Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

MongoDB: The Developer Data Platform

Get the latest and greatest with MongoDB 6.0! Build faster and smarter with a developer data platform built on the leading modern database. Whether you’re tackling transactional, search, analytics, or mobile use cases, MongoDB has you covered. Plus, enjoy a common query interface and a data model that developers love.

General
PlatformMongoDB
Repositoryhttps://github.com/mongodb/mongo
TypeNoSQL Database

Key features

Build Faster: Ship and iterate 3–5x faster using MongoDB’s document data model. Enjoy a unified query interface for any use case.

Scale Further: Whether it’s your first customer or 20 million users, meet your performance SLAs in any environment.

Safety first: Ensure high availability, protect data integrity, and meet security and compliance standards for mission-critical workloads.

Fully Managed in the Cloud: Start in seconds and scale to millions with MongoDB Atlas, our cloud services. Explore a multi-cloud developer data platform for various use cases, from transactional to analytical.

Mobile Real-Time Data at the Edge: Launch secure mobile apps with native, edge-to-cloud sync and automatic conflict resolution.

Self-Managed Option: Run MongoDB anywhere, from your laptop to your data center.

Community Edition: Our distributed document database is where it all began. Free forever with seamless migration to Atlas.

Enterprise Advanced: For robust features and support, consider the enterprise version.

Built by Developers, for Developers: MongoDB’s document data model maps to how developers think and code.

What You Can Do with MongoDB

It’s a flexible document data model that lets you ship and iterate faster. Enjoy a unified query interface for all use cases. Whether it’s your first customer or 20 million users worldwide, MongoDB ensures performance SLAs in any environment. Easily ensure high availability, protect data integrity, and meet security and compliance standards for your mission-critical workloads. Plus, MongoDB Atlas provides fully managed cloud services, and you can run it anywhere, from your laptop to your data center.

AI Tutorials

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MongoDB AI technology page Hackathon projects

Discover innovative solutions crafted with MongoDB AI technology page, developed by our community members during our engaging hackathons.

MedSync AI Collaborative Crisis Intelligence

MedSync AI Collaborative Crisis Intelligence

TASK 3 — LONG DESCRIPTION Problem Every year, U.S. hospitals face over 150 million emergency department visits and thousands of mass casualty events. When a Level 3 Critical surge strikes — a multi-vehicle accident, an industrial disaster, a pandemic spike — the difference between life and death is measured in minutes. Yet the coordination systems hospitals depend on were designed for a pre-digital era. Incident commanders juggle phone calls, whiteboards, and pagers. Capacity managers refresh spreadsheets. Staffing coordinators text on-call nurses. Resource managers fax mutual aid requests. Compliance officers review binders of regulatory requirements. The result is catastrophic coordination failure: 34% of preventable hospital deaths are attributed to communication breakdowns during emergencies (Joint Commission, 2023) Average surge response time is 47 minutes — 37 minutes longer than best-practice targets $2.1M average cost per mass casualty event in operational inefficiency alone 72% of hospitals report that their Incident Command System breaks down under real pressure EMTALA violations during surges carry $119,942 fines per incident and potential loss of Medicare funding The fundamental problem is not a lack of data — it is a lack of coordinated decision-making under pressure. No single person can simultaneously optimize bed allocation, nurse staffing ratios, ventilator supply chains, and regulatory compliance within a 10-minute window. Solution MedSync AI is a Collaborative Multi-Agent System that brings autonomous, coordinated, AI-driven decision intelligence to hospital emergency response. Unlike a chatbot or a dashboard, MedSync AI deploys 5 specialized AI agents that work together — reading each other's outputs, challenging each other's recommendations, and negotiating until the plan is compliant, optimal, and ready for human approval.

TRiaD

TRiaD

TRiaD (Threat Intelligence & Automated Defense) is an AI-powered cybersecurity incident response platform developed during the Band of Agents Hackathon 2026. The system leverages a collaborative multi-agent architecture to automate the traditionally time-consuming processes of threat analysis, incident triage, intelligence correlation, and compliance reporting. The platform consists of specialized AI agents working together in a coordinated workflow. Incoming security alerts are processed by an ingestion layer, enriched with contextual threat intelligence, and analyzed through semantic similarity searches using a vector database. The analyst agent investigates indicators of compromise, correlates findings with historical incidents and MITRE ATT&CK techniques, and generates actionable insights. A manager agent then compiles compliance-ready incident reports suitable for security operations centers and organizational stakeholders. TRiaD provides a modern web dashboard with real-time updates, interactive alert monitoring, and automated reporting capabilities. By combining FastAPI, Next.js, ChromaDB, Gemini-powered reasoning, and WebSocket-based communication, the platform demonstrates how autonomous AI agents can significantly accelerate cyber defense operations while maintaining transparency, traceability, and auditability. The project showcases practical applications of agentic AI in cybersecurity, threat intelligence automation, incident response orchestration, and security analytics.

