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.

SignalWindow: GTM Intent Radar

GTM teams lose deals because the strongest buying signals never make it into their stack. Churn threads on Reddit, pricing complaints on Hacker News, and switching intent on G2 sit behind bot protection, JavaScript rendering, and rate limits—so RevOps and AEs research manually and still miss the window. SignalWindow is not a chatbot. It is a closed-loop intelligence pipeline built for the Bright Data hackathon. For each watchlist company, Bright Data SERP API discovers what changed (pricing, careers, changelog, community queries). Web Unlocker and Browser API extract blocked pages as markdown with source URLs and timestamps. A deterministic Window Score ranks winnability in Python; AI/ML API only explains why—never inventing the score. Cognee plus Redis remember signals across runs so context compounds. When the score crosses threshold, TriggerWare or Slack delivers an action-ready brief with outreach draft. The Streamlit demo runs end-to-end in demo mode with pre-cached extracts, or live against real Bright Data endpoints. Toggle “Simulate change” to inject a synthetic churn signal, watch the diff fire, score jump, and alert execute on stage—proving Discover → Extract → Score → Reason → Remember → Act in under five minutes. We surface buying intent before it appears in ZoomInfo or Clearbit: evidence-grade, cited, and automated. Bright Data unlocks the web; SignalWindow turns it into GTM action.

GTM Signal Intelligence

The GTM Intelligence Platform transforms raw public web signals into actionable B2B sales intelligence — detecting when companies adopt new enterprise software long before any public announcement. Stage 1 — Signal Collection runs four concurrent collectors: a real-time Certificate Transparency log stream, a multi-source DNS subdomain harvester from six free APIs, a Wayback Machine CDX crawler detecting new integration pages, and a GitHub activity monitor flagging commit spikes and customer mentions. Stage 2 — Bright Data Integration is the backbone of the web data layer. The main pipeline uses the Bright Data REST API (SERP zone) for budget-guarded SERP queries and page rendering. The Bright Data MCP server is used directly via SSE, calling search_engine and scrape_as_markdown tools for live web intelligence. Stage 3 — Parsing & Normalization routes signals through specialized parsers and a VendorFingerprinter with 36+ pre-computed patterns (Salesforce, HubSpot, Stripe, Okta, Snowflake, Datadog) to assign vendor hints and confidence scores. Stage 4 — Correlation Engine groups signals into DealCluster objects using a pandas 30-day rolling window and a NetworkX bipartite graph. A multi-factor scorer assigns each cluster a HIGH, MEDIUM, or LOW confidence tier. Stage 5 — AI Enrichment feeds each cluster into GPT-4o-mini, returning the suspected vendor, deal close date, optimal outreach window, and reasoning. Confidence is blended 60% LLM + 40% Stage 4. Stage 6 — Delivery exposes intelligence through a FastAPI REST API, React 18 dashboard, PostgreSQL storage, email via Resend, and Slack alerts for HIGH-tier deals. Deployed on Render via Docker. Launch Sniper is a companion module detecting unreleased competitor products via WHOIS registrations, USPTO trademark filings, and robots.txt changes — using the Bright Data MCP server for live scraping and search, then generating AI-powered counter-playbooks delivered via email and Slack.

GeniusTrade AI

GeniusTrade AI is a multi-agent AI trading intelligence platform that transforms live web data into real-time market analysis and actionable trading decisions. Built for modern retail and institutional traders, the platform eliminates the need for hours of manual research by automatically collecting, analysing, and interpreting financial information from across the web. The system continuously gathers live market data, including price movements, technical indicators, financial news, macroeconomic updates, sentiment signals, and trading activity from real-time web sources. Instead of presenting raw charts and fragmented information, GeniusTrade AI orchestrates multiple specialised AI agents that work together to generate structured, explainable, and decision-ready market intelligence. Powered by LangGraph, the platform uses a coordinated multi-agent architecture where dedicated AI analysts independently evaluate different aspects of the market, including fundamentals, technical analysis, sentiment, price action, trend strength, and economic conditions. A final trader agent synthesises these insights, resolves conflicting signals, evaluates confidence levels and risk exposure, and delivers a unified trading recommendation with clear reasoning. GeniusTrade AI provides traders with institutional-grade intelligence through an intuitive interface, enabling users to understand market behavior, identify opportunities, and react faster to changing conditions. With real-time web access, explainable AI reasoning, live sentiment tracking, and automated analytical workflows, the platform serves as an always-on AI analyst team, delivering continuously updated trading insights on demand. GeniusTrade AI combines live web intelligence, autonomous AI agents, predictive analytics, and explainable recommendations to help traders interact with financial markets more intelligently, quickly, transparently, and data-driven.

