dialog-reddit-tools

# Deep Research-Optimized Reddit Discovery Architecture ## Executive Summary This document presents a reimagined architecture for Reddit discovery that leverages OpenAI's deep research methodology. Instead of quick searches with basic scoring, this system conducts comprehensive, multi-phase research that can spend 5-30 minutes thoroughly analyzing Reddit communities, synthesizing insights, and producing evidence-based recommendations with full citations. ## Core Philosophy ### From Search to Research **Traditional Approach**: Quick API searches → Basic scoring → Return results **Deep Research Approach**: Research planning → Multi-phase investigation → Evidence collection → Synthesis → Comprehensive report ### Key Paradigm Shifts 1. **Time Investment**: From seconds to minutes (5-30 min research cycles) 2. **Depth Over Speed**: Thorough analysis over quick results 3. **Evidence-Based**: Every recommendation backed by specific examples 4. **Adaptive Research**: Dynamic pivoting based on discoveries 5. **Progressive Delivery**: Initial results enhanced over time ## Architecture Overview ``` ┌─────────────────────────────────────────────────────────┐ │ User Research Request │ └─────────────────────────┬───────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────┐ │ Research Planning Layer │ │ ┌─────────────────┐ ┌──────────────────┐ │ │ │ Research Planner├──►│ Strategy Builder │ │ │ └─────────────────┘ └──────────────────┘ │ └─────────────────────────┬───────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────┐ │ Multi-Phase Research Layer │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Phase 1: │ │ Phase 2: │ │ Phase 3: │ │ │ │ Discovery ├──►│ Analysis ├──►│ Synthesis │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ └─────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────┐ │ Evidence & Citation Layer │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Evidence │ │ Citation │ │ Confidence │ │ │ │ Collector │ │ Tracker │ │ Calculator │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ └─────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────┐ │ Comprehensive Report │ └─────────────────────────────────────────────────────────┘ ``` ## Agent Specifications ### 1. Research Planner Agent **File**: `agents/research_planner.py` **Purpose**: Creates and adapts multi-step research plans for comprehensive Reddit discovery. **Why This Agent?** Deep research requires sophisticated planning that goes beyond simple searches. The Research Planner creates adaptive, multi-phase research strategies that evolve based on discoveries. This agent: - **Decomposes Complex Queries**: Breaks down broad questions into research phases - **Plans Resource Allocation**: Budgets time and API calls across phases - **Adapts Dynamically**: Modifies plan based on intermediate findings - **Identifies Knowledge Gaps**: Recognizes what's missing and plans to fill it **Architectural Role**: - **Strategic Director**: Creates the overall research roadmap - **Resource Manager**: Allocates time budget across research phases - **Adaptation Engine**: Modifies strategy based on findings - **Quality Gatekeeper**: Ensures research meets depth requirements **Key Responsibilities**: - Generate multi-phase research plans - Allocate time budgets per phase - Define success criteria for each phase - Create backtrack strategies for poor results - Monitor research progress and adapt **Tools**: ```python @function_tool def create_research_plan(wrapper: RunContextWrapper[DeepResearchContext], query: str, time_budget: int) -> ResearchPlan: """Generate comprehensive research plan with phases and milestones.""" @function_tool def adapt_plan(wrapper: RunContextWrapper[DeepResearchContext], current_findings: List[Finding], remaining_time: int) -> AdaptedPlan: """Modify research plan based on intermediate discoveries.""" @function_tool def identify_knowledge_gaps(wrapper: RunContextWrapper[DeepResearchContext], collected_evidence: List[Evidence]) -> List[Gap]: """Identify what information is still needed.""" ``` **Model**: `gpt-4o` (complex strategic planning) **Handoffs**: - Discovery Researcher (Phase 1) - Community Analyst (Phase 2) - Content Researcher (Phase 2) - Synthesis Researcher (Phase 3) ### 2. Discovery Researcher Agent **File**: `agents/discovery_researcher.py` **Purpose**: Conducts broad discovery research to map the Reddit landscape for a topic. **Why This Agent?