WASI: Multi-Agent WhatsApp Food Ordering SaaS

WASI: Multi-Agent WhatsApp Food Ordering SaaS

WASI revolutionizes the fast-food ordering experience by transforming WhatsApp into an intelligent, seamless conversational storefront, deeply integrated with and monitored by Band AI Cloud. Traditional chatbots rely on rigid decision trees, but WASI utilizes an advanced LangGraph multi-agent state machine powered by Groq’s lightning-fast Llama-3-70B model. The entire conversational pipeline relies on Band AI Cloud telemetry for enterprise-grade observability. When a customer messages the restaurant in Roman-Urdu, Band AI tracks the Supervisor Agent as it dynamically routes the conversation through specialized sub-agents: Menu, Delivery, Payment, and Profile. These agents natively parse complex natural language into strictly typed JSON objects, dynamically updating the customer's cart, handling size variants, and calculating subtotals—all while streaming execution events back to the Band AI dashboard in real-time. To handle edge cases that AI cannot solve alone, WASI features a "Human-in-the-Loop" Receptionist Dashboard built with React and Vite. If an address is invalid or an item goes out of stock, a human receptionist can reject the order with a note. WASI’s AI instantly interprets this feedback, alters the internal database state, and re-engages the customer to fix the issue. Powered by Band AI's robust agent infrastructure and strict asynchronous locks to prevent race conditions, WASI is a production-ready SaaS template designed to bring powerful AI to local restaurants.

Weld AI - Autonomous Multi-Agent Radiography

Weld AI - Autonomous Multi-Agent Radiography

📌 Overview In regulated industries like oil & gas, piping, power, and manufacturing, weld structural failures can lead to catastrophic accidents. Quality assurance relies on Non-Destructive Testing (NDT) radiography, a manual, slow, and error-prone process. The Weld NDT AI Inspector transforms this workflow into a highly reliable, autonomous, and auditable multi-agent pipeline. The system is architected using Hexagonal (Ports & Adapters) design principles to keep core business rules independent of ML frameworks and databases, running securely on Google Cloud Platform (GCP) and MongoDB Atlas. 👥 The Multi-Agent Collaboration Room Inside a distributed Band.ai room, four remote agents work in sequence to ensure safe and compliant decision-making: Weld Orchestrator Agent: Directs the room flow, contrast-enhances the raw scan with CLAHE, dispatches payloads, and logs tamper-evident audit events. Weld Vision Agent: Deploys a fine-tuned computer vision model to locate and classify physical defects (e.g. porosity, slag, cracks) on grey-scale film. Weld Compliance Agent: Leverages a rules engine and Gemini to cross-reference dimensions against regulatory standard tolerances (ASME B31.3, API 1104, AWS D1.1). Weld Review Agent (Track 3 Core): A strict safety auditor that enforces mandatory overrides (e.g., any crack = mandatory reject, regardless of size) and routes ambiguous cases to an ESCALATE status for human Level III inspection. 💾 Enterprise-Grade Resiliency Dual-Database Adapter: Saves inspection records to MongoDB Atlas with automatic fallback to local SQLite storage to prevent downtime in offline industrial fields. Inference Caching: Indexes scans via cryptographic SHA-256 hashes, returning cached defect coordinates on duplicates to prevent redundant GPU overhead. Vertex AI Integration: Authenticates securely using GCP service account IAM policies, bypassing the need for static API keys.

Exertus Autonomous SOC

Exertus Autonomous SOC

The majority of SOC solutions require expensive cloud subscriptions, or the critical security data is sent to third parties, and therefore pose a risk from the moment a security alert is sent off the network. This project tackles both these problems, with an on-premises SOC platform, which uses five specialized local LLMs running on Ollama, without sending any data to a Cloud platform. All data is by design kept locally. Incidents, IOCs, Investigation findings and Agent outputs are stored in a local SQLite database and will NOT be sent to any third party AI provider. LLM inference is performed through the use of Ollama meaning no breach details, threat data ever leave the network to be analyzed. Third-party API keys are encrypted at rest, and are never accessible in the API beyond a boolean flag representing the presence of a key. With JWT based authentication and role based access control, only authorized user can access or update data and each mutation can be tracked by an audit trail, including the user, action, time, and outcome. An incident passed through 5 agents pipeline when ingested. Severity and threat category assigned by the Triage Agent. The Threat Intelligence Agent enhances IOCs from multiple providers (VirusTotal, AbuseIPDB, AlienVault OTX, MISP), adds a risk score and correlates results with MITRE ATT&CK techniques. The Investigation Agent is cross referencing the timeline and IOCs to determine the potential attack chains and impact. The Response Agent generates prioritized containment, eradication and recovery actions which are then approved by a human before being executed. All of this is then presented to the board in the Executive Summary Agent, including severity charts and MTTR metrics. It also includes a Detection Engineering module for SIGMA, YARA, Suricata and Wazuh rules management, a live dashboard of incidents, timelines, agent outputs and approvals, which allows for quick and automated responses without compromising data security.

InnerOS: Sovereign Multi-Agent IT Field Swarm

InnerOS: Sovereign Multi-Agent IT Field Swarm

InnerOS is a sovereign enterprise multi-agent swarm designed to automate critical IT field operations and diagnostic reporting, maximizing local data privacy. Operating within the Band network layer as its collaborative communication bus, the system registers 4 specialized external agents: Router, Memory Retrieval, Analyst, and Document Maker. When an operational query is received, the Router intercepts the request and handles internal orchestration via open-source LLMs hosted serverless on Featherless AI. The Memory Retrieval agent then queries a local MongoDB MCP enterprise database to unearth historical context and technical logs without leaking raw corporate information to the internet. Next, the Technical Analyst ingests this retrieved context, using advanced reasoning models via AIML API (DeepSeek-R1) to run root-cause diagnostics and evaluate infrastructure failures. Finally, the Document Maker synthesizes the analytical insights into an enterprise-grade, production-ready Executive Summary and SOP in English, automatically triggering a live webhook notification that dispatches the finalized report directly to the administrator's WhatsApp via Evolution API. By combining decentralized network coordination with a hybrid local-inference pipeline, InnerOS proves that enterprise automation can be fast, cost-effective, compliant, and strictly sovereign. Online Link: https://sworn-profusely-alongside.ngrok-free.dev/