Deep Research

Multi-round deep web research powered by Firecrawl with iterative query refinement.

pi install npm:@mikefreno/deep-research

Features

• Multi-round iteration: Each round generates follow-up queries based on previous findings (depth 1-3)
• Parallel query expansion: Multiple diverse search queries per round (breadth 1-5) covering technical, practical, comparative, critical, and forward-looking angles
• Round-robin parallel execution: Searches and analyses run concurrently within each round using bounded-concurrency worker pools, dramatically reducing total research time
• LLM-driven analysis: Each round's results are analyzed by an agent session to extract structured findings with confidence ratings
• Automatic deduplication: Search results are deduplicated by URL across all queries
• Graceful degradation: Individual search or analysis failures don't crash the full research — partial results are preserved
• Progress streaming: Real-time progress widget with spinner, phase indicators, and progress bar
• Abort support: Research can be cancelled mid-flight via AbortSignal
• Rich TUI rendering: Compact collapsed view and detailed expanded view in the terminal UI
• Fallback resilience: Built-in fallback query generation and report synthesis when LLM calls fail

Usage

Tool (LLM-callable)

Registers the deep_research tool for AI agent use:

deep_research — multi-round deep web research via Firecrawl with iterative query refinement

Parameters:

Parameter	Type	Default	Description
question	string	—	The research question to investigate
depth	integer (1-3)	2	Number of research rounds
breadth	integer (1-5)	3	Search queries per round
format	"markdown" | "structured"	"markdown"	Output format for the report
details.showRoundDetails	boolean	false	Include per-round search metadata in output

Command (interactive)

/deepi-research <your research question>

Prompts for depth (1-3 rounds) and breadth (1-5 queries) interactively, then runs the research and sends the final report as a user message.

Recommended usage

• Use deep_research for complex, multi-faceted questions that benefit from multiple search angles and iterative refinement.
• The tool handles query generation, web search, result analysis, and report synthesis automatically.
• For simple fact-finding questions, use firecrawl_search directly instead.

Architecture

Research Flow:

  Question
     ↓
  ┌─ Round 1 ───────────────────────────┐
  │  LLM → generate queries (N angles)   │
  │  Firecrawl → search each query       │
  │  LLM → analyze results → findings    │
  └──────────────┬───────────────────────┘
                 ↓ (follow-up queries)
  ┌─ Round 2 ───────────────────────────┐
  │  LLM → identify knowledge gaps       │
  │  Firecrawl → search follow-ups       │
  │  LLM → analyze → new findings        │
  └──────────────┬───────────────────────┘
                 ↓ (iterate depth times)
  ┌─ Synthesis ─────────────────────────┐
  │  LLM → synthesize all findings       │
  │  → comprehensive research report     │
  └─────────────────────────────────────┘

Configuration

Deep Research reads Firecrawl configuration from ~/.pi/agent/settings.json:

{
  "firecrawl": {
    "baseUrl": "http://localhost:3002",
    "apiKey": "your-api-key"
  }
}

Environment variables are also supported:

• FIRECRAWL_BASE_URL — overrides the Firecrawl endpoint (default: http://localhost:3002)
• FIRECRAWL_API_KEY — API key for authenticated Firecrawl instances

Session startup check

On session_start, the extension checks whether the Firecrawl endpoint is reachable. If not, it shows a warning notification so you know searches will fail before you try to use it.