LM-Kit.NET is an enterprise-grade toolkit designed for seamlessly integrating generative AI into your .NET applications, fully supporting Windows, Linux, and macOS. Empower your C# and VB.NET projects with a flexible platform that simplifies the creation and orchestration of dynamic AI agents.
Leverage efficient Small Language Models for on‑device inference, reducing computational load, minimizing latency, and enhancing security by processing data locally. Experience the power of Retrieval‑Augmented Generation (RAG) to boost accuracy and relevance, while advanced AI agents simplify complex workflows and accelerate development.
Native SDKs ensure smooth integration and high performance across diverse platforms. With robust support for custom AI agent development and multi‑agent orchestration, LM‑Kit.NET streamlines prototyping, deployment, and scalability—enabling you to build smarter, faster, and more secure solutions trusted by professionals worldwide.
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Gaffa is a comprehensive REST API designed for browser automation, allowing developers to efficiently control authentic, full browsers with just one API call, which removes the complexities of managing headless-browser frameworks, proxies, and scaling infrastructure. By default, it effectively manages JavaScript rendering, ensuring that web pages load precisely as they would for an actual user, and it accommodates a wide array of automation tasks, including web scraping, taking screenshots, exporting content to PDF, transforming pages into clean Markdown suitable for LLMs, infinite-scroll scraping of dynamic websites, filling out forms, capturing complete page screenshots, and archiving content for offline access. Additionally, Gaffa boasts a rotating residential proxy network that guarantees dependable access from various geographic locations, incorporates automatic CAPTCHA handling when necessary, and operates on a credit-based usage model, where costs are determined by actual browser execution time and bandwidth, making scaling and budget management significantly easier. With its robust features and user-friendly design, Gaffa streamlines the browser automation process for developers across different industries.
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Doctly
Doctly.ai serves as a sophisticated AI-driven PDF parser that proficiently retrieves text, tables, figures, and charts from intricate documents, transforming PDFs into organized Markdown suitable for various AI applications or workflows. Its intelligent model selection feature automatically identifies the most effective parsing strategy for each page's complexity, guaranteeing precise outcomes for different document types, ranging from straightforward text-based PDFs to complex multi-column formats that include graphics. Additionally, Doctly produces well-organized Markdown output, which facilitates seamless integration into an array of AI applications. The tool's advanced feature detection capabilities allow it to accurately pinpoint and extract diverse structural components within PDFs, thereby enhancing the content for subsequent utilization. Overall, Doctly.ai provides a user-friendly solution for those in need of efficient PDF data extraction and processing, making it an invaluable asset for professionals dealing with complex document workflows.
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Docling
Docling is a user-friendly, self-sufficient, open-source toolkit licensed under MIT that facilitates the transformation of disorganized documents into structured data, thereby enhancing subsequent document and AI workflows. This versatile tool can interpret a wide array of document types, including PDF, DOCX, PPTX, XLSX, HTML, Markdown, AsciiDoc, CSV, images, audio files, and even scanned documents using any preferred OCR engine. Docling proficiently identifies and processes various elements such as tables, formulas, reading sequences, bounding boxes, headers, footers, images, captions, code snippets, list items, paragraphs, and overall document architecture, which significantly aids in the searchability and integration of the extracted content into AI systems, retrieval-augmented generation, and agent-based applications. Furthermore, it allows for exporting the parsed output in formats like JSON, plain text, Markdown, HTML, and Doctags, thus providing developers with versatile options for their development pipelines and applications. By efficiently organizing and managing components based on reading sequence, Docling breaks down documents into manageable, continuous text segments, optimizing the processing experience.
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