| name | deep-research |
| description | Multi-mode research agent using Tavily. Quick searches, query expansion, and deep multi-query synthesis with smart stopping. **MANDATE:** Always load exa + valyu for comprehensive research. |
| homepage | https://tavily.com |
| metadata | {"clawdbot":{"emoji":"🔬","requires":{"bins":"[Truncated]","env":"[Truncated]"},"primaryEnv":"TAVILY_API_KEY"}} |
Deep Research
MANDATE: All research queries MUST load exa + valyu backends for comprehensive discovery.
Intelligent research agent with three modes: quick search, query expansion, and deep multi-query synthesis. Uses Tavily's AI-optimized search with smart stopping.
Required Backends
Before starting any research:
- Load
skill://exa-plus - Neural web + GitHub search
- Load
skill://valyu - Deep research + time travel analysis
- Use
lev find --scope=research for integrated search
Example:
lev find "authentication patterns 2026" --scope=research
bun {baseDir}/scripts/research.mjs "authentication patterns 2026" --deep
Usage
bun {baseDir}/scripts/research.mjs "query" --quick
bun {baseDir}/scripts/research.mjs "query" --expand
bun {baseDir}/scripts/research.mjs "query" --deep
Modes
Quick Mode (--quick)
Single Tavily search. Fast, returns top results with AI-generated answer.
- Best for: Simple factual questions, quick lookups
- Iterations: 1
Expand Mode (--expand) [default]
Iterative query refinement. Analyzes initial results and generates follow-up queries.
- Best for: Exploratory research, learning about a topic
- Iterations: 2-3 (stops when confident or no new info)
Deep Mode (--deep)
Multi-query parallel search with synthesis. Generates multiple angle queries, searches in parallel, and synthesizes findings.
- Best for: Comprehensive research, due diligence, complex topics
- Iterations: Up to 5 (configurable)
Options
--quick: Quick single-query mode
--expand: Iterative expansion mode (default)
--deep: Deep multi-query synthesis mode
--max-iter <n>: Maximum iterations (default: 5)
--confidence <n>: Confidence threshold 0-100 (default: 85)
--results <n>: Results per query (default: 5, max: 20)
--topic <t>: Search topic - general (default) or news
--json: Output raw JSON instead of markdown
Smart Stopping
The agent stops early when:
- Confidence threshold reached - Sources consistently agree
- No new information - Follow-up queries return redundant results
- Max iterations hit - Safety limit reached
Output Format
# Research: [Query]
## Summary
[Synthesized findings with confidence score]
## Key Findings
- Finding 1
- Finding 2
...
## Sources
- [Title](url) - relevance%
...
## Research Trace
- Iteration 1: [query] → [n] results
- Iteration 2: [follow-up] → [n] new results
...
Configuration
API key is read from (in order):
TAVILY_API_KEY environment variable
~/.clawdbot/clawdbot.json → skills.entries["tavily-search"].apiKey
Examples
bun {baseDir}/scripts/research.mjs "What is the current population of Tokyo?" --quick
bun {baseDir}/scripts/research.mjs "How does CRISPR gene editing work?"
bun {baseDir}/scripts/research.mjs "Best practices for Kubernetes autoscaling in production" --deep
bun {baseDir}/scripts/research.mjs "AI regulation updates 2024" --topic news --deep
Related Search Tools
Choose the right tool for your task:
| Tool | Best For | When to Use |
|---|
| deep-research (this) | Multi-query synthesis, iterative refinement | Complex research, topic exploration |
| valyu | Turn-based recursive research (1-10 turns) | Confidence-driven research, automatic query refinement |
| lev-research | Multi-perspective orchestration | Architecture analysis, gap detection |
| lev-find | Unified search (local + external) | Cross-domain search, finding related work |
| brave-search | Quick web search | Documentation, API references |
| tavily-search | Single-query AI search | Fast lookups, clean snippets |
| exa-plus | Neural search, GitHub, LinkedIn | People/company search, research papers |
| grok-research | Real-time X/Twitter, current events | Social sentiment, trending topics |
| firecrawl | Web scraping, site mapping | Content extraction, structured data |
| qmd | Local session/doc search | Conversation history, markdown collections |
Integration pattern:
brave-search "keyword" --num 5
deep-research "keyword context" --deep
valyu research "keyword context" --turns 5 --threshold 0.85
lev-research "keyword" --template=technology_assessment
Notes
- Uses Tavily's
advanced search depth for better results in expand/deep modes
- Deduplicates sources across iterations
- Tracks information density to detect diminishing returns
- Outputs structured markdown optimized for AI consumption