Bright Data Web Scraper API

The Bright Data Web Scraper API is a cloud-based data extraction service that delivers structured data from over 120 popular websites without requiring proxy management or anti-bot handling code. Developers send a target URL or platform identifier and receive clean, structured JSON in return, with Bright Data handling unblocking, rendering, and parsing behind the scenes.

General
Developer	Bright Data
Type	Managed Web Scraping API
Sites Supported	120+
Ready-made Scrapers	600+
Documentation	docs.brightdata.com/scraping
GitHub	brightdata/sdk-python

Core Features

600+ ready-made scrapers: pre-built extractors for Amazon, LinkedIn, Instagram, TikTok, Zillow, and 115+ other sites, maintained by Bright Data.
Automatic unblocking: built-in proxy rotation, CAPTCHA solving, and fingerprint management so scrapers do not get blocked.
JavaScript rendering: pages requiring JS execution are handled server-side before data extraction.
Pay-per-result pricing: charges apply only to successful responses, not failed or blocked requests.
Structured JSON output: data returned in clean, schema-consistent JSON without HTML parsing.
Cloud scaling: no infrastructure to manage; requests scale automatically with demand.

Scraper Studio

Scraper Studio is an AI-powered scraper builder inside the Bright Data platform. Developers provide a URL and a description of the data they need, and the studio generates and deploys a working scraper. Self-Healing mode automatically updates scrapers when target sites change their structure, reducing maintenance overhead.

Tools and Resources

Python SDK: call the Web Scraper API from Python with async and sync support.
JavaScript SDK: Node.js, Bun, and Deno compatible client.
Bright Data CLI: scrape and extract data from 40+ platforms directly in the terminal.
API Reference: full endpoint documentation and payload schemas.
Scraper Studio: build and deploy custom scrapers in the browser without writing scraping logic.

Ecosystem and Integrations

Integrates with Bright Data proxy networks for combined infrastructure and extraction.
Available via the Azure Marketplace as a managed SaaS product.
Works alongside the MCP Server to expose scraping capabilities to Claude, Cursor, and other AI agents.
Data output can be piped into databases, data warehouses, or AI training pipelines.

Start extracting data in minutes at brightdata.com/products/web-scraper or follow the quickstart guide to make your first API call.

Agent ai contents generation

Agent AI Contents Generation is a multi-step AI agent that turns a short brief into ready-to-publish content. The user provides a topic, target audience, tone of voice and desired channels (blog, LinkedIn, X, newsletter). The agent then plans the content, gathers relevant context through web search, and generates drafts for each channel. The system is built as a pipeline of specialised agents: a Planner that breaks the brief into tasks, a Researcher that collects up-to-date facts, a Writer that produces the draft, and an Editor that reviews it for clarity, factual consistency, style and length limits. If the Editor finds issues, the draft goes back for revision until the quality criteria are met. Our goal is to save marketers, founders and creators hours of manual work while keeping a consistent brand voice. Users can adjust tone, length and format, regenerate individual sections, and export the results. In the future we plan to add image generation, post scheduling and analytics-driven optimisation of content.

HandSpeak

SignBridge is a real-time AI-powered sign language interpretation system designed to make communication more accessible between sign language users and people who do not understand sign language. Using a camera, the system detects hand and body movements, analyzes gesture sequences, and converts recognized signs into natural-language text and speech. The project will leverage AMD GPU acceleration to improve real-time computer vision and AI inference performance, allowing the system to process video with low latency. Beyond individual signs, SignBridge aims to recognize sequences of gestures and reconstruct meaningful phrases, making communication more natural. The project combines accessibility, computer vision, AI, and high-performance AMD computing to create a practical assistive technology solution.

DevFolio AI

Many developers, especially self-taught and student developers, have real skills and solid projects sitting in their GitHub repos, but no polished way to present them. Scholarship committees, recruiters, and hackathon judges rarely have time to open and read through every repository, so good work often goes unnoticed simply because it isn't presented well. DevFolio AI solves this by turning any public GitHub username into an instant, professional portfolio website. The user enters a GitHub username, and the app fetches their public repositories, programming languages, and README files directly from the GitHub API. It then aggregates this data into a clean profile view: a header with the user's avatar and stats, a "Top Skills" section showing languages ranked by actual code volume, and a project grid where each repo is displayed with its README summary, language tags, star/fork counts, and a direct link back to GitHub. The target users are developers who want a fast, credible way to show their work — students applying for scholarships or internships, freelancers pitching clients, and hackathon participants who need a quick professional presence. Unlike a static personal website that has to be built and maintained by hand, DevFolio AI requires zero setup: no sign-up, no configuration, just a username and one click. What makes it unique is that it is entirely generative and live — the portfolio is built fresh from real, current GitHub data every time, so it's never out of date. I also built and tested this on my own GitHub profile as a self-taught developer from Pakistan, and it immediately gave me a much stronger way to present my own project history for my own scholarship applications, proving the tool solves a genuine, personally-felt problem.

