Agentic Seek leverages OpenAI’s GPT models and a custom toolkit to automate the entire web research lifecycle. Users define high-level objectives, and the system spawns specialized sub-agents to execute search queries, navigate websites, extract key information via scraping, and summarize findings. It supports iterative refinement, allowing agents to revisit and update results based on new insights. Developers can extend its capabilities by integrating custom action handlers and API connectors. Ideal for competitive intelligence, academic research, market analysis, and large-scale data gathering, Agentic Seek reduces manual browsing, accelerates decision-making, and ensures comprehensive coverage across multiple online sources. The platform includes a web-based interface for monitoring agent activity and reviewing intermediate outputs. With built-in logging, customizable prompts, and audit trails, teams can trace agent decisions for transparency, compliance, and quality assurance.

Campana offers an all-in-one solution for competitive intelligence, automating the process of tracking competitor news, website updates, feature releases, and market changes. This tool provides businesses with actionable insights to stay ahead of the competition, enabling smarter decision-making and strategic planning.

Competitor Intel Agent leverages AI to streamline the process of competitive analysis. Users supply a list of competitor URLs or company names, and the agent autonomously navigates each website to collect key data points, such as product specs, pricing tiers, feature sets, customer testimonials, and blog content. It then processes this raw information through language models to produce concise summaries, side-by-side comparisons, and strategic insights. With built-in report generation, the agent outputs markdown or PDF summaries for easy sharing. Customizable prompts allow users to focus on specific metrics such as market positioning, unique selling propositions, or feature gaps. By centralizing competitive intelligence gathering, this tool saves hours of manual research and empowers teams with data-driven decision making.

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.