Redis

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

Redis

Redis provides access to mutable data structures such as strings, hashes, lists, sets, and sorted sets. These data structures can be manipulated using a variety of commands that are sent over a simple protocol using TCP sockets. Redis also supports various advanced features such as transactions, Lua scripting, pub/sub messaging, and bitmap operations.
The solutions from Redis provide an additional range and capabilities to solutions built on transformer technologies. RediSearch, RedisJson, and other Redis modules allow for building the next generation of AI-Native software solutions.

General
Relese date	April 10, 2009
Author	Redis
Type	in-memory data store

Tutorials

Great tutorials on how to build with Redis!

👉 Discover more Redis Tutorials on lablab.ai

Redis - Helpful Resources

Check it out to become Redis Master!

Redis Redis website
Redis Docs Documentation of Redis
Redis Repo Open-source Redis repository
Redis Cloud Redis Cloud Solution

Redis - Clients

Connect Redis to your Project!

Redis Python Client Python Client for Redis
Redis Node High-Performance Redis Client for Node.js
Redis Go Go Client for Redis
Redis Ruby Ruby Client for Redis

Redis - Boilerplates

Get started with Redis!

Redis x OpenAI - Q&A Project How to utilize Redis to build a Q&A system with OpenAI?
Paper Search App powered by Redis Speed up your research with Redis!
Redis: AI Resources List of projects/tools/ideas for using Redis with AI

Redis - Projects

ChatGPT Memory Allows to scale the ChatGPT API to multiple simultaneous sessions with infinite contextual and adaptive memory powered by GPT and Redis datastore
ChatGPT Retrieval Plugin The ChatGPT Retrieval Plugin repository provides a flexible solution for semantic search and retrieval of personal or organizational documents using natural language queries

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Redis AI technology page Hackathon projects

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

AI Trading Agents Harness by Swiftward

AI Trading Agents Harness is a platform where fundamentally different trading agent architectures share one MCP toolchain and operate under a common risk, identity, and evidence layer. Three pillars: 1. Smarter Agents - three architectures on one harness. Two jailed Claude Code agents: Alpha for momentum trading, and Gamma with five debating sub-agents and self-improving memory. A deterministic Python quant with a 3-stage mathematical brain (market filter, rotation, sizing). A Ruby arena for parallel strategy evaluation. Plus Go, Java, and Rust LLM baselines. Bi-directional Telegram: agents stream output live, operators message mid-session to guide decisions. 2. Trading Platform - 45 MCP tools across 7 servers. Multi-source market data with 7 server-side indicators, alerts, conditional orders with OCO, soft/trailing stops. Persistent per-agent Python sandbox. React 19 dashboard embedded in the trading server. 3. Super Safe - a declarative YAML risk engine with 31 live rules, 668 observed policy violations, graduated tiers, heartbeat kill switches, loss-streak circuit breaker, shadow-mode A/B testing, and eval fixtures. Claude Code agents are fully isolated in Docker with no direct network egress - all traffic forced through three gateways: Internet (domain allowlist), LLM (PG2 + BERT prompt-injection detection), MCP (per-agent tool permissions). Every decision is keccak256 hash-chained (RFC 8785 canonical JSON). ERC-8004 on Sepolia: four agents on the Identity Registry, each backed by an EIP-1271 AgentWallet. Every trade is EIP-712 signed, submitted to the Risk Router, and attested to the Validation Registry as a checkpoint, plus some Reputation Registry scores. Evidence chain is publicly queryable via GET /v1/evidence/{hash}. Kraken: execution via Kraken CLI with per-agent isolation and native stop orders. Bonus: AgentIntel - an independent audit of all 67 agents in the hackathon (7K on-chain trades, $1.5M volume) with AI verdicts and sybil/gaming detection.

ContentCraft AI

Content creators spend 4–6 hours producing a single YouTube video's worth of content across multiple disconnected tools — writing scripts in one place, designing thumbnails in another, researching hashtags somewhere else. ContentCraft AI eliminates that entirely. Built using Complete.dev, ContentCraft AI is a dual-agent AI platform where two specialised agents work in a coordinated pipeline. Agent 1 uses GPT-4 to generate a YouTube-optimised title, full video script with timestamps, SEO-optimised description, and 10 relevant hashtags — all tailored to the creator's brand voice and target audience. Agent 2 then takes that output and uses DALL-E 3 and Pillow to produce 3 thumbnail variants: an AI-generated image, a brand-coloured text design, and a minimalist layout. The result is a downloadable ZIP pack containing everything needed to publish a YouTube video — delivered in under 60 seconds from a single intake form. Target users include solo YouTube creators, social media managers, and small businesses that need consistent, high-quality branded content without a dedicated production team. The backend is built in Python/Flask, with Redis handling agent communication and job state, Docker and Nginx managing production deployment, and a comprehensive async validation suite ensuring production readiness.

