Perplexity Deep Research excels at:
- Discovery & real-time web search
-
- Cited, synthesized answers from the web
-
- 3-5 iterative search passes
-
- Exportable reports (PDF, Markdown)
Where we want to go deeper:
- Multi-stage reasoning with extended thinking
-
- Cross-source validation and contradiction detection
-
- Integration with internal knowledge (Google Workspace, docs)
-
- Memory persistence across research sessions
-
- Deeper source analysis (follow citation trails)
-
- Higher quality synthesis and long-form output
┌─────────────────────────────────────────────────────
USER INTERFACE
(Claude Chat / API / Custom UI)
└────────────────────────────────────────────────
┌──────────────────────────────────────────────────────
CLAUDE-FLOW v2.7
Agent Orchestration Platform
┌─ ┌─────
Swarm Mgmt Hive-Mind 25 Claude Skills
64 Agents Coordination Natural Lang
└──── └──
└─────────────────────────────────────────
┌────────────────────────────────────
AGENTDB v1.3.9
High-Performance Memory & Vector DB
┌─────── ┌─────────────────
Vector Semantic RAG Engine
Search 96x Memory Document Index
└── └──────────
└───────────────────────
│
┌───────────────────────────────
MCP CONNECTORS
┌────── ┌─────────┐
Web Search Academic Google Custom APIs
Brave/etc Semantic Workspace Domain-spec
└─────── └──
└──────────────────────────────────────────────────
```
---
## Key Components
### 1. Claude-Flow (ruvnet/claude-flow)
**Role:** Agent orchestration and coordination
**Key Features for Deep Research:**
- **Hive-Mind Intelligence**: Queen-led coordination for complex multi-step research
- **25 Claude Skills**: Natural language activation for research tasks
- **100 MCP Tools**: Comprehensive toolkit for automation
- **Swarm Orchestration**: Parallel research agents (researcher, analyst, synthesizer)
- **Session Persistence**: Resume research across sessions
**Research-Relevant Skills:**
- `pair-programming` adapt for pair-research
- `github-code-review` adapt for source review
- `agentdb-vector-search` semantic memory search
- `swarm-orchestration` multi-agent research coordination
### 2. AgentDB (louloulin/AgentDB)
**Role:** Memory, vector search, and RAG
**Performance Stats:**
- Vector Search: 22ms (5x faster than target)
- Semantic Search: 17ms (3x faster than target)
- 96x-164x faster than alternatives
**Key Features:**
- **HNSW Indexing**: O(log n) vector search
- **9 RL Algorithms**: Q-Learning, PPO, MCTS, Decision Transformer
- **Reflexion Memory**: Learn from past research patterns
- **Skill Library**: Auto-consolidate successful research strategies
- **Quantization**: 4-32x memory reduction
### 3. MCP Connectors (To Build/Integrate)
**Required Connectors:**
```
[ ] Web Search API (Brave, Tavily, Serper)
[ ] Academic Search (Semantic Scholar, arXiv, PubMed)
[ ] Google Workspace (Gmail, Docs, Calendar)
[ ] Document Processing (PDF, DOCX extraction)
[ ] Citation Graph API (follow reference trails)
```
---
## Research Pipeline Design
### Phase 1: Query Decomposition
```
User Query Claude Extended Thinking Sub-questions
Research angles
Source types needed
Depth parameters
```
### Phase 2: Parallel Discovery (Swarm)
```
┌─
QUEEN AGENT Coordinates research strategy
└─
┌
┌── ┌────────
Web Academic Internal │Expert
Searcher Searcher Doc Source
Searcher Finder
└──────── └──────
```
### Phase 3: Cross-Validation
```
Sources Claim Extraction Cross-Reference Matrix
Contradiction Detection
Confidence Scoring
Source Quality Weighting
```
### Phase 4: Deep Analysis
```
Validated Claims Extended Thinking Analysis
Follow citation trails (depth 2-3)
Identify gaps in knowledge
Generate follow-up searches
```
### Phase 5: Synthesis
```
Analyzed Content Structured Report Generation
Inline Citations
Confidence Annotations
Export (Markdown, PDF, Notion)
```
---
## Differentiators vs Perplexity
| Feature | Perplexity | Our System |
|---------|------------|------------|
| Search Passes | 3-5 | Unlimited (depth parameter) |
| Source Types | Web | Web + Academic + Internal + Custom |
| Memory | Session only | Persistent (AgentDB) |
| Reasoning | Hidden | Extended thinking visible |
| Contradictions | Not surfaced | Explicit detection |
| Citation Depth | 1 level | Follow trails 2-3 deep |
| Learning | None | Reflexion memory, skill consolidation |
| Customization | Fixed | MCP connectors, custom agents |
---
## Implementation Roadmap
### Phase 1: Foundation (Week 1-2)
- [ ] Install claude-flow v2.7
- [ ] Configure AgentDB integration
- [ ] Set up basic MCP connectors (web search)
- [ ] Test swarm orchestration
### Phase 2: Research Agents (Week 3-4)
- [ ] Create specialized research agents
- [ ] Implement query decomposition
- [ ] Build cross-validation logic
- [ ] Add citation trail following
### Phase 3: Memory & Learning (Week 5-6)
- [ ] Configure reflexion memory
- [ ] Implement skill consolidation
- [ ] Add research pattern learning
- [ ] Session persistence
### Phase 4: Quality & Output (Week 7-8)
- [ ] Source quality scoring
- [ ] Contradiction detection UI
- [ ] Report generation templates
- [ ] Export formats
---
## Quick Start Commands
```bash
# Install claude-flow
npx claude-flow@alpha init --force
# Set up AgentDB
npm install [email protected]
# Initialize research swarm
npx claude-flow@alpha swarm init --topology mesh --max-agents 5
# Spawn research agents
npx claude-flow@alpha swarm spawn researcher "deep research on [topic]"
npx claude-flow@alpha swarm spawn analyst "cross-validate findings"
npx claude-flow@alpha swarm spawn synthesizer "compile final report"
# Use hive-mind for complex research
npx claude-flow@alpha hive-mind spawn "comprehensive research on [topic]" --claude
# Query memory
npx claude-flow@alpha memory vector-search "previous research on [topic]" --k 10
# Store research findings
npx claude-flow@alpha memory store-vector research_findings "key insights" --namespace research
```
---
## Notes & Ideas
### Open Questions
- [ ] Best web search API for research quality?
- [ ] How to handle rate limits across multiple sources?
- [ ] Optimal swarm topology for research tasks?
- [ ] How to measure research quality/depth?
### Future Enhancements
- Real-time collaboration mode
- Research template library
- Domain-specific knowledge bases
- Quality benchmarking system
---
## Resources
- **claude-flow**: https://github.com/ruvnet/claude-flow
- **AgentDB**: https://github.com/louloulin/AgentDB
- **Claude Research (official)**: https://claude.com/blog/research
- **MCP Protocol**: https://modelcontextprotocol.io
---
*Last Updated: December 2024*
*Status: Design Phase*