| name | mofa-research-2.0 |
| description | Deep research pipeline with dynamic parallel search, inspired by DeerFlow and mofa-research. Triggers: deep research, 深度研究, comprehensive research, research report, 研究报告 |
| requires_bins | curl |
| requires_env | TAVILY_API_KEY |
| always | false |
MOFA Research 2.0
A deep research pipeline that combines DeerFlow's iterative exploration with mofa-research's dynamic parallel architecture.
Architecture
┌─────────────────────────────────────────────────────────────────────────────┐
│ MOFA RESEARCH 2.0 PIPELINE │
└─────────────────────────────────────────────────────────────────────────────┘
Phase 1: PLAN (Strong Model)
─────────────────────────────
Lead Agent analyzes query → Generates 4-8 dynamic search angles
↓
┌─────────────────────┐
│ JSON Output: │
│ [{task, label}, │
│ {task, label}, │
│ ...] │
└─────────────────────┘
Phase 2: DISCOVERY (Parallel Workers)
──────────────────────────────────────
┌─────────────┐
┌────┤ Worker 1 ├────┐
│ └─────────────┘ │
│ ┌─────────────┐ │
Spawn ├────┤ Worker 2 ├────┤ Parallel
All │ └─────────────┘ │ Execution
at │ ┌─────────────┐ │
Once ├────┤ Worker 3 ├────┤
│ └─────────────┘ │
│ ... │
│ ┌─────────────┐ │
└────┤ Worker N ├────┘
└─────────────┘
Each Worker:
- Has ISOLATED context (only sees its own task)
- Runs Tavily search with time-filtered queries
- Extracts facts, sources, follow-up leads
- Returns structured output
Phase 3: MERGE
──────────────
All worker outputs combined with:
- ## Label headers
- --- separators
- → KnowledgeBase (merged_outputs.md)
Phase 4: ANALYZE (Strong Model)
───────────────────────────────
Cross-reference all findings:
┌─────────────────────────────────────────────────────────┐
│ • Identify key facts across ALL angles │
│ • Cross-reference information │
│ • Filter by RECENCY (prioritize last 30 days) │
│ • Organize by subtopic │
│ • Rate source credibility │
│ • Flag contradictions │
│ • Identify gaps │
└─────────────────────────────────────────────────────────┘
↓
→ Analysis Report (analysis.md)
Phase 5: SYNTHESIZE (Strong Model + Goal Gate)
──────────────────────────────────────────────
Generate comprehensive report:
┌─────────────────────────────────────────────────────────┐
│ • Executive Summary │
│ • Key Findings (5-8 detailed) │
│ • Detailed Analysis │
│ • Contradictions & Uncertainties │
│ • Full Source List │
│ │
│ MANDATORY: Save to ./research/{slug}/report.md │
│ MINIMUM: 8000 characters │
│ NO EMOJI │
└─────────────────────────────────────────────────────────┘
Phase 6: RECURSION (Optional Deep Dive)
────────────────────────────────────────
If gaps identified or topics need deeper exploration:
↓
┌─────────────────────┐
│ Generate Sub-Angles │
│ for Each Gap │
└──────────┬──────────┘
↓
┌─────────────────────┐
│ Spawn Sub-Workers │
│ (Layer 2 Search) │
└──────────┬──────────┘
↓
┌─────────────────────┐
│ Background/Impact │
│ Analysis │
└─────────────────────┘
Time-Aware Research (CRITICAL)
DeerFlow-inspired temporal precision:
| User Intent | Temporal Precision | Example Query |
|---|
| "today / this morning" | Month + Day | "tech news March 11 2026" |
| "this week" | Week range | "AI releases week of March 9 2026" |
| "recently / latest" | Month | "AI breakthroughs March 2026" |
| "this year" | Year | "tech trends 2026" |
Rules:
- ALWAYS check current date before forming queries
- Never use year-only when day-level precision is needed
- Try multiple phrasings: numeric (
2026-03-11), written (March 11 2026)
- Include time filters in Tavily queries
Phase 1: Plan (Dynamic Angle Generation)
Model: Strong (for planning quality)
Planning Prompt:
{
"role": "user",
"content": "Generate 4-8 research search angles for this query. Each angle should cover a different aspect — do NOT just rephrase the same query.\n\nQuery: {{QUERY}}\nCurrent Date: {{CURRENT_DATE}}\n\nRequirements:\n- 4-8 distinct angles covering: core topic, alternatives/comparisons, technical details, recent developments, market impact, regional differences, expert opinions, challenges/limitations\n- Include at least one angle in Chinese and one in English\n- Use SPECIFIC temporal filters based on current date\n- Each task should be executable as a standalone search\n\nRespond with ONLY a JSON array:\n[\n {\"task\": \"Search specific query with date filter\", \"label\": \"Descriptive Label\"},\n ...\n]"
}
Fallback: If planner fails or returns <2 tasks, use:
[
{"task": "{{query}} latest news {{current_year}}", "label": "Latest News"},
{"task": "{{query}} developments {{current_month}} {{current_year}}", "label": "Recent Developments"},
{"task": "搜 {{query}} 最新消息 {{current_year}}", "label": "中文新闻"}
]
Phase 2: Discovery (Isolated Parallel Workers)
Model: Fast/Cheap (for parallel execution)
Worker Isolation: Each worker ONLY sees its assigned task, not other angles.