** Unlike simple search, discovery research explores the entire ecosystem around a topic. This agent: - **Maps Community Networks**: Discovers related and adjacent communities - **Identifies Key Players**: Finds influential subreddits and users - **Explores Variations**: Investigates different perspectives and niches - **Builds Foundation**: Creates comprehensive base for deeper analysis **Architectural Role**: - **Landscape Mapper**: Creates comprehensive view of topic on Reddit - **Breadth Provider**: Ensures no major communities are missed - **Network Analyzer**: Maps relationships between communities - **Foundation Builder**: Provides base for deeper investigation **Key Responsibilities**: - Execute broad searches with multiple strategies - Discover primary and adjacent communities - Map community relationships and overlaps - Identify key terminology and variations - Build initial evidence repository **Tools**: ```python @function_tool async def broad_discovery_search(wrapper: RunContextWrapper[DeepResearchContext], topics: List[str], strategies: List[SearchStrategy]) -> DiscoveryResults: """Execute comprehensive discovery across multiple search strategies.""" @function_tool def map_community_network(wrapper: RunContextWrapper[DeepResearchContext], communities: List[Subreddit]) -> CommunityGraph: """Map relationships and overlaps between discovered communities.""" @function_tool def identify_terminology(wrapper: RunContextWrapper[DeepResearchContext], initial_results: DiscoveryResults) -> TerminologyMap: """Extract key terms, variations, and jargon used in communities.""" ``` **Model**: `gpt-4o` (pattern recognition and mapping) **Evidence Collection**: - Community discovery paths - Search strategies used - Relationship mappings - Terminology variations found ### 3. Community Analyst Agent **File**: `agents/community_analyst.py` **Purpose**: Conducts deep analysis of discovered communities to understand culture, quality, and relevance. **Why This Agent?** Surface-level metrics don't reveal community quality or fit. The Community Analyst dives deep into each community to understand: - **Cultural Fit**: How welcoming to target audience - **Content Quality**: Depth and value of discussions - **Activity Patterns**: When and how community engages - **Governance**: Rules, moderation, and community health **Architectural Role**: - **Quality Assessor**: Evaluates community health and value - **Culture Decoder**: Understands community norms and values - **Pattern Identifier**: Recognizes activity and engagement patterns - **Fit Evaluator**: Assesses alignment with user needs **Key Responsibilities**: - Analyze community rules and culture - Evaluate content quality metrics - Assess moderator activity and governance - Study user engagement patterns - Measure beginner-friendliness or expertise level **Tools**: ```python @function_tool async def analyze_community_culture(wrapper: RunContextWrapper[DeepResearchContext], subreddit: str) -> CultureAnalysis: """Deep dive into community culture, rules, and norms.""" @function_tool def evaluate_content_quality(wrapper: RunContextWrapper[DeepResearchContext], posts: List[Post]) -> QualityMetrics: """Assess depth, helpfulness, and value of community content.""" @function_tool def measure_engagement_patterns(wrapper: RunContextWrapper[DeepResearchContext], subreddit: str, timeframe: str) -> EngagementAnalysis: """Analyze when and how users engage with the community.""" ``` **Evidence Collection**: - Exemplar posts demonstrating quality - Community rules and guidelines - Moderator actions and responses - User testimonials and feedback - Activity heatmaps and patterns ### 4. Content Researcher Agent **File**: `agents/content_researcher.py` **Purpose**: Researches actual content within communities to extract insights, resources, and recommendations. **Why This Agent?** Beyond community metrics, the actual content provides the real value. The Content Researcher: - **Extracts Knowledge**: Identifies valuable information and resources - **Finds Patterns**: Recognizes common questions and solutions - **Collects Resources**: Gathers links, guides, and recommendations - **Tracks Trends**: Identifies emerging topics and discussions **Architectural Role**: - **Knowledge Extractor**: Mines communities for valuable information - **Resource Collector**: Gathers useful links and materials - **Pattern Recognizer**: Identifies common themes and solutions - **Trend Spotter**: Finds emerging topics and discussions **Key Responsibilities**: - Extract top resources and guides - Identify frequently asked questions - Collect expert recommendations - Find success stories and case studies - Track trending topics and discussions **Tools**: ```python @function_tool async def extract_top_resources(wrapper: