deep-research

name	deep-research
description	Multi-agent research reports on any topic.
metadata	{"nanobot":{"emoji":"🔬","requires":{"bins":["deeptutor"]}}}
always	false

Deep Research

Use the exec tool to invoke DeepTutor's multi-agent research pipeline for comprehensive topic analysis.

When to Use

• User needs a research report or literature review
• User wants a comparison of approaches, frameworks, or technologies
• User asks for a learning path or structured overview of a field
• The topic requires aggregating information from multiple sources

Command

deeptutor run deep_research "<topic>" --format json -l <lang> --config-json '<json>'

Config JSON Fields

Field	Values	Description
mode	notes, report, comparison, learning_path	Output format
depth	quick, standard, deep	Research thoroughness
sources	["kb", "web", "papers"]	Information sources to use

Examples

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

Important

• All three config fields (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.
• Always pass --format json to get parseable NDJSON output.
• Parse lines with "type": "content" and concatenate for the full report.