Worker Execution:
"You are a research specialist focused on: {label}
Your task: {task}
Current date: {current_date}
Execute Tavily search, then extract:
1. Key facts with dates
2. Important quotes
3. Source URLs
4. Follow-up leads for deeper exploration
Return structured JSON output."
Tavily Search with Time Filtering:
curl -s "https://api.tavily.com/search" \
-H "Content-Type: application/json" \
-d '{
"api_key": "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",
"query": "AI breakthrough March 11 2026", # <-- Time-specific!
"search_depth": "advanced",
"max_results": 10
}'
Parallel Execution:
spawn "research-worker-1" "Worker 1 prompt with isolated context" &
spawn "research-worker-2" "Worker 2 prompt with isolated context" &
spawn "research-worker-3" "Worker 3 prompt with isolated context" &
spawn "research-worker-4" "Worker 4 prompt with isolated context" &
wait
Worker Output Format (Structured JSON):
{
"angle_label": "Label from planning",
"query_executed": "Actual search query used",
"execution_timestamp": "ISO 8601 timestamp",
"results": [
{
"rank": 1,
"title": "...",
"url": "...",
"content": "Summary...",
"published_date": "2026-03-10",
"source_type": "news|blog|academic|official",
"authority_score": 0.8,
"key_facts": [
{
"fact": "Specific claim",
"quote": "Exact text from source",
"confidence": "high|medium|low"
}
]
}
],
"follow_up_leads": [
{
"type": "event|person|company|technology",
"entity": "Name",
"reason_to_explore": "Why this matters",
"suggested_queries": ["...", "..."]
}
],
"coverage_assessment": {
"satisfied": true|false,
"gaps": ["..."]
}
}
Phase 2.5: Worker Failure Handling (容错机制)
CRITICAL: Always handle worker failures gracefully
After workers complete (success or failure):
Failure Recovery
completed_workers = []
failed_workers = []
for worker in spawned_workers:
if worker.status == "completed":
completed_workers.append(worker)
elif worker.status == "failed" or worker.status == "timeout":
failed_workers.append(worker)
if worker.partial_output:
completed_workers.append(worker)
write_file "./research/{query-slug}/kb/execution_log.md" "Execution Summary:
- Total angles: {total}
- Successful: {len(completed_workers)}
- Failed: {len(failed_workers)}
- Failed angles: {failed_angles_list}"
Minimum Viable Research
Even if only 1 worker succeeds, continue to Merge phase.
Requirements adjusted based on success rate:
| Success Rate | Min Angles | Min Sources | Min Facts | Report Length |
|---|
| 100% (4/4) | 4 | 15 | 25 | 8000 chars |
| 75% (3/4) | 3 | 12 | 20 | 6000 chars |
| 50% (2/4) | 2 | 8 | 15 | 5000 chars |
| 25% (1/4) | 1 | 5 | 10 | 3000 chars |
Always generate report with caveats:
"Note: Based on {N} of {total} planned research angles due to timeouts. Coverage may be incomplete."
Phase 3: Merge (KnowledgeBase Construction - ALWAYS EXECUTE)
MANDATORY: This phase ALWAYS runs, regardless of worker failures.
Combine all worker outputs (complete and partial):
# Merged Research Outputs
Query: {original_query}
Generated: {timestamp}
Execution Summary:
- Total angles planned: {N}
- Successfully completed: {M}
- Failed/timeout: {N-M}
- Angles included in this merge: {list}
---
## Angle 1: {label} [STATUS: completed|partial|failed]
Query: {task}
{Worker 1 output OR failure reason}
---
## Angle 2: {label} [STATUS: completed|partial|failed]
Query: {task}
{Worker 2 output OR failure reason}
---
[Continue for all angles...]
