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.

Agents Terminal V3

AGENTS TERMINAL V3 — Self-Improving AI Agent Orchestration Every interaction makes your AI team smarter. Every business on WhatsApp faces the same problem: customers ask the same questions daily: hours, pricing, orders. The chatbot answers some, fails on others, and the gaps repeat tomorrow. It never learns. For African SMEs this is critical, load-shedding kills uptime, no engineering team exists, and WhatsApp is the primary business channel running on a static AI. Agents Terminal V3 implements a compound loop: every customer message flows through n8n (84+ nodes, 6 workflows) → An LLM 7-intent classifier (200ms) → dual capture to Redis Cloud + PostgreSQL → daily artifacts across 4 time windows → 5-step memory loop (episode → lessons → promote → revise → index) → weekly gap analysis → weekly upgrade pipeline (A 7 phases pipeline generating FAQ suggestions, knowledge base entries, prompt refinements, handoff reviews) → human reviews → applies → smarter customer service the following week. Architecture: Docker containers for n8n + PostgreSQL. Redis Cloud for real-time streams. DeepSeek v4-flash for classification. Twilio + Cloudflare for WhatsApp ingress. 16-command orchestrator CLI. 4 JavaScript tools (capture.js, 3lm.js, review.js, promote.js). 3-Layer Memory: episodic, semantic, procedural — file-first, markdown-first. 19 connections. 5 subsystems. 0 broken links. The loop compounds every week. Built with AMD GPU Cloud credits + Fireworks AI for AMD-optimized inference path. Team: AI-Suplex Labs GitHub: github.com/kmagwenzi/Agents-Terminal-V3

ROCm Navigator

ROCm Navigator is an enterprise-grade, autonomous multi-agent platform engineered to break proprietary software lock-in by automating the migration of GPU applications from NVIDIA CUDA to open-source AMD ROCm (HIP). While traditional conversion tools (like regex-based hipify) frequently break on complex unified memory pointers, inline assembly, or cooperative warp-level primitives, ROCm Navigator treats codebase migration as a closed-loop optimization problem. The platform orchestrates a collaborative mesh of specialized, asynchronous AI agents to achieve drop-in runtime parity on AMD Instinct™ Accelerators (such as the CDNA3/MI300X series): Core Scanner Agent: Uses a Tree-Sitter & NetworkX Abstract Syntax Tree (AST) parser to build a structural graph of the codebase, pinpointing hardware-specific CUDA functions and migration bottlenecks. LLM Synthesis Agent: Leverages LangGraph workflows and fine-tuned Gemma LLMs to generate high-performance HIP code translations, preserving memory semantics and kernel-level configurations. Security Audit Agent: Scans translated assets post-migration for configuration vulnerabilities, secrets leakage, or potential security vulnerabilities. Sandbox Runtime Agent: Spins up hardware-isolated Docker compilation and testing containers. It attempts to compile the translated HIP code, feeds compile errors directly back into the LLM synthesis loop for automated self-correction, and benchmarks the resulting binaries on target AMD GPUs. The entire ecosystem is monitored through a sleek Next.js dashboard utilizing a responsive, glassmorphic dark-mode design. Through the interface, users can visualize real-time agent telemetry, review interactive side-by-side git diffs of the code translation, track compilation feedback, and monitor GPU benchmarking performance. By turning manual migration months into automated minutes, ROCm Navigator bridges the gap between raw syntax porting and instant operational deployment on AMD hardware.

Cyrus: Autonomous Space Weather Defense System

Space weather can disrupt satellites, aviation, power grids, navigation, and radio communications. Solar flares can cause HF radio blackouts within minutes, while coronal mass ejections and high speed solar wind streams can trigger geomagnetic storms that threaten critical infrastructure. Cyrus transforms space weather forecasting from passive monitoring into coordinated operational response. At its core is a Surya inference service optimized to run on AMD GPUs with ROCm. Rather than only running the foundation model, we configured Surya to execute all four downstream tasks within a unified service: solar flare forecasting, active region segmentation, EUV spectral prediction, and solar wind forecasting. Using NASA Solar Dynamics Observatory observations, the service performs accelerated inference, combines the outputs into a complete solar threat picture, and publishes forecasts through RabbitMQ for autonomous processing. LangGraph agents powered by Fireworks AI consume these forecasts and connect to the Cyrus Command Center registry of satellites, flights, and power grid assets. Using operational tools, agents identify affected infrastructure and execute protective actions. Satellites can be placed into safe mode, vulnerable assets can be prioritized, aviation routes can be adjusted around communication risks, and grid operators can receive mitigation actions during geomagnetic storms. A Commander agent synthesizes all decisions into a single operational briefing. Redis Streams provide real time communication between the intelligence layer and the 3D command dashboard. Operators can monitor solar activity, active regions, flare probability, EUV conditions, solar wind forecasts, affected assets, and agent actions as events evolve. By combining AMD accelerated AI inference, scientific foundation models, event driven architecture, and autonomous agents, Cyrus turns complex space weather data into actionable intelligence for protecting critical infrastructure.

