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FAQ

Table of Contents

Where's the name DeerFlow come from?

DeerFlow is short for Deep Exploration and Efficient Research Flow. It is named after the deer, which is a symbol of gentleness and elegance. We hope DeerFlow can bring a gentle and elegant deep research experience to you.

Which models does DeerFlow support?

Please refer to the Configuration Guide for more details.

How do I view complete model output?

If you want to see the complete model output, including system prompts, tool calls, and LLM responses:

  1. Enable debug logging by setting DEBUG=True in your .env file

  2. Enable LangChain verbose logging by adding these to your .env:

    LANGCHAIN_VERBOSE=true
LANGCHAIN_DEBUG=true

  3. Use LangSmith tracing for visual debugging (recommended for production):

    LANGSMITH_TRACING=true
LANGSMITH_API_KEY="your-api-key"
LANGSMITH_PROJECT="your-project-name"

For detailed instructions, see the Debugging Guide.

How do I enable debug logging?

To enable debug logging:

  1. Open your .env file
  2. Set DEBUG=True
  3. Restart your application

For Docker Compose:

docker compose restart

For development:

uv run main.py

You'll now see detailed logs including:

  • System prompts sent to LLMs
  • Model responses
  • Tool execution details
  • Workflow state transitions

See the Debugging Guide for more options.

How do I troubleshoot issues?

When encountering issues:

  1. Check the logs: Enable debug logging as described above
  2. Review configuration: Ensure your .env and conf.yaml are correct
  3. Check existing issues: Search GitHub Issues for similar problems
  4. Enable verbose logging: Use LANGCHAIN_VERBOSE=true for detailed output
  5. Use LangSmith: For visual debugging, enable LangSmith tracing

For Docker-specific issues:

# View logs
docker compose logs -f

# Check container status
docker compose ps

# Restart services
docker compose restart

For more detailed troubleshooting steps, see the Debugging Guide.