Agent Script provides a declarative scripting layer over large language models, enabling you to write YAML or JSON scripts that define agent workflows, tool calls, and memory usage. You can plug in OpenAI, local LLMs, or other providers, connect external APIs as tools, and configure long-term memory backends. The framework handles context management, asynchronous execution, and detailed logging out of the box. With minimal code, you can prototype chatbots, RPA workflows, data extraction agents, or custom control loops, making it easy to build, test, and deploy AI-powered automations.

Agentube AI Agent is a web-based platform that empowers businesses and developers to create AI-driven agents without code. It offers drag-and-drop conversation flows, memory management, analytics dashboards, and seamless API integrations. Agents can handle customer support, lead qualification, scheduling, and data retrieval tasks. Built on Vercel, it supports real-time updates, collaborative editing, and one-click deployments to web widgets, Telegram, WhatsApp, or custom endpoints.

Browse AI is a no-code web data extraction and monitoring platform. It allows users to convert any website into an API, and extract data into spreadsheets with ease. Users can monitor web pages for changes and update their data automatically. The platform offers prebuilt robots for specialized tasks, making it an accessible solution for users with no programming skills.

Cinnamon AI develops AI solutions aimed at automating data extraction from unstructured documents, such as invoices, contracts, and forms. Their technology eliminates the need for manual data entry, increasing business efficiency and accuracy. Cinnamon AI's products are designed to serve various industries, including finance and technology, by employing advanced document processing and generative AI models.

Deep-Research-With-Web-Scraping-by-LLM-And-AI-Agent is designed to automate the end-to-end research workflow by combining web scraping techniques with large language model capabilities. Users define target domains, specify URL patterns or search queries, and set parsing rules using BeautifulSoup or similar libraries. The framework orchestrates HTTP requests to extract raw text, tables, or metadata, then feeds the retrieved content into an LLM for tasks such as summarization, topic clustering, Q&A, or data normalization. It supports iterative loops where LLM outputs guide subsequent scraping tasks, enabling deep dives into related sources. With built-in caching, error handling, and configurable prompt templates, this agent streamlines comprehensive information gathering, making it ideal for academic literature reviews, competitive intelligence, and market research automation.

Labs is an open-source, embeddable domain-specific language designed for defining and executing AI agents using large language models. It provides constructs to declare prompts, manage context, conditionally branch, and integrate external tools (e.g., databases, APIs). With Labs, developers describe agent workflows as code, orchestrating multi-step tasks like data retrieval, analysis, and generation. The framework compiles DSL scripts into executable pipelines that can be run locally or in production. Labs supports interactive REPL, command-line tooling, and integrates with standard LLM providers. Its modular architecture allows easy extension with custom functions and utilities, promoting rapid prototyping and maintainable agent development. The lightweight runtime ensures low overhead and seamless embedding in existing applications.

LLMFlow provides a declarative way to design, test, and deploy complex language model workflows. Developers create Nodes which represent prompts or actions, then chain them into Flows that can branch based on conditions or external tool outputs. Built-in memory management tracks context between steps, while adapters enable seamless integration with OpenAI, Hugging Face, and others. Extend functionality via plugins for custom tools or data sources. Execute Flows locally, in containers, or as serverless functions. Use cases include creating conversational agents, automated report generation, and data extraction pipelines—all with transparent execution and logging.

Nanonets provides a cutting-edge AI platform designed to automate data extraction from unstructured or semi-structured documents. The platform simplifies workflows across multiple business processes by using machine learning models to capture, recognize, and extract data efficiently. By automating these tasks, businesses can save time, reduce errors, and improve overall productivity.

Stock Alpha leverages cutting-edge AI technology to transform how investors search, analyze, and interpret company data. It allows users to pose questions and receive comprehensive summaries of annual reports, earnings call transcripts, and investor decks for more than 200 Indian stocks. By automating the extraction of crucial information, Stock Alpha not only saves time but also enhances decision-making efficiency, helping users to uncover actionable insights in their investment journeys.

Virtual Scientists V2 serves as a modular AI agent framework tailored for scientific research. It defines multiple virtual scientists—Chemist, Physicist, Biologist, and Data Scientist—each equipped with domain-specific knowledge and tool integrations. These agents utilize LangChain to orchestrate API calls to sources like Semantic Scholar, ArXiv, and web search, enabling automated literature retrieval, contextual analysis, and data extraction. Users script tasks by specifying research objectives; agents autonomously gather papers, summarize methodologies and results, propose experimental protocols, generate hypotheses, and produce structured reports. The framework supports plugins for custom tools and workflows, promoting extensibility. By automating repetitive research tasks, Virtual Scientists V2 accelerates insight generation and reduces manual effort across multidisciplinary projects.