xiaohongshu
xiaohongshu
langchain-deep-research
Run LangChain Open Deep Research agent for iterative web research and comprehensive reports. Requires LLM API keys and search API (e.g., OPENAI_API_KEY, TAVILY_API_KEY).
Full skill instructions
This skill utilizes the LangChain Open Deep Research framework to perform iterative web research with reflection and knowledge gap identification, producing comprehensive reports with citations.
Dependencies: Requires the open-deep-research package and LangGraph.
pip install open-deep-research langgraph-cli python-dotenv
API Key Configuration: Requires API keys for an LLM and a search provider.
# Set up your API keys
echo "# LLM Configuration" >> .env
echo "OPENAI_API_KEY=your_openai_key" >> .env
echo "# Search Configuration" >> .env
echo "TAVILY_API_KEY=your_tavily_key" >> .env
if [ -f .gitignore ] && ! grep -q ".env" .gitignore; then echo ".env" >> .gitignore; fi
echo "API keys saved to .env."
Use the scripts/research.py script to run a research task.
python3 scripts/research.py --query "<research_query>" [--max-iterations <N>]
--query (Required): The research question or topic.--max-iterations (Optional): Maximum number of research iterations (default: 3).--output (Optional): Output file path for the final report (default: stdout).python3 scripts/research.py --query "What are the latest developments in quantum computing error correction?" --max-iterations 4 --output report.md
The script outputs a comprehensive research report with:
xiaohongshu
technical spec
product ux expert
database patterns
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