RasoSynthTune: Text-to-Dataset & Fine-Tuning

ML teams fine-tuning models spend immense time hand-curating data or blindly trusting API-generated data that silently poisons training runs with hallucinations. RasoSynthTune solves this bottleneck as an autonomous multi-agent data refinery that converts raw, unstructured documents into verified fine-tuning datasets. The platform ensures quality through a rigorous three-layer verification shield: consensus-based LLM judge scoring, iterative self-refinement where low-scoring samples are dynamically revised using the judge's own critiques, and a Human-in-the-Loop (HITL) manual review API. Furthermore, the system actively truncates hallucinated claims in generated multi-turn dialogues. Crucially, RasoSynthTune provides genuine hardware utilization rather than just standard API routing. Heavy multi-agent workflows and dynamic inference routing natively prioritize Fireworks AI, executing Google Gemma models (gemma2-9b-it and gemma2-27b-it) directly on AMD Instinct GPUs. The platform takes this further with an on-metal LoRA distillation loop, utilizing ROCm to fine-tune a smaller model directly on AMD hardware using the pipeline's own rigorously verified output data. Built for enterprise scale, the entire architecture—featuring a LangGraph multi-agent engine, FastAPI router, PostgreSQL, Redis, Qdrant vector storage, and a React/Next.js visual workflow studio—is fully containerized and features OpenTelemetry and Prometheus observability. It is engineered to deploy instantly on the AMD Developer Cloud, delivering transparent datasets (JSONL, Parquet) complete with hardware and model provenance cards.

CyLnk Learn

By 2026, the world faces a 4.8M cybersecurity workforce gap. Traditional training fails: passive videos and certification mills produce paper engineers, not defenders. Meanwhile, 95% of cyberattacks target SMEs that can't afford $200K/year experts. Introducing CyLnk Learn: an AI-powered adaptive platform that fixes this by combining three pillars: 1. Probabilistic Mastery: Bayesian Knowledge Tracing (BKT) + SM-2 models every skill as a probability. Every next step is chosen by math, not a fixed table. 2. Vision-Aware AI Tutor: A LangGraph state machine over a RAG corpus. Paste a screenshot of a failing terminal, and our multimodal AI instantly diagnoses the exploit. 3. Live Sandboxed Labs: On-demand Docker/Podman exploit environments, isolated per learner, auto-graded, and evicted after 30 mins idle. Powered by AMD & Fireworks AI: We use a hybrid inference architecture running natively on AMD Instinct GPUs. We run lightweight Gemma models locally via AMD ROCm for zero data leakage, and route complex tasks through the Fireworks AI API. We implement smart model routing: Gemma 4B for intent, 12B for pedagogy, and 31B for vision analysis. This "right tool for the job" approach maximizes intelligence while minimizing inference costs. Market & Mission: Our beachhead is B2C learners in developing nations (Bangladesh, Nigeria, Vietnam) and under-defended SMEs globally. By making high-quality, personalized cybersecurity education affordable, we turn motivated learners into job-ready defenders, creating a remote talent pipeline for SMEs that desperately need them. CyLnk Learn isn't just a hackathon project; it's a battle-tested, validated startup (Winner of Infinity AI BuildFest 2026, Runner-Up Technopreneurship 2026). Learn by exploiting. Not by watching.

VeritasCore

VeritasCore is a real-time AI auditing engine that runs entirely on AMD Instinct MI300X via ROCm — no external inference API, no vendor lock-in, no per-token billing eating into the audit budget. Six autonomous LangGraph agents continuously red-team a deployed model: a Triage agent fingerprints the target, then parallel Bias, Adversarial, and Drift agents fire a library of 60+ adversarial probes — paired demographic-parity tests, prompt injection, encoding-based jailbreaks — all served locally through vLLM on MI300X. A Compliance Mapper cross-references every failure against regulatory clauses. A Meta-Agent tracks whether the probe library itself is losing effectiveness and synthesizes new adversarial cases from recent failures, so test coverage evolves instead of going stale like a static eval suite. The audit concludes with a cryptographically signed (Ed25519) certificate — tamper-proof, verifiable, ready for regulatory submission. What makes this an AMD stack story specifically: probe generation, target-model inference, and the embedding-based bias/drift scoring all run as concurrent vLLM workloads on a single MI300X instance. Running the full six-agent pipeline — including the self-evolving probe factory — at interactive latency on one GPU is only feasible because of MI300X's 192GB HBM3, which keeps the target model, the red-team model, and the embedding model resident simultaneously instead of swapping between them. Unlike static AI-safety scanners that run a fixed checklist once, VeritasCore treats auditing as a continuous, self-improving process — and does it without sending a single prompt to an external API.