An autonomous AI agent that conducts comprehensive research on any topic and generates detailed, well-structured reports.
Manual research is time-consuming, inconsistent, and often incomplete. This agent autonomously:
- Researches topics across multiple sources
- Synthesizes information intelligently
- Generates comprehensive, well-structured reports
- Adapts research depth based on query complexity
- Autonomous Research: Automatically searches and gathers information from multiple sources
- Intelligent Synthesis: Combines findings from different sources into coherent insights
- Smart Report Generation: Creates well-formatted reports with citations and structure
- Multi-step Reasoning: Plans research strategy and executes systematically
- Source Tracking: Maintains references and citations for all information
├── src/
│ ├── agent/
│ │ ├── __init__.py
│ │ ├── research_agent.py # Main agent orchestrator
│ │ └── planner.py # Research planning logic
│ ├── modules/
│ │ ├── __init__.py
│ │ ├── web_researcher.py # Web search and content extraction
│ │ ├── synthesizer.py # Information synthesis
│ │ └── report_generator.py # Report creation
│ └── utils/
│ ├── __init__.py
│ └── config.py # Configuration management
├── outputs/ # Generated reports
├── .env.example # Environment variables template
├── requirements.txt # Python dependencies
└── main.py # CLI entry point
- Install dependencies:
pip install -r requirements.txt- Set up environment variables:
cp .env.example .env
# Edit .env and add your API keys- Run the agent:
python main.pyfrom src.agent.research_agent import ResearchAgent
agent = ResearchAgent()
report = agent.research("Impact of AI on healthcare")
print(report)- LangChain: Agent framework and orchestration
- Google Gemini: LLM for reasoning and generation (free API available)
- Tavily/DuckDuckGo: Web search capabilities
- BeautifulSoup: Web content extraction
- Markdown: Report formatting