## Cross-Reference Matrix
| Fact | Sources | Confidence |
|------|---------|------------|
| ... | ... | ... |
## Coverage Gaps (from failed angles)
- {Angle X}: Failed due to timeout - potential gap in {topic}
- {Angle Y}: Failed due to timeout - potential gap in {topic}
Save to: ./research/{query-slug}/kb/merged_outputs.md
Phase 4: Analyze (Cross-Reference)
Model: Strong
Analysis Prompt:
{
"role": "user",
"content": "You are a research analyst. Analyze the following merged search results from multiple parallel angles.\n\n[Read from: ./research/{query-slug}/kb/merged_outputs.md]\n\nYour task:\n1. Extract all key facts, data points, and insights from ALL angles\n2. Cross-reference: identify where sources agree or disagree\n3. TEMPORAL FILTERING: Prioritize facts from last 30 days\n4. Organize by subtopic, merging related information\n5. Rate source credibility (official > academic > news > blog)\n6. Flag contradictions explicitly\n7. Identify coverage gaps needing deeper exploration\n\nOutput: Structured analysis with:\n- Facts by subtopic\n- Contradictions table\n- Source credibility ratings\n- Gap analysis\n- Recommendations for recursion (if needed)"
}
Save to: ./research/{query-slug}/kb/analysis.md
Phase 5: Synthesize (Report Generation) - ALWAYS EXECUTE
Model: Strong
Goal Gate: MUST write file before completion.
IMPORTANT: This phase ALWAYS runs, even if all workers failed. Generate report based on available data.
Adaptive Requirements (based on worker success rate):
| Success Rate | Min Findings | Min Sources | Min Report Length |
|---|
| 100% (4/4) | 5-8 | 15 | 8000 chars |
| 75% (3/4) | 4-6 | 10 | 6000 chars |
| 50% (2/4) | 3-5 | 8 | 4000 chars |
| 25% (1/4) | 2-4 | 5 | 2500 chars |
| 0% (0/4) | 1-2 | 0 | 1000 chars (note) |
Synthesis Prompt:
{
"role": "user",
"content": "You are a research synthesis expert. Produce a comprehensive report.\n\n[Read from: ./research/{query-slug}/kb/analysis.md]\n\nFirst, check execution summary in merged_outputs.md to determine success rate and adjust your approach.\n\nReport Requirements:\n\n1. EXECUTIVE SUMMARY (2-8 sentences based on available data)\n - Key finding (or 'Limited data available' if workers failed)\n - Methodology summary (note any timeouts/failures)\n - Overall confidence\n\n2. KEY FINDINGS (see adaptive table for minimum count)\n Each finding MUST include:\n - Clear title\n - Detailed explanation (not just headline)\n - Specific numbers/dates/quotes\n - Citations [n]\n - Confidence level\n\n3. DETAILED ANALYSIS\n - Organized by subtopic\n - Comprehensive coverage (or note gaps if data is limited)\n\n4. CONTRADICTIONS & UNCERTAINTIES (if any)\n\n5. COVERAGE LIMITATIONS (MANDATORY if workers failed)\n > Note: This report is based on X of Y planned research angles due to timeouts. Coverage may be incomplete.\n\n6. SOURCES\n - Full bibliography\n - [n] Title - URL (Type, Date, Authority)\n\nCRITICAL RULES:\n- Adjust length based on available data (see adaptive table)\n- Every major claim has citation\n- NO EMOJI - plain text only\n- Match query language\n- Include specific data, not generalizations\n- ALWAYS include coverage limitations section if any workers failed\n\nFINAL STEP: Use write_file to save to ./research/{query-slug}/report.md"
}
Phase 5.5: Memory Persistence (mofa-memory integration)
This phase completes the ResearchMemory node shown in architecture.dot. It has no extra compute cost — it calls mofa-memory which runs in milliseconds (SQLite read/write) after the embedding API call.
Pre-Research Recall (before Phase 1)
Before generating research angles, call retrieve_memory to surface any prior research on the same topic:
{"query": "{{ORIGINAL_USER_QUERY}}", "top_k": 3, "min_score": 0.75}
If results are returned with score >= 0.75, present them to the user:
"I found prior research on this topic from {created_at} (relevance: {score}). Shall I build on it or start fresh?"
If the user wants to build on it, include the prior report content in the Entry Agent's context for Phase 1.
Post-Synthesis Persistence (after Phase 5)
After the report is written to ./research/{slug}/report.md, persist it to long-term memory.