RunContextWrapper[DeepResearchContext], subreddit: str, topic: str) -> List[Resource]: """Extract most valuable resources mentioned in community.""" @function_tool def analyze_discussion_patterns(wrapper: RunContextWrapper[DeepResearchContext], posts: List[Post]) -> DiscussionPatterns: """Identify common questions, problems, and solutions.""" @function_tool def collect_expert_insights(wrapper: RunContextWrapper[DeepResearchContext], subreddit: str) -> List[ExpertInsight]: """Find and extract insights from recognized experts.""" ``` **Evidence Collection**: - Direct links to valuable posts - Quoted expert responses - Resource compilation lists - Success story examples - FAQ patterns with answers ### 5. Trend Analyst Agent **File**: `agents/trend_analyst.py` **Purpose**: Analyzes temporal patterns, emerging trends, and community evolution. **Why This Agent?** Static analysis misses community dynamics. The Trend Analyst provides: - **Temporal Intelligence**: How communities change over time - **Emerging Topics**: What's gaining traction - **Declining Interests**: What's losing relevance - **Seasonal Patterns**: Recurring themes and cycles **Architectural Role**: - **Temporal Analyzer**: Understands community evolution - **Trend Identifier**: Spots emerging and declining topics - **Pattern Predictor**: Anticipates future community directions - **Historical Context Provider**: Adds time-based perspective **Key Responsibilities**: - Track topic popularity over time - Identify emerging communities - Analyze seasonal patterns - Compare historical vs current activity - Predict future trends **Tools**: ```python @function_tool def analyze_temporal_patterns(wrapper: RunContextWrapper[DeepResearchContext], subreddit: str, timeframes: List[str]) -> TemporalAnalysis: """Analyze how community and topics evolve over time.""" @function_tool def identify_emerging_topics(wrapper: RunContextWrapper[DeepResearchContext], communities: List[str]) -> List[EmergingTopic]: """Find topics gaining unusual traction recently.""" @function_tool def compare_historical_activity(wrapper: RunContextWrapper[DeepResearchContext], subreddit: str, periods: List[Period]) -> ActivityComparison: """Compare activity patterns across different time periods.""" ``` ### 6. Evidence Collector Agent **File**: `agents/evidence_collector.py` **Purpose**: Systematically collects, verifies, and organizes evidence to support all findings. **Why This Agent?** Deep research requires rigorous evidence. The Evidence Collector: - **Maintains Chain of Evidence**: Links every claim to sources - **Verifies Information**: Cross-references claims - **Organizes Proof**: Structures evidence for easy reference - **Tracks Confidence**: Rates evidence quality **Architectural Role**: - **Evidence Manager**: Central repository for all proof - **Verification Engine**: Validates claims across sources - **Citation Builder**: Creates proper references - **Confidence Assessor**: Rates evidence strength **Key Responsibilities**: - Collect evidence for each finding - Verify claims across multiple sources - Organize evidence by category - Track citation sources - Rate evidence confidence **Tools**: ```python @function_tool def collect_evidence(wrapper: RunContextWrapper[DeepResearchContext], finding: Finding) -> List[Evidence]: """Collect supporting evidence for a finding.""" @function_tool def verify_claim(wrapper: RunContextWrapper[DeepResearchContext], claim: Claim) -> VerificationResult: """Cross-reference claim across multiple sources.""" @function_tool def rate_evidence_strength(wrapper: RunContextWrapper[DeepResearchContext], evidence: Evidence) -> ConfidenceRating: """Assess the strength and reliability of evidence.""" ``` ### 7. Synthesis Researcher Agent **File**: `agents/synthesis_researcher.py` **Purpose**: Synthesizes all research into comprehensive, actionable insights with full documentation. **Why This Agent?** Raw research needs expert synthesis. The Synthesis Researcher: - **Creates Narratives**: Weaves findings into coherent stories - **Generates Insights**: Derives non-obvious conclusions - **Builds Recommendations**: Creates actionable advice - **Documents Thoroughly**: Provides complete citations **Architectural Role**: - **Master Synthesizer**: Combines all research threads - **Insight Generator**: Creates value from raw data - **Recommendation Engine**: Produces actionable guidance - **Report Builder**: Creates comprehensive documentation **Key Responsibilities**: - Synthesize findings across all phases - Generate key insights and patterns - Create actionable recommendations - Build comprehensive report - Include all citations and evidence **Output Type**: ```python class DeepResearchReport(BaseModel): executive_summary: str research_methodology: ResearchMethodology key_findings: List[Finding] detailed_analysis: DetailedAnalysis recommendations: List[Recommendation] evidence_repository: EvidenceRepository citations: List[Citation] confidence_assessment: ConfidenceReport research_metrics: ResearchMetrics ``` **Model**: `gpt-4o` (complex synthesis and insight generation) ### 8. Backtrack Coordinator Agent **File**: `agents/backtrack_coordinator.py` **Purpose**: Manages research pivots when initial strategies don't yield quality results. **Why This Agent?