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.

Exam Dragon

Eksamilohe is a agentic AI exam-prep platform for 9th and 12th graders preparing for their national exams. Launching in Estonia for the riigieksamid and built pan-EU from the schema up, it gives every student a personal study companion, Lohe (dragon), an orange dragon mascot that progresses through five states as they learn, backed by four specialised AI agents working in concert: a Study Planner that prioritises weak outcomes by exam proximity, a Self-Assessment agent that generates and grades curriculum-aligned questions with pedagogy checks, an Exam-Deadlines tracker that handles multi-jurisdiction calendars, and a Learning-Materials recommender. The platform is genuinely free at the point of use. Every student-facing feature, diagnostic tests, study plan, focus timer, badges, conversational tutor, is deliberately unmetered, with funding coming from ministries, state innovation funds, and strategic sponsors rather than student fees. Each generated test question is tagged with the exact learning outcome it probes (HARNO eristuskiri in Estonia, equivalent frameworks elsewhere), so mastery rolls up per topic and per subject rather than as a single opaque score. Speaking practice runs in real time over the browser's audio API, with Estonian-aware speech-to-text that handles õ/ä/ö/ü properly. The architecture is opinionated for compliance and longevity: a country-agnostic database schema from day one, immutable audit logs for every mutation, and an MCP (Model Context Protocol) gateway that exposes the same typed surface to the web UI, a Telegram bot, and any future AI client. Estonia 2026 is seeded and live in production at eksamilohe.ee; Finland is next, then the Nordics, Baltics, and major Western European systems. Built by Pärle Laigna, a Tallinna 21. Kool student, with her father Alvar Laigna.

MusKent Commerce OS

MusKent is a production-ready autonomous AI system designed to support real commerce operations across revenue, sales, automation, fulfillment, billing, and marketplace workflows. It moves beyond traditional copilots by combining reasoning agents, async execution, tool orchestration, and multimodal input into a unified operating system. At its core, MusKent uses agent-driven decision flows. It continuously evaluates business signals such as revenue performance, marketplace activity, sales trends, and operational state, then determines the next best action using AI. These agents operate within structured workflows, interact with internal tools and external APIs, and execute multi-step tasks while safely degrading to fallback systems when needed. The platform integrates multiple intelligence layers, including AI-powered reasoning for decision-making, specialized compute for ranking and scoring, and voice-based interaction through real-time and batch transcription. This enables a collaborative multi-agent system where different models and providers handle specific tasks like reasoning, analysis, and fallback execution. MusKent is designed for reliability and real-world usage. It supports asynchronous job processing for long-running operations, structured outputs for consistency, provider health awareness, and safe fallback mechanisms to maintain performance even under degraded conditions. From a systems perspective, MusKent delivers intelligent reasoning, agentic workflows, enterprise utility, and multimodal interaction in a single platform. The result is an AI-powered commerce operating system that can analyze, plan, and act across business operations while remaining resilient in production environments.

Vesper — Autonomous Incident Post-mortem Agent

Vesper is an autonomous multi-agent system that eliminates the most painful part of incident management: writing the post-mortem. Engineering teams lose critical institutional knowledge after every incident because post-mortems are manual, slow, and consistently skipped when teams are exhausted. Vesper solves this with a four-agent pipeline that runs automatically the moment an incident is triggered. The Ingestor Agent connects to Slack and Gmail, pulling every signal from incident threads, alerts, and email chains into a unified context. The Transcriber Agent submits war-room call recordings to Speechmatics, which returns a speaker-diarized transcript — identifying who said what, when. The Analyst Agent feeds the full context to Gemini Flash, which reasons across all signals to extract root cause, contributing factors, timeline, impact, and severity score as structured data. The Writer Agent then uses Gemini to compose a complete post-mortem document in Markdown, which is automatically indexed into a pgvector knowledge base for semantic search. The entire pipeline streams live to the frontend via WebSocket — judges and users can watch each agent step complete in real time on the incident detail page. Built on FastAPI, Next.js 15, PostgreSQL with pgvector, Redis, and Celery, Vesper is a production-grade system deployed on Vultr. The frontend features a glassmorphism dark UI with live agent feed, full post-mortem editor with inline editing, audio file upload, knowledge base search, and integration management for Slack, Gmail, and Speechmatics. Featherless provides an optional open-source model toggle — switching the Analyst Agent from Gemini to L

ClauseGuard

ClauseGuard is an enterprise AI governance platform that transforms contract review into a secure, auditable, multi-agent pipeline. Legal and procurement teams upload contracts (PDF, DOCX, TXT) and receive instant risk scoring, clause-level explainability, and a full security audit trail — all enforced by a production-grade AI security proxy. The Problem: Enterprise AI deployments in legal and compliance contexts face dual risks — bad contracts and bad actors. Existing tools lack security observability and are vulnerable to prompt injection, data exfiltration, and role impersonation attacks. The Solution: ClauseGuard deploys five cooperative AI agents behind Lobster Trap, an OpenAI-compatible deep packet inspection proxy that monitors, scores, and blocks malicious LLM interactions in real time: NER Agent (GLiNER zero-shot) extracts parties, obligations, penalties, jurisdictions Analyst Agent (Qwen3 via Fireworks AI) scores risk CRITICAL/HIGH/MEDIUM/LOW Gemini Explainer (Gemini 2.5 Flash) provides clause-level legal reasoning Reporter Agent generates structured risk matrices and action items Lobster Trap Proxy enforces 7 ingress + 3 egress DPI rules, blocks prompt injection, logs all verdicts to a tamper-evident audit trail Security Features: Live red-team console with 6 adversarial attack payloads. In testing, 4 of 6 attacks blocked at proxy layer with zero tokens consumed. Real-time WebSocket audit dashboard shows verdict, ingress risk score, detected intent, and intent mismatch per call. Target Audience: Legal teams, compliance officers, enterprise security teams in finance, healthcare, and legal sectors requiring auditable, governable AI deployments.

Piythia Autonomous Trading Platform

Piythia is an autonomous multi-agent trading and portfolio intelligence platform designed to bring institutional-grade infrastructure, observability, and AI-driven reasoning into modern financial systems. Built around a simulation-first architecture, Piythia enables users to design strategies, validate them in historical environments, monitor risk and regime behavior, and deploy seamlessly into live markets using real-time market intelligence. The platform continuously ingests real-time market data streams to generate regime signals, detect volatility and market structure shifts, monitor portfolio exposure, and drive intelligent allocation decisions across autonomous trading agents. Piythia combines distributed execution systems, portfolio-level analytics, regime-aware allocation logic, and AI-powered workflows to create a production-style operating system for autonomous financial agents. For this hackathon, Piythia integrates Gemini-powered reasoning and AI-assisted workflows for market intelligence, strategy generation, agent commentary, and portfolio analysis while leveraging real-time market telemetry and multi-agent coordination to support intelligent financial decision-making.