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  • Powerful Auto Researcher automatically iterates research questions, fetches AI-generated answers, and compiles and exports structured insights.
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    What is Powerful Auto Researcher?
    Powerful Auto Researcher is a Python-based AI agent framework designed to automate and accelerate research workflows. Users define topics or initial questions, and the agent iteratively generates follow-up questions, submits them to OpenAI models, and aggregates the responses. It supports customizable prompt templates, workflow chaining, and automated export to Markdown, JSON, or PDF. Integrated logging and result management enable reproducibility. This tool is ideal for academic literature reviews, competitive intelligence gathering, market research, and technical deep dives, reducing manual overhead and ensuring systematic coverage of research questions.
    Powerful Auto Researcher Core Features
    • Iterative question generation and chaining
    • AI-powered answer retrieval via OpenAI models
    • Customizable prompt templates and workflows
    • Automated result aggregation and logging
    • Export to Markdown, JSON, and PDF formats
    • Integration with Jupyter notebooks and CLI
  • Duet GPT is a multi-agent orchestration framework enabling dual OpenAI GPT agents to collaboratively solve complex tasks.
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    What is Duet GPT?
    Duet GPT is a Python-based open source framework for orchestrating multi-agent conversations between two GPT models. You define distinct agent roles, customized with system prompts, and the framework manages turn-taking, message passing, and conversation history automatically. This cooperative structure accelerates complex task resolution, enabling comparative reasoning, critique cycles, and iterative refinement through back-and-forth exchanges. Its seamless OpenAI API integration, simple configuration, and built-in logging make it ideal for research, prototyping, and production workflows in coding assistance, decision support, and creative ideation. Developers can extend the core classes to integrate new LLM services, adjust the iterator logic, and export transcripts in JSON or Markdown formats for post-analysis.
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