GroktoCrawl Deep Research Demo

Demonstrating the --search-type {deep,focused} flag on groktocrawl agent.

Setup

Server: Self-hosted ghcr.io/groktopus/groktocrawl-agent on hal2000
LLM: DeepSeek V4 Flash via api.deepseek.com
Search: SlopSearX (SearXNG-compatible metasearch)
CLI: groktocrawl v1.9.0 (latest)

The Problem

Before this change, groktocrawl agent had no user control over research depth. The agent's research strategy (single-query vs multi-query, single-pass vs multi-pass) was auto-classified by an LLM Phase 0 ("Query Intelligence") with no CLI override. Users who wanted thorough multi-source investigation got thin single-pass results indistinguishable from a fast Q&A.

The Fix

Three-layer change across the stack:

Layer	Change
Model	`AgentRequest.search_type` field with `{deep, focused}` validator
Pipeline	`search_type` overrides auto-classification; `deep` forces multi-query + 2-pass
CLI	`--search-type {deep,focused}` flag on `groktocrawl agent` (default: `deep`)

Results

Comparison: Deep vs Focused on the same query

Cost of groktocrawl agent --search-type deep "..." at the default. Cost of groktocrawl agent --search-type focused "..." at the default.

All timings measured from the SSE streaming endpoint (POST /v2/agent with stream: true).

Focused Mode

Vanilla groktocrawl agent can also be run using --search-type focused for faster results, when you only need a quick answer.

Request:

time groktocrawl agent --search-type focused "What were the major technological innovations of the Han Dynasty?"

SSE Events (abbreviated):

data: {"type": "research_plan", "strategy": "focused",
       "queries": ["major technological innovations Han Dynasty",
                   "inventions of Han Dynasty"],
       "reasoning": "..."}

data: {"type": "research_pass", "pass": 1, "total_passes": 1}
data: {"type": "done", "latency_ms": 3202}

Metric	Value
Research passes	1
Search queries	2 (Phase 0 generated)
Total time	3.2s
Strategy	`focused` (overridden from auto-classified)

Deep Mode (default)

The user can control whether they want more research depth coverage, rather than the LLM deciding for them via auto-classification.

Request:

groktocrawl agent "What were the major technological innovations of the Han Dynasty?"

Or explicitly:

groktocrawl agent --search-type deep "What were the major technological innovations of the Han Dynasty?"

SSE Events (abbreviated):

data: {"type": "research_plan", "strategy": "deep",
       "queries": ["Han Dynasty technological innovations",
                   "inventions of Han Dynasty"],
       "reasoning": "..."}

data: {"type": "research_pass", "pass": 1, "total_passes": 2}
data: {"type": "status", "state": "searching"}
data: {"type": "done", "latency_ms": 5692}

Metric	Value
Research passes	2 (gap detection on pass 1 → pass 2 if needed)
Search queries	2 (Phase 0 generated)
Total time	5.7s (78% longer — expected for multi-pass)
Strategy	`deep` (overridden from auto-classified)

Side-by-Side

Dimension	Focused	Deep
Strategy displayed	`focused`	`deep`
Max passes	1	2
Searches generated	2 (single-query mode)	2 (multi-query mode)
Latency	3.2s	5.7s
Overhead ratio	1x	~1.78x
Gap detection	Disabled	Enabled (second pass if needed)

Search queries observed from the deep research run

The SlopSearX logs show the multi-query research plan executing distinct searches in parallel:

search?q=peak+era+of+American+railroads+late+19th+early+20th+century
search?q=history+of+the+transcontinental+railroad+and+its+impact
search?q=Interstate+Commerce+Act+and+railroad+regulation+effects
search?q=causes+of+decline+of+railroads+in+the+United+States+after+World+War+II
search?q=competition+from+automobiles+trucking+and+airlines+on+American+railroads

This confirms multi-query Phase 0 query intelligence, producing 5 distinct sub-queries for the single topic.

What was changed

All code is open-source at github.com/groktopus/groktocrawl.

File	Change
`agent-svc/agent/models.py`	`AgentRequest.search_type: str = "deep"` with `{deep, focused}` validator
`agent-svc/agent/research/gaps.py`	Gap detection: 4000→12000 char context, query-aware prompt
`agent-svc/agent/research/loop.py`	`search_type` param on `run_research`/`run_research_stream`; strategy override; 2-pass default for deep
`agent-svc/agent/research/streaming.py`	`search_type` pass-through from HTTP request to SSE pipeline
`agent-svc/agent/routes/agent.py`	`search_type=body.search_type` wired through streaming and sync paths
`agent-svc/agent/worker.py`	`search_type` param, cache bypass for deep requests
`groktocrawl` (CLI)	`--search-type {deep,focused}` on agent command (default: deep)
`scripts/check-cli-coverage.py`	Fixed to scan `routes/` package (was scanning nonexistent `api.py`)

Filed by Jasper (AI agent on behalf of Magnus Hedemark)

magnus919/groktocrawl-deep-research-demo.md

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