Data Ownership Console, Classroom Digital Twin, and teacher approval workflow. Live results display the provider, model, route, privacy classification, latency, safety note, and AI response. API keys remain server-side, and the privacy guard blocks sensitive requests from cloud inference. The project was inspired by rural schools where connectivity can be unreliable and student privacy is critical. Instead of acting as a simple chatbot, it serves as an intelligent routing and decision-support system. Teachers review, edit, approve, or reject recommendations before instructional actions are recorded. The current implementation includes a working browser interface, real backend routing, live Fireworks Serverless integration, server-side key protection, and Docker containerization. Local AMD AI PC inference, GPU/NPU acceleration, device telemetry, and production synchronization are clearly identified as future work. The long-term vision is a school-owned AI platform combining local intelligence, optional cloud reasoning, persistent classroom evidence, and teacher oversight for rural and underserved communities.

Macro Conflict Radar: An Agentic Skill Platform

Crypto markets are influenced by many macro forces at the same time. Liquidity can appear risk-on while interest rates, the dollar, inflation, or volatility indicate risk-off. Traditional dashboards show these indicators separately, and generic AI assistants explain them without reliably proving where their conclusions came from. Macro Conflict Radar identifies these disagreements and turns them into verifiable macro intelligence. The architecture deliberately separates computation from judgment. Finance algorithms compute indicators and conflicts. The knowledge layer connects those results to economic concepts. Agentic skills then assemble the appropriate evidence and reasoning path for each question. The LLM narrates the result only after the claims have been checked. The platform converts raw macro observations into reusable, evidence-backed intelligence objects.

ReproForge Sentinel — Claim-to-Evidence AI

AI systems can generate benchmark claims, model promises, and technical demonstrations faster than teams can verify them. ReproForge Sentinel closes that trust gap by turning an AI/ML claim into structured, inspectable evidence. A user submits a repository URL, the exact claim, a runtime target, and declared security policies. ReproForge evaluates the available claim metadata and policy signals, applies deterministic ShadowGuard risk and reproducibility scoring, records a trace of the evaluation, and produces a Reproducibility Passport. The Passport contains the verdict, evidence chain, missing proof, blocked actions, integrity hashes, security notes, and exportable JSON/PDF results. The hackathon build combines a premium React and TanStack interface with a FastAPI backend, Docker packaging, tests, proof schemas, and AMD/Gemma integration adapters. It also includes an AMD ROCm capture workflow that accepts hardware evidence only when device identity, HIP/ROCm, AMD SMI telemetry, workload metrics, and artifact hashes are successfully captured. Our guided sample is clearly labeled and designed for a reliable judge walkthrough. The current public MVP evaluates submitted claim metadata and declared policies; it does not yet clone or execute arbitrary repositories. Real Fireworks/Gemma inference and direct AMD ROCm telemetry remain explicitly marked pending when verified runtime provenance is unavailable. ReproForge never replaces missing measurements with invented proof. ReproForge is not a “truth machine.” It is an evidence machine: a verification layer for AI teams, security reviewers, researchers, investors, and judges who need to understand what was checked, what passed, what failed, and what still cannot be proven.

Vdcap.AI

Vdcap.AI is a full-stack, automated video captioning platform that generates and compares four distinct caption styles side-by-side. Designed for creators and marketers, the system accepts video uploads and extracts audio and keyframes using FFmpeg and OpenCV. If audio is present, it uses cloud Speech-to-Text APIs Fireworks (Whisper and LLM available on Fireworks AI) for high-fidelity transcription. If the video is mute, the pipeline falls back to visual frame analysis. The transcript and visual context are processed via Fireworks AI using the Llama-3.1-8B-Instruct and GLM models. These generate four tailored caption styles: Formal (professional), Sarcastic (witty), Humorous-Tech (developer-oriented), and Humorous-NonTech (casual). FFmpeg overlay filters burn these captions directly onto separate video streams. On the frontend, a handcrafted dark-mode glassmorphism interface presents the results in a synchronized 4-quadrant player. Seeking, playing, or pausing any video instantly syncs the other three, allowing real-time comparison of subtitle styles. Users can export their captions as individual SRT files, structured JSON data, captioned MP4s, or a complete ZIP package including an interactive HTML summary report. Vdcap.AI also contains a modular pipeline using LoRA adapters for fine-tuning caption models on MSR-VTT and ActivityNet datasets, making it fully ready for custom AI deployment.

PrepAI

This project is an AI-driven interview preparation platform combining agent-based orchestration and web tooling. CrewAI agents (agent1/agent2) run task-driven workflows that use tools for curriculum access, web research, and database storage. A FastAPI backend orchestrates kickoff routes and coordinates asynchronous agent execution. LLM integration is provided via litellm/Fireworks, with provider-specific handling for request compatibility. The frontend (Vite + React) hosts session UIs and authentication, while backend utilities seed and manage the database. Tests cover curriculum, database, and web access components. The system aims to generate targeted practice sessions, quizzes, and evaluative feedback tailored to role, topic, and skill level.