Top Builders

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

Kraken REST API

The Kraken REST API provides HTTP-based access to Kraken's spot and futures markets, account data, and trading operations. It covers public market data endpoints (no authentication required) and private endpoints for account management and order execution (HMAC-SHA512 authenticated). The API supports Spot, Futures, Custody, and Embed products, each with separate base URLs.

General
DeveloperKraken (Payward Inc.)
TypeREST API
LicenseCommercial API (free with Kraken account)
Documentationdocs.kraken.com
GitHubkrakenfx/api-go

Core Features

  • Public and private endpoints: public market data requires no authentication; private endpoints use HMAC-SHA512 signing.
  • Multiple product APIs: Spot, Futures, Custody, and Embed each have dedicated endpoints.
  • Tiered rate limiting: per-API-key counter with decay rates based on account tier (Intermediate or Pro).
  • Subaccount support: master accounts can manage subaccounts programmatically.
  • Earn and staking: endpoints for managing yield-generating positions.

Endpoints

Spot REST base URL: https://api.kraken.com/0/

CategoryEndpoints
PublicTicker, OHLC, order book, recent trades, spreads, asset pairs, system status
Private: AccountBalance, trade balance, open/closed orders, trade history, ledger entries
Private: TradingAdd order, amend order, cancel order, cancel all orders, batch orders
Private: FundingDeposit addresses, deposit methods, withdrawal info, withdraw funds
Private: EarnStaking and yield positions

Authentication

Spot REST authentication uses HMAC-SHA512:

  1. Generate a nonce (always-increasing unsigned 64-bit integer; millisecond UNIX timestamps recommended)
  2. Compute: SHA256(nonce + POST body data)
  3. Compute: HMAC-SHA512(URI path + SHA256 result, base64-decoded private key)
  4. Send API-Key header (public key) and API-Sign header (base64-encoded HMAC result)

The private key is never transmitted directly.

Rate Limits (Spot REST)

TierMax CounterDecay Rate
Intermediate200.5 per second
Pro201 per second

Ledger and trade history calls add 4 to the counter; all other private calls add 1. Order placement and cancellation use a separate trading rate limiter. Exceeding limits returns EAPI:Rate limit exceeded. Rate limits are shared across REST, WebSocket, and FIX for the same API key.

Tools and Resources

Ecosystem and Integrations

  • API keys are generated in Kraken account settings with configurable permissions (read-only, trading, funding).
  • Commercial redistribution of Kraken market data requires prior approval from marketdata@kraken.com.
  • Community SDKs available for Python, Go, C++, and Julia (listed in official documentation).

Generate API keys in your Kraken account settings and follow the REST quickstart to place your first programmatic order.

Edit on GitHub

kraken rest api AI technology Hackathon projects

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

ResearchOS

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

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

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

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

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

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.