Filing Pulse - Know the Moment It Matters

FilingPulse is an AI agent that monitors company filings and investor-relations pages, detects material changes, and pushes structured alerts with what changed, why it matters, confidence, and source link. Today, teams either refresh SEC filings, investor-relations pages, regulatory portals, and news sources manually — or they pay for expensive enterprise terminals. Smaller teams are priced out, which means they often discover important changes late: a delayed 10-K, an auditor resignation, a revised risk disclosure, a quiet investor-relations page edit, or a regulatory update. FilingPulse solves this by giving users a company watchlist. The agent continuously monitors public filing portals and investor-relations pages, detects whether page content has changed, filters out cosmetic edits, and only escalates materially relevant changes. Each alert is enriched by an LLM and returned in a structured format: what changed why it matters confidence score source URL company history The system is designed around a cost-efficient architecture: Bright Data handles live public web access, including bot-protected and JavaScript-heavy pages; then FilingPulse normalizes the page, hashes the meaningful content, compares it against previous snapshots, and only calls the AI model when a real change is detected. For the demo, FilingPulse monitors a curated watchlist of companies including Super Micro Computer, MicroStrategy, Unilever, Boeing, Carvana, Tesla, Palantir, and JPMorgan Chase. Each company represents a different compliance risk: filing delays, crypto treasury exposure, IR page edits, FAA/DOJ regulatory risk, covenant risk, CEO disclosure risk, contract concentration, or regulatory capital pressure. The result is a live feed of material filing intelligence for teams that cannot afford to find out late.

BrightSenani

BrightSenani is a competitive intelligence command center for GTM, sales, and marketing leaders in fast-moving markets. Teams lose deals when rival pricing, launches, and messaging surface too late, trapped in tabs and spreadsheets that never reach sales on live calls. Add any competitor once and receive a living intelligence profile: pricing and packaging, positioning and product narrative, social sentiment and share of voice, plus launch, press, changelog, and hiring signals that show where rivals invest next. Powered by Bright Data, BrightSenani collects reliably on the modern web: Web Unlocker and SERP for discovery and protected pages, Scraping Browser for JavaScript-heavy pricing and product sites. Public activity becomes structured intelligence you can query and act on. The platform runs a continuous loop: scheduled monitoring, change detection against history, and a GTM Threat Index so leaders see what matters now. When competitors move, AI battlecards give sales objection guidance, marketing counter-plays, and product gap priorities. Exportable GTM briefs and Slack or Discord webhooks push alerts where teams already work. A Strategic Advisor answers natural-language questions over stored intelligence and past runs, with live web search when history or context requires it. Prospect enrichment adds competitive context to target accounts. Market intent radar surfaces buying signals with traceable sources. Briefings aggregate changes and launches across every tracked rival in one feed. From first research to ongoing monitoring, BrightSenani turns public market activity into executive-ready insight and revenue-ready action in one workspace. Built for Track 1 GTM Intelligence, it helps organizations replace reactive research with always-on competitive advantage: see moves early, prioritize threat, and respond with prepared sales, marketing, and product plays across the full competitive set.

ConsumerIQ: Validate Demand Before You Build

ConsumerIQ is a demand validation engine for founders launching physical consumer products. The most expensive founder mistake isn't building badly. It's building something the market never asked for. Studies at CB Insight shows that roughly 43% of founders worldwide fail because their product has no market need, meaning no one actually wants to buy it. Inventory, supplier deposits, packaging, and launch ad budgets all get committed long before a single sale is proven. ConsumerIQ catches that risk before a dollar is spent on production. Founders submit a product concept, category, target market, and audience through a guided onboarding form. ConsumerIQ then maps the category to relevant marketplace and social data sources and scrapes real signals like competitor listings, reviews, complaints, pricing, SERP results, and social trends, using Bright Data's marketplace datasets, SERP API, and scraping browser across Amazon, Walmart, Etsy, and social platforms. The signals feed a hybrid AI pipeline. A local GPU stack runs Llama 3.2 3B for ReAct agent loops, fastembed MiniLM-L12 for embeddings, and Qwen 3.5 0.8B for CJK to EN translation and compliance preprocessing, with higher-order data synthesis to the dashboard powered by the Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled model served via featherless.ai. Signals persist in Postgres with pgvector and a Cognee knowledge graph for semantic memory. A Go ingest service, FastAPI admin layer, Redis queues, and Celery workers (split into inference and scraping pools) orchestrate the end-to-end pipeline behind an NGINX gateway. The output is a founder-ready dashboard across four sections (Market, Demand, Competitors, Launch) plus an interactive agent chat for follow-up questions. The final deliverable is a clear verdict: Build, Pivot, Stop, or Refine. One input. Real market data. One clear decision, built for founders who need an answer, not another dashboard.