Top Builders

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

OpenAI's Assistants API

OpenAI's Assistants API simplifies AI integration for developers, eliminating the need for managing conversation histories and providing access to tools like Code Interpreter and Retrieval. The API also allows developers to integrate their own tools, making it a versatile platform for AI assistant development.

General
AuthorOpenAI
DocumentationLink
TypeAI Assistant

Model Overview

The Assistants API enables developers to create AI assistants using OpenAI models and tools. It supports various functionalities such as managing conversation threads, triggering responses, and integrating customized tools.

Assistants API Tutorials

Technology Resources

The Assistants API allows developers to construct AI assistants within their applications. An assistant can leverage models, tools, and knowledge to respond to user queries effectively. Presently supporting Code Interpreter, Retrieval, and Function calling, the API aims to introduce more tools developed by OpenAI while also allowing user-provided tools on the platform.

To explore its capabilities, developers can use the Assistants Playground or follow the integration guide in the official documentation. The integration process involves defining an Assistant, enabling tools, managing conversation threads, and triggering responses.

Edit on GitHub

OpenAI Assistants API AI technology Hackathon projects

Discover innovative solutions crafted with OpenAI Assistants API AI technology, developed by our community members during our engaging hackathons.

Markster Recon: Be the First Call, Not the Fifth

Markster Recon: Be the First Call, Not the Fifth

A rep opens the CRM Monday morning. A target account: "no recent activity." They move on. Meanwhile that same account just posted 40 sales roles, closed a round, and quietly repriced - on the open web, where the CRM never looks. That gap is where pipeline dies. (76% of companies say fewer than half their CRM records are accurate - Validity, 2025.) Markster Recon closes it. Point it at any company and it runs a real pipeline, not a prompt: COLLECT - six Bright Data products fire in parallel: LinkedIn hiring (Web Scraper API), news + funding (Web Unlocker), competitor landscape (Discover API), market results (SERP API), JS-rendered pricing (Browser API), and funding research (Deep Lookup). SOURCE - every datum carries provenance: source URL, timestamp, method. Click any claim and verify it live. Nothing is unattributed. SCORE - confidence is computed (coverage x signal strength), not guessed by a model. SYNTHESIZE - the LLM writes the Account Action Plan: the read, routes in, who shapes the decision, honest evidence gaps, next actions. It writes narrative only - it can never invent a signal. Then the part most projects skip: Recon acts. It writes the decision into a live HubSpot - gtm_* properties, a sourced note on the timeline, and an urgent task where an AI agent executes or prioritizes for the rep. And it polices itself: a thin or low-confidence run is gated to "review only," so a weak signal can never look like an approved action. It is a standing watch on your target list, not a one-time lookup: run the loop on a schedule and you catch the window the day it opens. Judge-testable, no login: any company returns a full plan plus a preview of exactly what hits the CRM. It runs on a real production CRM, and synthesis is provider-portable - Azure OpenAI, AI/ML API, or open-source via Featherless. Built by a team that runs GTM on this exact stack. Live web -> sourced signal -> CRM action. That's the loop.

cis-research-agent

cis-research-agent

Global IT Research Agent is an AI-powered market intelligence tool built for the modern developer and recruiter. It combines Bright Data's real-time web scraping infrastructure with Google Gemini 2.5 Flash to deliver instant, structured insights about the IT job market across 8 countries and 6 languages. Whether you're a CS student in Kazakhstan wondering what skills to learn, a recruiter benchmarking salaries, or a developer exploring opportunities abroad — this agent gives you a live, data-driven answer in seconds. No outdated databases, no guesswork. Just real web data, analyzed by AI, delivered beautifully.

