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.

MEV Shield: Micropayment-Gated Mempool AI

MEV Shield is a real-time pre-trade intelligence layer for DeFi traders and autonomous agents. It captures live Ethereum mempool transactions from multiple sources (PublicNode, Alchemy, Infura), scoring each pending swap using proprietary microstructure signals — Hawkes process order-flow clustering, Kyle's Lambda price impact, and VPIN toxicity — to detect sandwich attacks and frontrunning risk before execution. Each transaction receives a cliff score (VPIN × LOB depth × gas Z-score) that quantifies extraction probability. Pools are monitored continuously with per-pool intelligence cards showing Hawkes intensity, Kyle's Lambda impact, and next-attack timing estimates. Bot fingerprinting clusters known MEV addresses by behavioral signature. Intelligence is delivered as a metered API where consumers pay $0.01 per alert via Nitrolite ERC-7824 off-chain state channels, eliminating per-query gas costs. Payments are cryptographically signed ECDSA state updates — verifiable without touching the chain. When accumulated payments reach a threshold, the channel closes via a Flashbots MEV-protected transaction and settles autonomously as a real USDC transfer on-chain via Circle Arc developer-controlled wallets, confirmed on ETH-Sepolia. Claude, AIsa.one provides pre-trade risk assessments on every fifth alert, combining quantitative microstructure signals with natural language recommendations covering slippage tolerance, private mempool routing, and sandwich guard configuration. AI agents can autonomously consume and pay for real-time financial intelligence. No subscriptions, no API keys per user, no manual settlement — just a self-sustaining micropayment loop. Live mempool data flows in, off-chain ECDSA payments accumulate, on-chain USDC settles when the threshold is reached. DeFi traders lose an estimated $1-3B annually to MEV extraction. MEV Shield is the pre-trade defense layer that makes institutional-grade microstructure analysis accessible at $0.01 per query.

Flowra

Flowra is a programmable payment agent that enables condition-based fund release. It allows users to define rules—such as time schedules, geographic location, duration of presence, or proof of completion—before a payment is executed. Instead of transferring funds immediately, Flowra introduces a controlled flow where payments are tied to real-world conditions and outcomes. Funds are held securely in escrow and only released when the specified conditions are verified. This reduces reliance on trust between parties and improves overall payment reliability. By enforcing rules at the payment level, Flowra removes the need for manual follow-ups, dispute resolution, or third-party intermediaries. It ensures that payments are aligned with what was actually agreed upon, rather than depending on assumptions after funds have already been sent. This approach is particularly useful in scenarios where outcomes matter. For example, payments can be tied to a worker being physically present at a job site for a defined period, or to a freelancer submitting proof of completed work. Funds can also be structured to unlock gradually over time, making it suitable for recurring payments, allowances, or milestone-based disbursements. In each case, Flowra provides a simple way to define expectations and enforce them automatically. Built on Arc, Flowra combines smart contracts for escrow, backend verification for real-world conditions, and USDC-based settlement for fast and efficient transactions. Smart contracts manage the secure holding and release of funds, while the backend layer verifies inputs such as location, time, and proof submissions. Arc handles transaction execution and settlement, ensuring reliability and scalability across payments. By integrating these components, Flowra creates a system where payments are not just fast, but also conditional, verifiable, and outcome-driven.

P402 Meter: Proof-of-Spend for AI Agents

P402 Meter is Proof-of-Spend infrastructure for AI agents. Today, AI systems consume tokens, call tools, trigger workflows, and create costs, but payments still happen through subscriptions, invoices, and opaque platform billing. P402 Meter changes this model by turning each AI action into a priced, governed, and auditable USDC event on Arc. The demo uses a synthetic healthcare prior authorization workflow. Gemini parses the intake packet, extracts administrative review fields, streams the review process, and records each meaningful unit of AI work into a live payment ledger. Every event has a price, budget status, receipt trail, and settlement reference. The project uses Arc, USDC, Circle Nanopayments, Circle Wallets, Circle Gateway, and x402-style payment logic to show why sub-cent AI commerce becomes practical on Arc. Each action is priced below $0.01, with 50+ transaction events and a margin comparison against Ethereum gas costs and traditional card rails. P402 Meter is not only a healthcare demo. It is a payment primitive for agentic commerce. Agents need budgets. Businesses need audit trails. Developers need monetization beyond subscriptions. P402 Meter gives each AI unit of work a receipt.

Yieldra

Yieldra is a "Liquidity-as-a-Service" protocol built on the Arc Network that powers the H2A (Human-to-Agent) economy. It enables humans to lend idle USDC to autonomous AI agents for high-frequency on-chain strategies. Key Highlights: H2A Economy: Humans earn yield while AI agents execute sub-second arbitrage and market making. Pure Trust Credit: The first 0% collateral credit model for verified agents, replacing traditional over-collateralization with reputation-based lending. Nanopayments: Optimized with Circle & Arc Network to settle transactions as low as $0.0001, breaking the "Gas Wall" for machine autonomy. Proven Scale: Validated with 1,100+ on-chain transactions and 85% capital efficiency during the Arc & Circle Hackathon.

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.