Multi-agent research reports on any topic.
Use the exec tool to invoke DeepTutor's multi-agent research pipeline for comprehensive topic analysis.
deeptutor run deep_research "<topic>" --format json -l <lang> --config-json '<json>'
| Field | Values | Description |
|---|---|---|
mode | , , , |
notesreportcomparisonlearning_path| Output format |
depth | quick, standard, deep | Research thoroughness |
sources | ["kb", "web", "papers"] | Information sources to use |
Standard report from web + papers:
deeptutor run deep_research "Recent advances in attention mechanisms" --format json -l en --config-json '{"mode":"report","depth":"deep","sources":["papers","web"]}'
Quick comparison:
deeptutor run deep_research "Rust vs Go for backend services" --format json -l en --config-json '{"mode":"comparison","depth":"quick","sources":["web"]}'
Learning path from knowledge base:
deeptutor run deep_research "Machine learning fundamentals" --format json -l zh --config-json '{"mode":"learning_path","depth":"standard","sources":["kb"]}' --kb ml-textbook
KB + web hybrid:
deeptutor run deep_research "Agentic RAG vs traditional RAG" --format json -l zh --config-json '{"mode":"comparison","depth":"deep","sources":["kb","web"]}' --kb my-papers
mode, depth, sources) are required. Always use --config-json to pass them together.depth=deep can take several minutes — use timeout=300 or higher with the exec tool.--format json to get parseable NDJSON output."type": "content" and concatenate for the full report.