MediaPipe is a cross-platform framework for building multimodal machine learning pipelines, primarily targeting on-device inference. It provides a graph-based execution model where modular 'calculators' process typed 'packets' through directed acyclic graphs. The framework includes a rich library of pre-built calculators for image processing, tensor manipulation, audio analysis, and ML inference, as well as high-level task APIs for vision (face/hand/pose landmarking, image classification, segmentation), text (classification, language detection), audio (classification), and generative AI (LLM inference). The architecture is layered: a core C++ framework (mediapipe/framework/) defines the graph runtime, stream handlers, and packet infrastructure; a calculator library (mediapipe/calculators/) provides reusable processing nodes; and task-specific APIs (mediapipe/tasks/) offer consistent interfaces across C++, Java, Python, iOS, Web, and C. The system relies on TensorFlow Lite (LiteRT) for model execution, OpenCV for image operations, protobuf for configuration, and Abseil for utilities. Key entry points include the Python package (mediapipe/__init__.py), the C++ task APIs (mediapipe/tasks/cc/), and the web TypeScript modules (mediapipe/web/). The codebase is large and actively developed, with ongoing migration from older API patterns to newer ones (api2/api3), and some deprecated components (legacy Python solutions, older calculator interfaces).
ScrapeGraphAI is a Python library that extracts structured data from websites and local documents using LLM-driven graph pipelines. Instead of a traditional web scraper with hardcoded selectors, users define extraction through natural-language prompts and optional Pydantic schemas; the library then executes a directed graph of nodes such as fetching, parsing, chunking, and answer generation. The public API is a set of graph classes (SmartScraperGraph, SearchGraph, JSONScraperGraph, CSVScraperGraph, SpeechGraph, ScriptCreatorGraph, etc.) in scrapegraphai/graphs, each composing reusable nodes from scrapegraphai/nodes and running them on a BaseGraph execution engine. It is consumed programmatically in Python and supports many LLM providers (OpenAI, Ollama, Groq, Mistral, Anthropic, DeepSeek, xAI, NVIDIA, MiniMax) and multiple fetching backends (Playwright, Selenium, Chromium, BrowserBase, Scrape.do). The architecture is a lightweight DAG framework: nodes have typed inputs/outputs, can branch conditionally, iterate, merge results, and accumulate cost/token metadata; BaseGraph walks the graph, handles state, and collects execution information. The repository also contains document loaders, model token/cost registries, prompt templates, telemetry, proxy rotation, screenshot-scraping utilities, a GraphBuilder that constructs graphs from natural language, and integrations such as Burr and Indexify. While the README aggressively promotes a commercial hosted API at scrapegraphai.com, the repository itself is a self-contained open-source library with examples and tests. The codebase is generally well structured and modular, with clear separation between graphs, nodes, prompts, helpers, utils, and models. It shows signs of rapid growth: some typos, duplicated test fixtures, hardcoded model pricing tables, a Python version mismatch between pyproject and Dockerfile, and dependency fragility around LangChain and optional browser/OCR packages. These issues are manageable but give the codebase a medium technical drift. Overall it is an active, useful library for LLM-based extraction, and several utilities are good candidates for reuse outside the project.