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.build

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

Agent_Trap_Evals

The problem: agents rarely fail hard questions. They fail chores. They report "backup verified" because the log says success, while the archive is truncated. These false claims slip out during routine work, and a real shell makes them dangerous. What Receipts does: you give it a real task, like checking a backup or triaging a failing build. A control layer plans the work and runs every action in an isolated Vultr sandbox [gVisor pods on VKE]. The sandbox has no secrets, has time and memory caps, and is destroyed after each task. Every command's output, exit code, and files become a receipt. Before any claim reaches you, a verifier checks it against those receipts: did the agent check the layer that matters, or just the easy one? An unsupported claim is blocked and sent back to be verified, or reported as unverified. You get the answer together with its proof. Containment: [demo moment, e.g. the agent runs rm -rf / and the sandbox absorbs it; the host is untouched]. Measured: we score Receipts with agent-trap-evals, an open-source trap set we built before the hackathon from real agent incidents. Verifier off vs. on: [X/Y vs. X/Y caught], [n] false alarms on honest controls. For: platform and DevOps teams putting agents on operational work, where a confident false "done" costs more than a slow answer. Sandboxes like E2B contain actions but don't check claims. Eval tools score offline but don't stop a bad claim at runtime. Receipts does both, live. [Business model; next steps.] All LLM calls use Vultr Serverless Inference.

DocuPilot AI: Smart Docs & AI Asst.

DocuPilot AI by Tech_Innovators streamlines document processing and knowledge management for businesses. It tackles manual data extraction, inefficient searching, and verification challenges in documents like invoices, contracts, and reports. The platform integrates document extraction, intelligent search, invoice analysis, automatic classification, and human review. Users can upload documents, extract text, query content, and inspect original evidence securely. Key Features: 1. **Intelligent Document Extraction:** Extracts text from PDFs, images, and TXT. 2. **Evidence-Grounded Q&A:** Provides answers with source references and evidence. 3. **Invoice Intelligence:** Extracts invoice data, performs checks, and generates financial reports. 4. **Explainable Document Classification:** Auto-categorizes documents with traceable evidence. 5. **Human-in-the-Loop Verification:** Allows users to correct and approve extracted data. 6. **Private Document Management:** Ensures security with authentication and authorization. 7. **Optional Asynchronous Processing:** Supports background extraction with job tracking. Built with modern web technologies, DocuPilot AI emphasizes trustworthy workflows by linking information to original evidence and enabling human verification. It's ready for hackathon submission and demonstrations; public deployment requires further security validation.

National Fire Sentinel

Nusantara Fire Sentinel is an enterprise mission-critical command center that transforms Indonesia's forest and peatland fire (Karhutla) management from a reactive, multi-billion-dollar firefighting struggle into an autonomous, prescriptive prevention system operating 7 to 14 days before ignition occurs. The system partitions Indonesia's high-risk Peat Hydrological Units (KHG) across Sumatra and Kalimantan into 35,216 hexagonal cells using Uber H3 (Resolution 8). Leveraging an enterprise hybrid multi-cloud architecture, it evaluates probabilistic ignition risks across the entire archipelago in milliseconds using an AMD Instinct MI300X GPU-accelerated XGBoost model (192 GB HBM3), pierces through dense tropical cloud cover and smoke to detect illegal pre-ignition land clearing via Sentinel-1 Synthetic Aperture Radar (SAR), computes optimal zero-waste patrol routes for field ground brigades using Google OR-Tools VRP, and autonomously synthesizes military-grade tactical incident action plans aligned with Indonesian environmental regulation (PP 57/2016) via Google DeepMind Gemini.