PULSE Platform

PULSE Platform

Long Description (الملخص الطويل المختصر - 180 كلمة) > **PULSE (Real-time Health Monitoring Platform) is an integrated digital health surveillance system that bridges the gap between field diagnostics and centralized data. It connects the DAP mobile app—which uses embedded AI to identify parasite species from microscope images—with a live, web-based epidemiological monitoring platform.** > **To ensure absolute patient confidentiality, PULSE implements strict privacy-first protocols, including SHA-256 cryptographic hashing for patient IDs and location fuzzing (±500m random offset) before storing data in a secure PostgreSQL database.** > **The dual-language (Arabic + English) web platform provides health authorities with an interactive dashboard that auto-refreshes every 2 minutes. It features live map visualizations, color-coded disease markers, and smart outbreak alerts when daily cases cross defined thresholds. Furthermore, it uses linear regression models on 30-day historical data to provide 7-day future epidemic forecasting.** > **A key innovation is its automated WebData Integration via Celery, which automatically scrapes and aggregates global infectious disease alerts from the WHO and ProMED-mail every 6 hours. PULSE empowers health agencies to move from reactive measures to proactive epidemic management.*

TELMED AI DOCTOR

TELMED AI DOCTOR

Telmed AI Doctor is an AI-powered telemedicine platform built to close Africa's healthcare access crisis. Nigeria has only 0.4 doctors per 1,000 people. Millions suffer from treatable conditions simply because qualified medical guidance is out of reach. Telmed deploys AI as the first line of medical response — available 24/7, on any device, at zero cost to the patient. Users interact via text, voice, or image. They describe symptoms naturally or photograph affected areas and receive a structured clinical interpretation, severity classification — Low, Moderate, or Urgent — a personalised medication schedule, and clear escalation triggers indicating when a human doctor is immediately required. The AI engine runs on a multi-model cross-validation architecture. Google Gemini handles primary diagnostic reasoning while DeepSeek independently validates the same input, eliminating hallucination risk on clinical outputs. Google Cloud Vision powers image-based symptom analysis. Google Places connects users to the nearest verified pharmacy to complete the full care pathway. Built on Node.js and Express.js with a vanilla JavaScript frontend optimised for low-bandwidth African mobile networks, every decision was made for real-world deployment conditions. Telmed is not a product. It is infrastructure. The kind Africa should have built a decade ago.

LedgerLens

LedgerLens

LedgerLens is a trust-gated verification layer for autonomous agent commerce. A buyer agent requests web data. A seller agent makes a claim. Before any payment is allowed, LedgerLens uses Bright Data to collect live public-web evidence, then sends the evidence and seller claim through the GEM² Trust Gate. If the claim is grounded, LedgerLens approves an x402-shaped simulated settlement. If the claim is stale, partial, overclaimed, or unsupported, LedgerLens blocks the payment and exports an audit bundle. The demo supports live judge requests, deterministic replay cases, Bright Data evidence receipts, L1/L2 GEM² audit scores, final reports, and read-only audit bundles. LedgerLens targets Track 3: Security & Compliance by solving third-party claim risk before autonomous spend. It also overlaps with Finance & Market Intelligence through supplier/vendor risk, spend policy enforcement, and audit-ready payment records. We simulate settlement for public demo safety: no private keys, no Coinbase account, and no real funds. The payment rail is simulated, but the trust gate is real. No grounded claim, no payment.

Competitor Intelligence Firehose

Competitor Intelligence Firehose

Competitor Intelligence Firehose is an automated competitor monitoring system built for the Web Data UNLOCKED 2026 Hackathon. It monitors four major technology companies (Microsoft, Google, Amazon, Apple) by scraping their public websites and LinkedIn company pages. The project demonstrates real-world business value by calculating Return on Investment (ROI) for automated competitor research. Manual competitor monitoring typically takes 8 hours per week for a business analyst at $75/hour, costing companies $31,200 annually in labor costs. This solution automates that entire process using Bright Data's web infrastructure. The system is configured with a Bright Data API key and $250 in credits, ready to integrate Web Unlocker, Browser API, and Proxy Network products for production-scale deployment. Key features include: - Automatic competitor data collection - Success rate tracking and error handling - JSON report generation for easy integration - ROI calculation showing 5,206% return on investment The architecture is designed to scale from 4 competitors to 100+ by leveraging Bright Data's infrastructure for bypassing CAPTCHAs, rotating IP addresses, and rendering JavaScript-heavy pages. This reduces maintenance overhead from hours per week to zero while providing always-fresh competitor intelligence. Technical implementation uses Python 3 with the requests library, runs on Termux (Android), and is version-controlled on GitHub. The project successfully scrapes 4/4 competitor sites and generates comprehensive JSON reports ready for enterprise analytics pipelines.