Bright Data Scraping Browser

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Bright Data Scraping Browser

The Bright Data Scraping Browser is a cloud-hosted, managed browser environment that developers control using Playwright, Puppeteer, or Selenium. Unlike a self-hosted headless browser, all proxy routing, CAPTCHA solving, browser fingerprinting, and retry logic are handled automatically by Bright Data's infrastructure, so scripts focus on data extraction rather than unblocking.

General
Developer	Bright Data
Type	Managed Cloud Browser API
Protocols	Playwright, Puppeteer, Selenium
Documentation	docs.brightdata.com/browser
Python Boilerplate	brightdata/bright-data-browser-api-python-playwright-project

Core Features

Playwright, Puppeteer, and Selenium compatible: connect your existing scripts to the managed browser via a CDP (Chrome DevTools Protocol) endpoint, no SDK change required.
Built-in proxy rotation: each session is automatically routed through Bright Data's residential or datacenter proxy network.
CAPTCHA auto-solving: CAPTCHAs are solved in the background without script-level intervention.
Browser fingerprint management: browser signatures are rotated and normalised to avoid detection.
JavaScript rendering: full JS execution before data extraction, suitable for SPAs and dynamically loaded content.
Auto-scaling infrastructure: browser instances scale with your request volume; no pool management required.

Tools and Resources

Python Playwright Boilerplate: ready-to-run Python example project connecting Playwright to the Scraping Browser.
API Documentation: endpoint reference and session configuration options.
Control Panel: create browser zones and retrieve CDP connection strings.

Ecosystem and Integrations

Works alongside Bright Data proxy zones for granular proxy type selection per browser session.
Output can feed directly into structured data pipelines, databases, or AI training corpora.
Combined with the Web Unlocker for layered unblocking on particularly difficult targets.
The MCP Server exposes browser automation capabilities to AI agents without requiring direct Playwright scripting.

Connect your Playwright or Puppeteer scripts to the managed browser at brightdata.com/products/scraping-browser or follow the quickstart documentation.

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Bright Data Bright Data Scraping Browser AI technology Hackathon projects

Discover innovative solutions crafted with Bright Data Bright Data Scraping Browser AI technology, developed by our community members during our engaging hackathons.

DSS BACKGROUND HANDSHAKES PROTOCOL

Background handshake authentication vectored to three parameters user contacts, device and location. Eliminating log in authentication errors thus saving a massive amount of energy consumption from error handling processes of the digital grid at the global scale. Not only that it saves energy but also provides users ease of access. No more OTP, no more CAPTCHA challenges, no more friction from logging in saves time for users and prevents them anxiety due to forgotten passwords. It will eliminate third party intervention that usually is the gateway for data breach. Bad actors takes advantage of this vulnerability and could be the avenue for phishing and other scamming schemes.

Council AI: Multi-Agent Decision Intelligence

Council AI is a multi-agent decision-intelligence system built for the AMD Developer Hackathon: ACT II (Team Error 200). Instead of asking a single chatbot for an opinion, Council AI lets you brief a custom panel of AI specialists on any high-stakes question moving to a new city, choosing a tech stack, deciding whether to attend an event and get back a structured, evidence-backed recommendation. Here's how it works: an orchestrator agent reads the query and dynamically invents 3-4 specialist roles suited to that specific decision, rather than picking from a fixed set of categories. In Enhanced mode, a prompt-engineer agent further refines each specialist's brief. Each specialist then runs live web research via the Tavily API and produces an independent report. A debate agent cross-examines all the reports, surfacing where the specialists agree and where they conflict. Finally, a synthesis agent weighs the evidence and the debate to produce a final verdict, an executive summary, and a bottom-line recommendation. The backend is a FastAPI service that streams the whole pipeline to the browser over Server-Sent Events, so users watch the council assemble, research, and argue in real time instead of staring at a loading spinner. The frontend is built with TanStack Start, React 19, and Tailwind CSS 4, with Framer Motion powering the transitions. All reasoning is powered by GPT OSS 20 Instruct served through the Fireworks AI API. Council AI turns a single-shot LLM answer into something closer to how real high-stakes decisions get made: multiple experts, real research, honest disagreement, and a considered final call.

Verification-Driven Token-Efficient Routing Agent

veriroute is one container that detects its task from the input schema and runs the right agent — both built on the same harness philosophy: deterministic routing, code-verified answers, escalate only proven failures. Track 1 — token-efficient router. A local Qwen2.5-1.5B answers sentiment/NER/summarization behind format verifiers, math via program-of-thought (the model writes solve(), a sandbox executes it), codegen via generated self-tests. Only verified failures and factual recall escalate to the best non-thinking model in ALLOWED_MODELS. Guardrails: stub-first atomic output, ALLOWED_MODELS asserted before any network I/O, hard token budget, prompt-prefix prewarming to fit 2-vCPU windows. Measured on a grader-class VM: 4/8 practice tasks answered free, 2,305 tokens total. Track 2 — all-local captioner. ffmpeg frames -> SmolVLM2-500M describes with a cross-frame consistency check -> Gemma 3 4B writes all four caption styles in one few-shot call. Few-shot beat two LoRA fine-tunes (Fireworks SFT llama-8b and our own GPU LoRA) in a blind judged bake-off — we measured, then chose. All 12 example captions ship in genuine distinct styles at 259s/3 clips on worst-case hardware. 100 tests including SIGKILL-resilience; submission journal with prediction-vs-leaderboard tracking in the repo. R&D (dataset distillation, LoRA training runs, bake-off harness): github.com/bogdan-lmk/gemmacap.

