Ultimate structured results Solutions for Everyone

Discover all-in-one structured results tools that adapt to your needs. Reach new heights of productivity with ease.

structured results

  • Effortlessly search multiple platforms with one click using SeekAll.
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    What is SeekAll?
    SeekAll is a user-friendly Chrome extension designed to enhance your online research experience. With just a single click, users can search through multiple websites simultaneously, including traditional search engines and AI-powered platforms. This innovative tool displays results in a structured format, making it easier to find relevant information quickly and efficiently. SeekAll is perfect for professionals, students, and anyone needing to gather information from various sources without the hassle of switching tabs.
  • GenAI Job Agents is an open-source framework that automates task execution using generative AI-based job agents.
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    What is GenAI Job Agents?
    GenAI Job Agents is a Python-based open-source framework designed to streamline the creation and management of AI-powered job agents. Developers can define customized job types and agent behaviors using simple configuration files or Python classes. The system integrates seamlessly with OpenAI for LLM-powered reasoning and LangChain for chaining calls. Jobs can be queued, executed in parallel, and monitored through built-in logging and error-handling mechanisms. Agents can handle dynamic inputs, retry failures automatically, and produce structured results for downstream processing. With modular architecture, extensible plugins, and clear APIs, GenAI Job Agents empowers teams to automate repetitive tasks, orchestrate complex workflows, and scale AI-driven operations in production environments.
  • A Python framework for easily defining and executing AI agent workflows declaratively using YAML-like specifications.
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    What is Noema Declarative AI?
    Noema Declarative AI allows developers and researchers to specify AI agents and their workflows in a high-level, declarative manner. By writing YAML or JSON configuration files, you define agents, prompts, tools, and memory modules. The Noema runtime then parses these definitions, loads language models, executes each step of your pipeline, handles state and context, and returns structured results. This approach reduces boilerplate, improves reproducibility, and separates logic from execution, making it ideal for prototyping chatbots, automation scripts, and research experiments.
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