An AI-powered deep research agent for autonomous web research
Project description
Deep Research Agent
An AI-powered research agent that autonomously searches the web, analyzes content, and generates comprehensive research reports on any topic.
Features
- Automated Web Research: Generate search queries, scrape content, and synthesize information
- Multi-cycle Research: Progressively build deeper understanding with multiple research cycles
- Reflective Analysis: Identify knowledge gaps and areas for further exploration
- Structured Reports: Generate well-organized markdown reports with clear sections
- Multiple Interfaces: Use via command line, Python API, or Streamlit web UI
Installation
pip install deep-research-agent
Usage
Command Line
deep-research --topic "Your research topic" --cycles 2 --output report.md
Streamlit UI
deep-research-ui
Then open your browser to the URL shown (typically http://localhost:8501).
Python API
from deep_research_agent import ResearchConfig, ResearchController
# Configure research parameters
config = ResearchConfig(
topic="Your research topic",
max_research_cycles=2,
max_search_results_per_query=5,
max_urls_to_scrape_per_cycle=3
)
# Initialize and run the research controller
controller = ResearchController(config)
results = controller.run_full_research()
# Access the final report
print(results["final_report"])
Requirements
- Python 3.9+
- Local LLM (Ollama with QwQ model recommended)
Environment Configuration
Create a .env file with:
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=qwq
How It Works
- Query Generation: Creates effective search queries based on the topic and current knowledge
- Web Search: Retrieves search results from DuckDuckGo
- Content Scraping: Extracts and cleans content from web pages
- Summary Generation: Integrates new information with existing knowledge
- Reflection: Identifies gaps and contradictions to guide further research
- Report Generation: Creates a comprehensive final report
License
MIT License - see LICENSE file for details.
Project details
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