Persist the final report:
{
"content": "<full text read from ./research/{query-slug}/report.md>",
"tags": ["{query-slug}", "{primary-topic}", "research-report"],
"source": "./research/{query-slug}/report.md"
}
Persist the knowledge base:
{
"content": "<full text read from ./research/{query-slug}/kb/merged_outputs.md>",
"tags": ["{query-slug}", "{primary-topic}", "kb-cache"],
"source": "./research/{query-slug}/kb/merged_outputs.md"
}
Requires: mofa-memory skill installed and OPENAI_API_KEY available.
Skip silently if mofa-memory binary is not found — memory is optional, research always completes.
User Output (Simplified)
During research, ONLY show user:
正在研究: {query}
├── 角度1: {label} [完成]
├── 角度2: {label} [超时]
├── 角度3: {label} [完成]
└── 角度4: {label} [完成]
正在交叉验证... 完成
正在生成报告... 完成
报告已保存: ./research/{query-slug}/report.md
DO NOT show:
- Internal JSON structures
- Raw search results
- Individual worker outputs
- File paths for intermediate files (merged_outputs.md, analysis.md)
- Detailed error traces
DO show:
- High-level progress (phase names only)
- Success/failure counts
- Final report location and summary
Phase 6: Recursion (Deep Dive)
If analysis identifies gaps or high-priority follow-ups:
Gap Identified
↓
Generate Sub-Angles (2-4 focused queries)
↓
Spawn Sub-Workers (Layer 2)
↓
Background/Impact Analysis
↓
Merge with Layer 1 Results
↓
Updated Analysis
Recursion Triggers:
- Important claim with single source
- Contradiction between sources
- Event without background context
- Technology without impact assessment
Output Requirements (MANDATORY)
Files Must Be Written
- Merged Outputs →
./research/{query-slug}/kb/merged_outputs.md
- Analysis →
./research/{query-slug}/kb/analysis.md
- Final Report →
./research/{query-slug}/report.md
Minimum Requirements
| Metric | Minimum | Target |
|---|
| Search Angles | 4 | 6-8 |
| Sources | 15 | 25+ |
| Facts | 25 | 40+ |
| Report Length | 8000 chars | 12000+ |
| Recursion Depth | Layer 1 | Layer 2-3 |
| Citations | 10 | 15+ |
Prohibited
- NO EMOJI in any output
- NO skipping file writes
- NO reports under 8000 characters
- NO single-source claims without verification
Configuration
See config.toml for:
min_angles: 4max_workers: 8min_report_length: 8000recursion_enabled: truetemporal_awareness: strict
Performance
With 4-8 parallel workers:
- Plan: ~30s
- Discovery: ~60s (parallel bottleneck)
- Analyze: ~90s
- Synthesize: ~60s
- Total: ~4-5 minutes for comprehensive research
Comparison: mofa-research vs 2.0
| Feature | mofa-research | mofa-research-2.0 |
|---|
| Architecture | DOT pipeline | DOT + DeerFlow hybrid |
| Worker Isolation | No | Yes (isolated contexts) |
| Time Awareness | Basic | Strict (DeerFlow-inspired) |
| Recursion | Static | Dynamic (gap-driven) |
| Output Format | Simple | Docling-inspired structured |
| Plan Mode | No | TodoList-ready |
Related Skills
mofa-memory
Persistent vector memory across agent runs. Stores research reports in SQLite with OpenAI embeddings for semantic recall in future sessions.
Use with mofa-research-2.0 when:
- Running repeated research on related topics (avoids redundant work)
- Building a knowledge base over multiple research sessions
- Wanting to detect prior research before starting a new investigation
Integration pattern:
mofa-memory: retrieve_memory(query) → surface prior research (optional)
↓
mofa-research-2.0: Plan → Discover → Analyze → Synthesize → Report
↓
mofa-memory: store_memory(report + kb) → persist for future sessions
mofa-crawler
Web crawling via Cloudflare Browser Rendering API for data acquisition.
Use with mofa-research-2.0 when:
- Target data is on specific websites not indexed by search engines
- Need full-site extraction (e.g., documentation, knowledge bases)
- Require JavaScript-rendered content (SPAs, React/Vue sites)
- Want structured data extraction with AI
Integration pattern:
mofa-crawler: Crawl docs site → Markdown
↓
mofa-research-2.0: Analyze + Synthesize → Research report
mofa-firecrawl
Advanced web crawling and scraping via Firecrawl CLI with support for single-page scraping, full-site crawling, URL mapping, web search, and cloud browser automation.
