ResearchAgent leverages large language models to conduct automated research across online databases and web sources. Users provide a research query, and the agent executes searches, scrapes document metadata, extracts abstracts, highlights key findings, and generates organized summaries with citations. It supports customizable pipelines, allowing integration with APIs, PDF parsing, and export to Markdown or JSON for further analysis or reporting.

Agent TARS leverages a combination of advanced computer vision and natural language processing techniques to understand and manipulate graphical user interfaces. By capturing visual representations of web pages, TARS can identify buttons, forms, tables, and other page elements. Users interact with TARS through natural language prompts, instructing it to click, scroll, extract text, or fill forms across multiple pages. It supports customizable workflows that chain tasks—such as logging into accounts, scraping data, and exporting results to CSV or JSON. With support for headless and headful browser modes, TARS enables both interactive exploration and unattended automation, making it ideal for testing, data acquisition, and routine browser-based operations.

Agent Transparency Tool offers a comprehensive framework for instrumenting AI agents with transparency features. It provides logging interfaces to record state transitions and decisions, modules to compute key transparency metrics (e.g., confidence scores, decision lineage), and visualization dashboards to explore agent behavior over time. By integrating seamlessly with popular agent frameworks, it generates structured transparency logs, supports export to JSON or CSV formats, and includes utilities to plot transparency curves for audit and performance analysis. This toolkit empowers teams to identify biases, debug workflows, and demonstrate responsible AI practices.

Browser Operator transforms your browser into an AI-powered agent that understands natural language commands. By connecting to your OpenAI API key, it can navigate to URLs, click buttons, fill out forms, extract tables or lists, and download content. You can chain multiple steps into a single workflow, export results as CSV or JSON, and even schedule tasks. All processing happens locally in your browser, ensuring fast response times and data privacy. No coding is required—just tell Browser Operator what you need, and it handles the rest.

Deep Research Agent leverages OpenAI's GPT models to perform advanced document retrieval and analysis. Users configure data sources (e.g., PubMed, arXiv), define queries, and receive digestible summaries highlighting methods, results, and key arguments. It supports multi-document comparison, citation extraction, and interactive Q&A sessions. Modular architecture allows integration of custom connectors, NLP pipelines, and export formats like markdown or JSON. With built-in scheduling, it can periodically update literature reviews, detect new research trends, and generate reports. Ideal for research teams, academics, and industry analysts seeking to reduce manual reading time and improve insight discovery in vast scientific corpora.

NanoBrowser runs directly in your browser as a Chrome extension, enabling you to automate repetitive or complex web tasks via natural-language prompts. You configure it with your own LLM API key—OpenAI GPT, self-hosted LLaMA models, or others—and define workflows composed of multiple agents. It supports data scraping, form interactions, automated research, and workflow chaining through LangChain integration. You can orchestrate agents to collaborate on subtasks, export results in CSV or JSON, and debug or refine steps interactively. As an open-source alternative to proprietary operators, NanoBrowser prioritizes privacy, extensibility, and ease of use.