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.

Ferry: Agentic Legacy Modernization

Ferry is an AI-assisted legacy modernization platform that helps teams move old systems into modern codebases with more visibility and control. Instead of handing an entire migration to one model, Ferry coordinates a Band room of specialized agents that each own a clear part of the process: planning, source analysis, business logic extraction, target code generation, database migration, test generation, review, final approval, and GitHub pull request creation. A user points Ferry at a legacy COBOL, Java, or PHP repository. Ferry analyzes the project, translates the application to Go or Rust, prepares MySQL MyISAM to InnoDB migration plans when needed, writes tests against the original behavior, and produces migration artifacts the team can review. The workflow ends in a GitHub pull request, so teams can inspect the diff, tests, and migration plan before anything is merged.

Joint Search

Joint Search is a collaborative, chat-based fact-checking and research platform designed to provide deep, context-aware information retrieval and synthesis. At its core, the project connects a modern React/Vite frontend with a robust Express.js backend (utilizing PostgreSQL for data persistence and MinIO for S3-compatible object storage) to deliver a highly interactive user experience. Users can upload documents to knowledge bases, organize them with file or folder tags, and dynamically reference them inside chat conversations. To answer complex queries, the system integrates a powerful multi-agent orchestration workflow built on the Band framework. This workflow delegates tasks in a structured sequence: The Research Orchestrator manages quality and initiates the pipeline. The Research Planner decomposes the main query into distinct subtopics. Specialist Researcher Agents execute targeted web searches using Perplexity API models. The Medior Agent collects these findings, guides a single-round debate among researchers to resolve discrepancies, and synthesizes a final draft.

Parallax

PARALLAX is a GenAI-native cyber-fraud investigation platform for banks, fraud teams, SOC analysts, and cyber cells responding to APK-led financial crime. It analyzes suspicious Android applications and related fraud evidence to identify banking-malware behavior such as accessibility abuse, overlay attacks, SMS or notification interception, suspicious permissions, network indicators, and runtime compromise signals. PARALLAX converts these findings into structured evidence, confidence-scored claims, risk assessments, and investigation artifacts. The system is designed around an evidence-first workflow: ingest a suspicious APK, run static and dynamic analysis, extract IOCs and observations, snapshot the evidence bundle, coordinate specialist AI agents, surface challenges or disagreements, and produce a final human-reviewable action packet. Instead of treating fraud review as a static report, PARALLAX models it as a live investigation. Specialist agents can assess device compromise, transaction traces, mule-account patterns, evidence quality, liability context, legal evidence requirements, and final recommendations. The result is an auditable reasoning trail that helps teams understand not just what the system concluded, but why. PARALLAX keeps humans in control. Its outputs are designed to support analysts and bank officers with clear evidence, unresolved questions, recommended next steps, and exportable documentation for escalation or cyber-cell reporting.

CorpoAgents

CorpoAgents is an AI organizational operating system that transforms individual AI agents into a collaborative digital workforce. Instead of isolated assistants working independently, agents are deployed into departments such as Finance, HR, Operations, Partnerships, Compliance, and Executive Leadership, where they communicate, coordinate, and solve problems together just like employees in a real company. Users create an organization, deploy AI employees, and manage work through department channels, direct messaging, boardrooms, meetings, tasks, reports, and organizational memory. Agents can discuss issues within their teams, collaborate across departments, escalate decisions to leadership, generate reports, and maintain long-term organizational knowledge. Human leaders remain in control and can participate in conversations, assign work, approve decisions, and guide strategic direction. The platform combines persistent communication, organizational memory, workflow management, and multi-agent collaboration into a single environment. As issues arise, agents investigate, communicate, coordinate actions, and produce recommendations or reports. Over time, the organization develops institutional knowledge and becomes increasingly effective at handling recurring business challenges. CorpoAgents enables businesses to manage an AI workforce rather than individual AI tools, creating a living digital organization capable of continuous collaboration, decision-making, and operational support.

SATRIA: AI Triage & Queue for Indonesia Healthcare

SATRIA (AI-Powered Patient Triage and Queue Routing) is a digital transformation solution designed for Indonesian primary healthcare (Puskesmas and clinics). Utilizing a three-agent cooperative orchestration powered by the Band framework and AIML API (Claude-3.5-Sonnet & Gemini-2.5-Pro), SATRIA automates the intake and clinical evaluation pipeline: 1. Anamnesis Agent (@anamnesis): Guides patients in natural language (Indonesian/English) to collect demographic data (via NIK parsing) and clinical history. 2. Clinical Expert Agent (@clinical-expert): Leverages ChromaDB vector search (RAG) over official Kemenkes clinical guidelines (PNPK) to calculate patient triage priority (P1/P2/P3) and automatically book queues at the nearest facility. 3. Compliance Auditor Agent (@compliance-auditor): Conducts clinical safety checks (drug interactions, allergies) and compiles structured SOAP Electronic Medical Records (EMR) according to PMK No. 24/2022. The system connects an Astro/Tailwind frontend, a high-concurrency FastAPI gateway, and Redis caching. It reduces patient queue times, prevents clinical errors, and relieves administrative burdens from doctors.

Renggo: Live Escalation War Room

Current AI call center agents are fantastic at handling routine queries, but they hit a wall when faced with high-stakes issues that require strict policy adherence, compliance verification, or human authorization. In these scenarios, standalone AIs typically hallucinate or force the caller into a frustrating transfer queue. Renggo: Live Escalation War Room solves this by bridging the ultra-low-latency, full-duplex capabilities of the Renggo voice AI platform with a powerful, asynchronous multi-agent backend using the Band of Agents SDK. When a caller presents a complex issue—such as a sensitive billing dispute—the frontline voice agent dynamically delegates the problem to a backend "War Room" while keeping the caller engaged. Within this room, specialized agents autonomously collaborate: a LangGraph-powered Triage Agent classifies the severity of the case, a Knowledge Agent uses RAG to pull exact company policies, and a Compliance Agent audits the proposed resolution against strict regulations. If an issue is flagged as critical, human supervisors can seamlessly jump into the Band web UI to provide one-click authorization. The most impressive part? All of this complex, multi-agent reasoning happens in mere seconds while the caller is still on the line. Once the War Room reaches a consensus, the frontline voice agent confidently delivers the fully-audited resolution. By seamlessly combining real-time voice with asynchronous agentic workflows, this project redefines the boundaries of what autonomous customer support can safely achieve.