Use with mofa-research-2.0 when:
- Need comprehensive site crawling with structured output
- Require dynamic content extraction with JavaScript rendering
- Want to combine web search with content scraping
- Need browser automation (clicking, form filling, navigation)
- Target sites require advanced extraction capabilities
Integration pattern:
mofa-research-2.0: Plan research angles
↓
mofa-firecrawl: Crawl target sites + Search supplementary sources
↓
mofa-research-2.0: Cross-reference + Analyze + Synthesize
Feature comparison:
| Feature | mofa-crawler | mofa-firecrawl |
|---|
| Single page scrape | ✓ | ✓ |
| Full-site crawl | ✗ | ✓ |
| URL mapping | ✗ | ✓ |
| Web search | ✗ | ✓ |
| Browser automation | ✗ | ✓ |
| Cloud browser sessions | ✗ | ✓ |
| Self-hosted option | ✗ | ✓ |
mofa-verge-browser
GUI browser sandbox automation with real visual Chromium, supporting CDP automation, GUI screenshots, and human-in-the-loop intervention.
Use with mofa-research-2.0 when:
- Target sites require visual interaction (captcha, MFA, complex authentication)
- Need human verification or intervention during research
- Want visual evidence of page states during research
- Researching sites with anti-bot measures that need real browser behavior
- Need to handle popups, downloads, or multi-tab scenarios
Integration pattern:
mofa-research-2.0: Plan research angles
↓
mofa-verge-browser: Visual browser automation + human intervention when needed
↓
mofa-research-2.0: Analyze visual evidence and extracted data
Feature comparison with mofa-firecrawl:
| Feature | mofa-firecrawl | mofa-verge-browser |
|---|
| Hosting | Cloud service | Self-hosted (local/Docker) |
| GUI browser | ✗ | ✓ (Real Chromium with GUI) |
| Visual screenshot | Basic | GUI-level with browser UI |
| Human intervention | ✗ | ✓ (noVNC/Xpra sessions) |
| CDP/Playwright | Limited | Full WebSocket CDP support |
| Captcha handling | ✗ | ✓ (with human-in-the-loop) |
| Ideal for | Large-scale crawling | Complex auth, visual tasks |
mofa-pinchtab
Lightweight AI browser control via HTTP API with token-efficient text extraction (~800 tokens/page) and multi-instance parallel processing.
Use with mofa-research-2.0 when:
- Need token-efficient content extraction (cheaper than screenshots by 5-13x)
- Want lightweight local browser control without Docker complexity
- Need parallel multi-instance processing for batch URL extraction
- Require fast HTTP API-based automation
Integration pattern:
mofa-research-2.0: Plan research angles
↓
mofa-pinchtab: Parallel extraction of content from multiple sources
↓
mofa-research-2.0: Analyze extracted text → Research report
Feature comparison:
| Feature | mofa-firecrawl | mofa-verge-browser | mofa-pinchtab |
|---|
| Hosting | Cloud | Self-hosted (Docker) | Local binary |
| Setup complexity | Low | High | Low |
| Token efficiency | Medium | Low | High (~800t/page) |
| Multi-instance | ✗ | ✓ | ✓ |
| GUI browser | ✗ | ✓ | ✗ |
| Human intervention | ✗ | ✓ | ✗ |
| Best for | Scale | Complex auth | Token-efficient extraction |
mofa-xhs
Xiaohongshu (小红书) integration for social media research.
Use with mofa-research-2.0 when:
- Researching Chinese consumer trends and opinions
- Analyzing product reviews and user experiences
- Tracking influencer content and engagement
- Studying fashion/beauty/travel trends
Integration pattern:
mofa-xhs: Search topics → Extract notes
↓
mofa-research-2.0: Cross-reference + Analyze → Trend report
mofa-public-apis
Public API discovery for accessing external data sources.
Use with mofa-research-2.0 when:
- Need weather, finance, news data for research
- Looking for free data sources without API keys
- Building data enrichment pipelines
mofa-crawlee-python
Web scraping with Crawlee-Python library for structured data extraction.
Use with mofa-research-2.0 when:
- Need to crawl and extract data from websites
- Target sites have JavaScript-heavy content (SPAs)
- Require robust scraping with retries and proxy rotation
- Want exportable datasets (JSON, CSV, Parquet)
Integration pattern:
mofa-research-2.0: Identify data sources
↓
mofa-crawlee-python: Crawl and extract structured data
↓
mofa-research-2.0: Analyze extracted data → Research report