Flowback: AI-Powered Work-Context Recovery System

Flowback is an AI-powered Work-Context Recovery System built by HackMantra to solve a problem that traditional browser history does not address: losing the mental context of work after an interruption. Modern work is fragmented across documentation, code, research, AI tools, GitHub, and communication tabs. When an interruption happens, users often remember the website they were on but not what they were doing, what they already tried, where they stopped, or what they should do next. Flowback observes meaningful browser context and detects interruptions, then reconstructs a structured recovery capsule containing what the user was working on, what they tried or referenced, where they left off, and a recommended next step. The user can resume the original tab and scroll position with one action. The current MVP is a Chrome Manifest V3 extension with background monitoring, context extraction, deterministic recovery logic, a recovery UI, resume functionality, and optional AI enhancement. AI is treated as an enhancement rather than a single point of failure, with deterministic fallback when AI or backend services are unavailable. Our architecture is designed for accelerated AI inference and future deployment on AMD infrastructure. During the hackathon, HackMantra aims to experiment with AMD GPU acceleration, optimized local/hosted inference, and benchmarking to determine where AMD compute provides the greatest benefit to Flowback's recovery intelligence. Flowback's long-term direction is to evolve from browser recovery into cross-device work continuity, helping users preserve work context across desktop and mobile environments. Team: HackMantra.

Fresno LUX: A Living City

Fresno LUX is a prototype for a living intelligence layer across an entire city. Fresno already contains enormous amounts of intelligence: residents know where problems exist, nonprofits know where help is needed, businesses know where opportunities exist, educators understand gaps in learning, researchers generate knowledge, and government maintains critical infrastructure and data. The problem is that much of this intelligence is fragmented across disconnected systems. Fresno LUX connects those pieces. The platform creates a shared city graph where people, organizations, resources, needs, projects, observations, experiments, and outcomes become connected nodes. Instead of simply collecting data, LUX continuously asks: What is happening? What do we know? What is missing? What can we test? What changed? A community problem can become an observable signal. That signal can become an experiment. The experiment produces evidence. Evidence changes the system's understanding. That updated understanding informs the next action. Observe → Connect → Understand → Act → Measure → Learn → Converge. The goal is to move Fresno from disconnected information toward collective intelligence—a system where knowledge can travel across boundaries and successful solutions can be discovered, tested, measured, and replicated. LUX is designed to make the invisible connections visible: the relationship between a neighborhood and its resources, between a student and educational opportunity, between a business and workforce capacity, between environmental conditions and community health, and between an idea and measurable real-world impact. This hackathon prototype demonstrates the beginning of a Living Fresno Graph: a city that can observe itself, learn from evidence, and coordinate toward better outcomes.

OnRecord - verificarion for spoken claims

Problem statement: In founder pitches, vendor demos, and partner calls, people make confident, specific, checkable claims — market sizes, competitor pricing, "we're the only ones who do X." Almost none of it is verified before a decision is made. Verification is manual and slow: listen back, work out which claims are checkable, open twenty tabs, then try to remember whether this person said something different eight months ago. The evidence is public. The bottleneck is that nobody can traverse it fast enough. Target user: Analysts, angel investors, and operators evaluating founder pitches, vendor demos, and partner calls. Adjacent: journalists and podcast producers doing pre-publication verification, enterprise vendor diligence, and comms teams reviewing their own executives. How native.builder was used: Built end to end in native.builder. Product Architect scoped the PRD, data model, and Edge Function architecture before any code was written — it surfaced four design problems in my brief, including an ambiguity about which Bright Data product the pipeline needed. Task Planner produced the dependency graph. Builder agent generated the React application, Supabase schema, and Edge Functions across ten incremental prompts, and diagnosed a stale Vite dependency cache without intervention. The UI, data model, verdict logic, and contradiction detection are complete and demonstrated on fixture data. The Speechmatics transcription Edge Function is built and deployed; the multipart submission format is still being resolved. Tools: native.builder, Supabase (Postgres, Storage, Edge Functions, Realtime), Speechmatics Batch API, Bright Data (SERP API, Web Unlocker), AI/ML API, React, Vite, Tailwind.

Bright Data Web Scraper API

Core Features

Scraper Studio

Tools and Resources

Ecosystem and Integrations