deep_researcher_research
Decomposes research questions into targeted sub-questions, searches the web in parallel, scores source credibility, and synthesizes a structured markdown report with findings and citations.
Instructions
Run a multi-agent research pipeline that decomposes a query into sub-questions, searches the web in parallel via Tavily, scores source credibility, and synthesizes a comprehensive markdown report with findings, knowledge gaps, and cited sources.
The pipeline has three stages:
1. Planner — breaks the query into 3-5 targeted sub-questions (GPT-4o)
2. Searcher — runs parallel Tavily web searches with credibility scoring
3. Synthesizer — produces a structured markdown report (GPT-4o)
Results are cached for 24 hours. Identical (query + context + depth) combinations
return instantly on subsequent calls.
Args:
params (ResearchInput): Validated input containing:
- query (str): The research question (3-2000 chars)
- search_depth (SearchDepth): 'basic' for speed or 'advanced' for depth
- conversation_context (str): Prior research for multi-turn follow-ups
Returns:
str: Markdown research report with executive summary, key findings per
sub-question, knowledge gaps, and numbered source citations with
credibility tags (high/medium/unverified).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| params | Yes |