AEGIS//GRID

AEGIS//GRID 2.0 is an industrial AI prototype designed to make automated investigation and recovery decisions safer, more explainable, and auditable. The system combines a native C++20 digital twin of six interconnected manufacturing cells with a physics-based anomaly detector. It models thermal behavior, shared coolant flow, and equipment faults, including sensor spoofing and restricted cooling. When an anomaly is detected, a LangGraph multi-agent workflow uses AMD Token Factory hosted inference to investigate potential causes. Pinecone retrieval supplies contextual evidence from 1,235 synthetic plant records and 103 external reference records. AI-generated recovery options are evaluated using native counterfactual simulation and independent safety rules rather than being executed directly. AEGIS//GRID separates intelligence from execution authority. An operator must authorize an incident-specific action through WebAuthn. The independent executor then verifies a short-lived cryptographic capability, checks simulator state and safety constraints, and records an auditable execution receipt. In a verified commissioned simulator demonstration, an authorized safe stop reduced the hottest cell temperature from 42.37°C to 33.45°C over 120 simulated seconds. Simulator evaluation achieved 73% fault-diagnosis recall, 100% precision within the evaluated test set, and 70% thermal-hazard forecasting recall. The project also passed 453 automated checks across Python, C++, Node.js, and Chromium. AEGIS//GRID demonstrates a practical approach to trustworthy industrial autonomy: AI proposes, physics verifies, humans authorize, and independent safeguards control simulator execution. This is a simulator-validated prototype, not a certified production or physical-factory control system.

Colag

Colag is a shared room where a team and its AI coding agents build the same product together. The problem was coding agents are made for one person at one keyboard. The plan gets lost in chat, agents overwrite each other's files, and nobody clearly owns the decision to ship. How Colag works is everyone in the room writes one shared plan and design board with requirements, algorithms, diagrams and drawings. You can connect your own coder, such as Cursor or Claude Code. You can also command Colag's agent team. Atlas drafts the plan and the tasks. Ada designs the architecture. Forge builds the backend and Pixel builds the frontend, each in its own Git workspace so they never overwrite each other. Sentinel runs the tests in a sandbox without internet access and reviews the result. The AMD part: for this hackathon we added a runtime that runs these agents on AMD Token Factory, using DeepSeek V4.1 Flash on AMD GPUs. The agents only see Colag's own tools, so permissions stay exactly the same. We built it for a shared team key: it waits and retries when the rate limit is reached, and it checks every structured answer from the model before using it. A person approves the plan and a person merges the tested code. No agent and no model can do either. The code is open source under the MIT license.

CertGate: mill certificates, a person decides

When steel arrives at a plant, it comes with a mill certificate, an inspection certificate of the EN 10204 3.1 kind. A goods-in or Quality technician must check by hand that it belongs to the batch and that every value meets the purchase specification. CertGate prepares that check. The open model Qwen3.8-27B, served by AMD Token Factory, reads the page images of the certificate; live reading also offers Gemma-4-31B-IT and experimental models. On a digital PDF, a second channel without any model reads the text layer, and any disagreement sends the case to review. Deterministic, versioned rules with exact decimals then propose release, block or review, and a person decides. On 27 evaluated fictitious certificates, in two independent Qwen3.8-27B runs, false conforming was 0 of 18 in both runs. Proposals equal to the expected proposal were 27 of 27 in the implementer run and 26 of 27 in the auditor's independent run. Fields read correctly were 591 of 594 in both runs. Of the 9 cases whose expected proposal is release, 9 of 9 were proposed release in the implementer run and 8 of 9 in the auditor's run. Sent to review without need: 0 of 9 in the implementer run and 1 of 9 in the auditor's run (cg-004, a decimal comma caught by the channel check). In a comparison run on AMD Token Factory, Gemma-4-31B-IT gave false conforming 0 of 18, 26 of 27 as expected and 591 of 594 fields correct. The text-layer parser covers the two fictitious layouts of the dataset, not arbitrary mill certificates. A digital PDF in another layout gets no second channel and goes to review, like a scan: this is safe, but it reduces the saving. Four agent seats built and verified CertGate with Claude Code: an analyst, an implementer, an adversary and an independent auditor, with the expected outcomes committed before any product code, mutation checks at every stage, the auditor's own real evaluation run, and a claims table linking every sentence to its proof. All data is fictitious.