Reddit Intelligence Swarm

Reddit Swarm Intelligence is a full-stack, agentic AI system that transforms Reddit into a live, queryable knowledge base. Using BrightData's web scraping infrastructure, it collects posts and comments on-demand across user-defined categories. A multi-agent LangGraph swarm then processes the raw data: a Keyword Extractor identifies the most relevant search terms, a Database Writer archives everything into a structured PostgreSQL schema organized by hierarchical topics, and a Data Synthesizer generates natural-language answers grounded in the scraped content. The Vue.js dashboard offers a rich analytical surface — users can trigger batch scrapes, read AI-generated category summaries, explore topic metrics via bar charts, and dynamically extract subtopics through an on-demand LLM clustering agent that renders the results as an interactive pie chart. Clicking any pie slice fires an intelligent query directly into the Swarm chat, which synthesizes Reddit sentiment and key perspectives on that subtopic in real time.

AI Recruitment Platform

AI Recruitment Platform is a multi-agent system designed to automate and enhance the job discovery process for software and data professionals. The platform continuously collects job postings from multiple sources, including LinkedIn and staff.am, and processes them through a collaborative network of specialized agents. The system is built around a three-agent architecture. The Ingestion Agent is responsible for crawling, collecting, and normalizing job postings from various online sources. Once jobs are gathered, the Scoring Agent evaluates opportunities based on predefined criteria such as role relevance, skills, location, employment type, and other metadata. Finally, the Recommendation Agent analyzes the ranked opportunities and generates personalized recommendations for users. To demonstrate the agent workflow, the platform includes an interactive dashboard that visualizes the entire pipeline from data ingestion to recommendation generation. Users can monitor crawler activity, view job statistics, inspect processed opportunities, and observe how agents collaborate to transform raw job data into actionable recommendations. The project addresses a common challenge faced by job seekers: discovering relevant opportunities quickly across fragmented platforms. By automating job collection and leveraging agent-based processing, the system significantly reduces manual effort while increasing coverage and relevance. Key features include: • Multi-source job crawling and aggregation • Autonomous multi-agent workflow • Job scoring and ranking engine • Recommendation generation pipeline • Real-time dashboard and monitoring • SQLite-based persistence layer • Smart fallback mechanisms for high availability • Streamlit-powered user interface The platform demonstrates how multi-agent systems can be applied to real-world productivity and career development problems, showcasing autonomous collaboration, workflow orchestration, and practical AI-assisted decision-making.

PriceGhost: Dynamic Pricing Forensic Exposé

PriceGhost is a full-stack forensic intelligence platform that detects, measures, and cryptographically proves dynamic geographic pricing discrimination. THE PROBLEM: Corporations silently charge different prices based on your location, device, and browser fingerprint. 78% of consumers report feeling targeted by location-based pricing bias, yet proving it is nearly impossible. HOW IT WORKS: PriceGhost coordinates 10 simultaneous residential proxy scrapes across global coordinates (Mumbai, New York, London, Tokyo, Berlin, Sydney, Lagos, Buenos Aires, Dubai, Singapore) via Bright Data's Web Unlocker API. Each scrape rotates device fingerprints and captures raw HTML payloads. STATISTICAL FORENSICS ENGINE: Four custom mathematical algorithms run natively — Gini Coefficient of Spatial Inequality, Coefficient of Variation, Mann-Whitney U Significance Test (p < 0.05), and GDP Pearson Wealth Correlation — establishing courtroom-ready mathematical proof of pricing discrimination. AI-POWERED PARSING: When standard regex price extraction fails on complex HTML, Featherless AI's hosted Llama-3 model acts as a semantic fallback parser. AI/ML API generates authoritative natural language indictments styled as investigative exposés. COGNITIVE MEMORY: Cognee's semantic graph database indexes every pricing anomaly, enabling live queries against historical precedents to expose long-term corporate discrimination patterns. AUTOMATED ALERTS: TriggerWare webhooks automatically dispatch incident alerts to legal networks when Gini/Pearson indices flag "Severe" exploitation levels. EVIDENCE INTEGRITY: Every scrape result is sealed with SHA-256 cryptographic signatures and timestamp chains, producing immutable evidence packages exportable as courtroom-ready JSON dossiers. BUILT WITH: Next.js 16 (Turbopack), better-sqlite3 (7-table schema with WAL), Recharts composed visualizations, Leaflet dynamic trace maps.