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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 dateApril 10, 2009
AuthorRedis
Typein-memory data store

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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.

SentinelIQ Autonomous Enterprise Intelligence

SentinelIQ Autonomous Enterprise Intelligence

The Autonomous Intelligence Layer for the Real-Time Enterprise Enterprise data is scattered across silos — client metrics in CRM, financials in ERP, partner data in spreadsheets, compliance logs in separate systems. By the time teams connect the dots, the insight is stale and the window to act has closed. SentinelIQ solves this with two autonomous AI agents. The Sentinel Scanner generates its own deep audit missions across six domains — risk, security, compliance , operations and transactions — executing queries, scoring findings with dynamic risk assessment, and automatically deep-diving into critical detections. It doesn't wait to be asked. It hunts. The Natural Language Query Engine lets anyone — from the CEO to an analyst — interrogate the entire data layer in plain English. SentinelIQ's NL2SQL pipeline classifies intent, extracts entities with fuzzy matching, generates precise SQL, and returns results with AI-generated insights and actionable recommendations. Every query can spin up an on-demand dashboard for instant visual exploration. Cross-domain correlation is the core differentiator. SentinelIQ connects patterns that span multiple business functions — linking a revenue decline to a partner channel shift caused by competitive pricing pressure — the kind of insight that takes human analysts weeks to assemble. Everything streams in real-time via server-sent events. Critical findings push to Slack with auto-tagged domain owners. After every scan, an Executive Intelligence Brief delivers an overall risk score, active threat vectors, prioritised actions, and monitoring recommendations — a decision document ready for the C-suite. Built with FastAPI, LangGraph, Gemini LLM, React/TypeScript, and SQLite. Autonomous scanning. Real-time intelligence. Executive-ready output. One platform.

TalentIQ

TalentIQ

TalentIQ transforms fragmented workplace signals into proactive intelligence that helps managers support their teams before problems escalate. The system addresses three critical gaps: burnout detected too late, performance degradation missed, and high-potential talent overlooked. The platform currently uses synthetic demonstration data representing 12 employees across 3 teams, each with 14 days of activity across 24 signals spanning Delivery (task completion, deadlines, velocity), Engagement (meeting load, focus time, after-hours work), Collaboration (cross-team interactions, feedback, support), and Growth (learning, stretch assignments, initiative). The architecture supports real-time integration with Slack for live signal ingestion. The system employs a hybrid intelligence approach that balances deterministic scoring with AI sophistication. Days 1-13 use rule-based weighted formulas ensuring auditability, every score shows exactly which signals contributed and by how much. For the latest day (day 14), when OpenAI is configured, the system sends the full 14-day time series to the AI, which analyzes patterns, trajectories, and cross-signal correlations that fixed rules cannot detect. This produces three composite scores: Burnout Risk (0-100), Performance Health (0-100), and Growth Potential (0-100), with AI-generated rationale explaining key drivers and trajectory. If AI is unavailable, the system gracefully falls back to rule-based scoring, ensuring the platform always functions. OpenAI powers six additional capabilities: insight generation explaining why alerts fired, deep scoring analysis finding hidden risk patterns, time-series analysis with anomaly detection and two-week forecasts, personalized coaching guides that transform data into empathetic conversation starters, dynamic performance reviews generated from actual behavior rather than manager memory, and predictive alerts identifying burnout trajectories before crisis.

Atlas AI

Atlas AI

Atlas AI is an explainable artificial intelligence system designed for trading platforms operating in high-risk financial environments. Today, platforms face three major challenges: financial losses caused by fraud, poor user risk management, and increasing regulatory pressure requiring transparency in automated decisions. Existing solutions often rely on black-box models that lack trust and accountability. Atlas AI addresses this gap by combining real-time risk prediction with explainable AI. The system analyzes user behavior, trading patterns, and risk signals to detect anomalies, predict potential losses, and flag high-risk accounts. For every alert or decision, Atlas AI provides clear, human-readable explanations that can be understood by platform operators, users, and regulators. This approach improves trust, reduces fraud, strengthens compliance, and enables safer, more responsible platform growth. Atlas AI is scalable, API-driven, and designed to integrate seamlessly into platforms like Deriv.