ARCC The Universal AI Logistics OS

The Problem: Modern logistics is fragmented. Companies are locked into proprietary ecosystems (Kiva, Locus, etc.), making it impossible to orchestrate mixed fleets of robots from different vendors. Data is siloed, and "smart" fleets are often just running static, brittle rule-sets. The Solution: ARCC (Autonomous Robotics Command Center) is the first Universal Operating System for Logistics. We decoupled the intelligence from the hardware. By acting as a "Headless" Digital Twin, ARCC connects to ANY physics engine (Webots, Gazebo, Nvidia Isaac) or physical robot fleet via a standardized API. Key Innovations: Gemini Brain: We replaced hard-coded logic with a multimodal AI agent that understands context. It doesn't just route robots; it reasons about bottlenecks, battery health, and priority shifts in real-time. Chaos-Resilient: Our "God Mode" chaos engineering engine proves the system can self-heal during network failures, battery spikes, and demand surges. Self-Optimizing: The system learns. After every operational cycle, it analyzes throughput metrics and refines its own strategy weights, becoming more efficient every single day. ARCC isn't just a dashboard; it's the infrastructure layer for the next generation of autonomous supply chains.

Repulsor

Repulsor is an AI-native lifecycle intelligence platform for robotics development teams and businesses. Today, the modern robotics businesses run hundreds to thousands of simulations and validation jobs every week. While CI systems, simulators and cloud platforms execute these runs reliably, teams still interpret results manually. Failures repeat across time, performance drifts subtly or even worst to happen, compute costs scale with increasing volume while the lifecycle decisions remain reactive and fragmented. We built repulsor to exist above this current robotics infrastructure as an intelligence layer that integrates with repositories, observability systems, artifact storage, test results, test case definitions, cloud platforms, etc to normalize execution data across runs. From this structured dataset/context, the system detects recurring behavioral patterns, identifies drift relative to stable baselines, surfaces cost concentration trends and highlights redundant or low-signal validation activity. Rather than acting as another dashboard or analytics engine, Repulsor synthesizes these signals into structured, impact-aware recommendations. Engineers can see what changed, why it matters and what the projected impact would be before taking action. All of these decisions are recorded with traceable context, creating an accountable lifecycle history. The long-term vision is to move robotics development from execution-heavy workflows to intelligence-driven systems, where validation stacks learn from their own history and optimize continuously. Repulsor does not replace simulation platforms or existing tools used currently by the businesses. It sits above them, transforming high-volume execution data into structured lifecycle reasoning and defensible engineering decisions.

FleetMind

Operating robot fleets traditionally requires complex dashboards, rigid scripts, and specialized ROS knowledge. FleetMind democratizes robotics by allowing operators to control multiple agents using simple natural language commands like "Patrol the north sector" or "Inspect the warehouse center." Our core innovation is a Hybrid AI Architecture that solves the gap between high-level reasoning and low-level control: The Brain (Gemini 2.0 Flash): Acts as the strategic commander. It parses vague human intents, resolves ambiguity (e.g., "Go back" implies memory), and outputs precise JSON mission parameters for multiple robots simultaneously. The Reflex (Reinforcement Learning): Local Q-Learning agents run on each robot to handle real-time survival. Robots autonomously decide to abort missions and seek charging stations when battery is critical—demonstrating self-optimization without hard-coded rules. Digital Twin: A high-fidelity 3D simulation (built with Three.js and React Three Fiber) visualizes the fleet in real-time, featuring simulated physics for collision detection and battery drain. FleetMind also integrates Voice Control via the WebSpeech API and simulated Telemetry streams, providing a complete "Mission Control" experience accessible from any web browser.

RAKSHAK - Autonomy Evaluation Framework

RAKSHAK is an evaluation and benchmarking framework that validates autonomous robots before real-world deployment. As autonomous systems enter disaster zones, hospitals, warehouses, food and medicine delivery networks, agricultural pesticide spraying, and public infrastructure, failures are no longer minor bugs — they can result in injuries, recalls, lawsuits, and lost trust. Most autonomy failures occur in edge-case conditions not covered by standard testing. Field validation can cost $50K–$500K per failure iteration. RAKSHAK exposes these failures safely in simulation before deployment risk exists. Built on top of Webots for real-time 3D simulation, RAKSHAK transforms simulation into adversarial validation infrastructure. Instead of testing robots under ideal conditions, it injects 50+ structured chaos scenarios including battery degradation, sensor blackouts, communication loss, environmental hazards, network latency, and multi-agent conflicts derived from real-world robotics failure modes. The platform integrates LLM-driven autonomy using the Gemini API and runs cloud-deployed simulations on Vultr infrastructure with WebSocket-based real-time telemetry. It performs live stress injection and generates a quantified Trust Score (0–100) across safety, resilience, efficiency, communication reliability, and task completion. Example: A delivery drone carrying food or emergency medicine passes obstacle avoidance tests but crashes when battery drops below 20% during evasive maneuvers. RAKSHAK’s structured power-drop scenario exposes this weakness before first flight — preventing potential six-figure losses in hardware, liability, operational downtime, and public trust. This is not just simulation. This is measurable deployment readiness. As autonomous systems scale globally, validation must scale with them. RAKSHAK ensures robots are trusted before they are deployed.