AI Research Agent

The AI Research Agent automates comprehensive research tasks by gathering, analyzing, and synthesizing information from multiple sources. It leverages Tavily's search API and LLM capabilities to produce detailed, well-structured research reports.

AI Research Agent workflow and architecture

Configuration Parameters

Required Input Parameters

research_task: Research topic or question
tavily_api_key: API key for Tavily search

Optional Configuration

llm: Language model configuration
- model_name: Model identifier
- temperature: Response creativity
- max_tokens: Response length limit
max_revisions: Maximum report revision cycles
research_params: Research configuration
- depth: Research depth level
- sources_required: Minimum sources
- max_time: Time limit in minutes
- citation_format: Citation style

Output Format

{
  "final_research_report": {
    "title": string,
    "executive_summary": string,
    "methodology": {
      "search_strategy": string,
      "sources_consulted": number,
      "time_spent": number
    },
    "findings": [
      {
        "section": string,
        "content": string,
        "key_points": array,
        "sources": array
      }
    ],
    "analysis": {
      "main_insights": array,
      "trends": array,
      "limitations": array
    },
    "conclusions": {
      "summary": string,
      "recommendations": array,
      "future_research": array
    },
    "references": [
      {
        "title": string,
        "url": string,
        "accessed_date": string,
        "relevance_score": number
      }
    ],
    "metadata": {
      "revision_count": number,
      "confidence_score": number,
      "completion_time": string
    }
  }
}

Features

Automated research gathering
Source verification
Content synthesis
Citation management
Report structuring
Quality assurance
Iterative refinement
Progress tracking

Note: The quality of research output depends on the specificity of the research task and the diversity of available sources. Consider setting appropriate max_revisions for complex topics.

Tip: Use specific research questions for better results. Enable multiple revision cycles for comprehensive coverage and accuracy.

Example Usage

const researchAgent = new AIResearchAgent({
  research_task: "Analyze the impact of quantum computing on cryptography",
  tavily_api_key: "tvly-****",
  llm: {
    model_name: "gpt-4",
    temperature: 0.3,
    max_tokens: 4000
  },
  max_revisions: 3,
  research_params: {
    depth: "comprehensive",
    sources_required: 10,
    max_time: 60,
    citation_format: "APA"
  }
});

const report = await researchAgent.conductResearch();