Conversational HR Voice Automation Platform

Conversational HR Voice Automation Platform

Our project is an AI-powered self-service HR platform designed to automate routine HR operations through humanized voice AI agents. The system generates legally compliant contracts, answers employee policy questions in natural conversation, and proactively manages compliance requirements such as visas, certifications, and renewals. By integrating with existing HR systems, the platform handles routine workflows without human intervention while maintaining privacy and human oversight for sensitive decisions. Employees interact with HR through intuitive voice conversations, receiving instant support and updates. This reduces administrative workload, improves employee experience, and allows HR teams to focus on strategic initiatives. Our vision is to create an scalable, intelligent HR ecosystem where operations run autonomously and seamlessly.

Kynet AI

Kynet AI

The Problem We built Kynet because we noticed a huge gap between AI demos and reality. In demos, agents are perfect. In the real world, they are incredibly fragile. An agent might be running a perfect workflow, but the moment it hits a CAPTCHA, a missing API key, or a website layout change, it crashes. It has no way to "unstuck" itself. Our Solution Kynet is a capability network that gives agents a way to pay their way out of problems. We call this the "Escalation Ladder." Streams: First, the agent tries to buy a pre-made Python tool from our marketplace using USDC. Genesis: If no tool exists, the agent uses Gemini to write, test, and deploy its own tool in real-time. Relay: If code fails (like a visual verification task), the agent pays a human via Telegram to solve it. How we built it We used Arc L1 for settlement because the fees ($0.001) make micropayments actually viable. For the agent's financial brain, we used Circle Developer-Controlled Wallets. This was critical because agents need to transact autonomously—they can't wait for a human to sign a transaction in a browser extension. Circle Product Feedback Products used: Circle Developer-Controlled Wallets, USDC, Arc L1 Testnet. Why we chose them: Our users are AI agents running on servers, so we needed a headless wallet setup with no browser pop-ups or manual signing. Circle’s Developer-Controlled Wallets let us execute transfers programmatically based purely on agent logic, which fit our use case perfectly. What worked well: Once set up, wallet-to-wallet transfers were smooth and reliable. Arc’s transaction speed was fast enough to avoid slowing down our agents, and settlement was consistent. What could be improved: The Entity Secret setup caused a lot of friction. We ended up abandoning a few accounts after misconfiguring it, since there was no clear way to reset the secret in the UI. Requiring local scripts for secret generation felt unnecessarily complex.

OmniAgentPay Payment Infrastructure for AI Agent

OmniAgentPay Payment Infrastructure for AI Agent

OmniAgentPay gives AI agents a complete payment infrastructure layer. Think of it as Stripe for AI agents one SDK that lets any autonomous agent handle money safely and instantly. Traditional blockchain SDKs are built for humans with screens and confirmation buttons. Handing an AI agent a private key is a security disaster waiting to happen hallucinations, prompt injection, or simple logic bugs can drain a treasury in seconds. OmniAgentPay solves this. We wrap Circle's developer-controlled USDC wallets in an atomic Safety Kernel that enforces spending rules before any transaction executes. What agents get: Universal pay() Method: One call intelligently routes through direct USDC transfers, x402 pay-per-use APIs, or cross-chain CCTP transfers automatically. Atomic Spending Guards: Budget limits, rate limits, recipient whitelists, and transaction caps enforced atomically, even under high concurrency. Autonomous Cross-Chain: Full Agent-Side CCTP minting to Arc Testnet and other networks no external relayers required. Zero Blockchain Complexity: No ABIs, no gas math, no private keys. Just pay(). We're not building another agent application. We're building the infrastructure any AI agent can plug into to handle money whether it's LangChain, OmniCoreAgent, or a fully custom agent. Built With: Python, Circle Developer-Controlled Wallets, CCTP V2, x402 Protocol, Arc.