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Researcher Agent

The Researcher Agent is responsible for gathering comprehensive and accurate information about the specified topic from various sources. It serves as the foundation for the newsletter generation process, providing the raw material that the Writer Agent will transform into engaging content.

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Role and Responsibilities

The Researcher Agent is defined with the following attributes:

  • Role: Research Specialist
  • Goal: Gather comprehensive and accurate information about specified topics
  • Backstory: An expert research specialist with a keen eye for detail and the ability to find the most relevant and up-to-date information, specializing in AI technology, industry trends, and market analysis.

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Tools

The Researcher Agent is equipped with several tools to gather information from different sources:

These tools allow the agent to collect information from a wide range of sources, ensuring comprehensive coverage of the topic.

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Implementation

The Researcher Agent is implemented in src/agents/researcher.py using CrewAI’s Agent class:

@staticmethod
def create(llm, actor) -> Agent:
    return Agent(
        role='Research Specialist',
        goal='Gather comprehensive and accurate information about specified topics',
        backstory="""You are an expert research specialist with a keen eye for detail 
        and the ability to find the most relevant and up-to-date information. You 
        specialize in AI technology, industry trends, and market analysis.""",
        tools=[
            GoogleScraperTool(actor=actor),
            RedditScraperTool(actor=actor),
            TwitterScraperTool(actor=actor),
            YouTubeScraperTool(actor=actor),
            GoogleNewsScraperTool(actor=actor)
        ],
        verbose=True,
        allow_delegation=False,
        llm=llm
    )

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Research Process

When assigned a task, the Researcher Agent follows this process:

  1. Search for Information: Uses its tools to search for information related to the topic
  2. Filter and Organize: Filters the information based on relevance, recency, and credibility
  3. Structure the Data: Organizes the information into categories for easier processing by the Writer Agent
  4. Provide Sources: Includes links to the original sources for reference

The agent’s output is structured as a JSON object with sections for different types of content:

{
  "summary": "Brief overview of the topic",
  "key_points": ["Key point 1", "Key point 2", "..."],
  "sources": ["URL1", "URL2", "..."],
  "sections": {
    "Latest News": [{"title": "News Title", "description": "News Description", "url": "URL"}],
    "Community Discussions": [{"title": "Discussion Title", "text": "Discussion Text", "url": "URL"}],
    "Social Media Insights": [{"text": "Tweet Text", "author": "Author", "url": "URL"}],
    "Video Content": [{"title": "Video Title", "description": "Video Description", "url": "URL"}]
  }
}

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Configuration

The Researcher Agent can be configured by modifying its creation parameters in src/agents/researcher.py:

  • LLM: Change the language model used by the agent
  • Tools: Add or remove tools to change the sources of information
  • Verbosity: Set verbose to True or False to control the amount of output

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Next Steps

  • Learn about the Writer Agent that transforms the research data into engaging content
  • Explore the tools that the Researcher Agent uses to gather information
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