** Research often requires course correction. The Backtrack Coordinator: - **Recognizes Dead Ends**: Identifies when to pivot - **Manages State**: Preserves valuable findings while changing direction - **Optimizes Pivots**: Chooses best alternative paths - **Learns from Failures**: Improves future research **Architectural Role**: - **Pivot Manager**: Handles strategy changes - **State Preserver**: Maintains valuable findings - **Path Optimizer**: Finds best alternative routes - **Learning Engine**: Improves from failed attempts **Tools**: ```python @function_tool def evaluate_research_quality(wrapper: RunContextWrapper[DeepResearchContext], current_findings: List[Finding]) -> QualityAssessment: """Assess if current research path is yielding quality results.""" @function_tool def generate_pivot_strategies(wrapper: RunContextWrapper[DeepResearchContext], failed_approach: ResearchPath) -> List[AlternativePath]: """Generate alternative research strategies.""" @function_tool def preserve_valuable_findings(wrapper: RunContextWrapper[DeepResearchContext], findings: List[Finding]) -> PreservedFindings: """Save valuable discoveries before pivoting.""" ``` ## Research Phases ### Phase 1: Discovery (5-10 minutes) **Objective**: Map the complete landscape of relevant Reddit communities **Activities**: 1. Broad keyword searches with variations 2. Related community discovery 3. Network mapping 4. Terminology extraction 5. Initial quality assessment **Deliverables**: - Community map with relationships - Initial relevance scores - Terminology glossary - Research refinement suggestions ### Phase 2: Deep Analysis (10-15 minutes) **Objective**: Thoroughly analyze promising communities and content **Activities**: 1. Community culture analysis 2. Content quality evaluation 3. User engagement study 4. Resource extraction 5. Trend identification **Deliverables**: - Detailed community profiles - Quality assessments with evidence - Resource compilations - Trend reports - Expert insights ### Phase 3: Synthesis (5-10 minutes) **Objective**: Transform research into actionable insights and recommendations **Activities**: 1. Cross-community pattern analysis 2. Insight generation 3. Recommendation formulation 4. Evidence compilation 5. Report generation **Deliverables**: - Executive summary - Detailed findings with citations - Actionable recommendations - Evidence repository - Confidence assessments ## Context Models ### Deep Research Context ```python from dataclasses import dataclass, field from typing import Dict, List, Any, Optional import praw @dataclass class DeepResearchContext: # Core components reddit_client: praw.Reddit research_plan: ResearchPlan time_budget: TimeBudget # Research state current_phase: ResearchPhase completed_phases: List[ResearchPhase] = field(default_factory=list) backtrack_stack: List[ResearchPath] = field(default_factory=list) # Evidence management evidence_repository: EvidenceRepository = field(default_factory=EvidenceRepository) citation_tracker: CitationTracker = field(default_factory=CitationTracker) # Findings and insights discoveries: List[Discovery] = field(default_factory=list) insights: List[Insight] = field(default_factory=list) patterns: List[Pattern] = field(default_factory=list) # Performance tracking api_calls: int = 0 research_metrics: ResearchMetrics = field(default_factory=ResearchMetrics) # Adaptive elements learned_patterns: Dict[str, Any] = field(default_factory=dict) optimization_hints: List[str] = field(default_factory=list) @dataclass class ResearchPlan: query: str objectives: List[ResearchObjective] phases: List[PlannedPhase] success_criteria: SuccessCriteria time_allocation: TimeAllocation backtrack_triggers: List[BacktrackTrigger] @dataclass class TimeBudget: total_minutes: int elapsed_minutes: float = 0 phase_budgets: Dict[str, float] = field(default_factory=dict) def remaining(self) -> float: return self.total_minutes - self.elapsed_minutes def can_continue(self) -> bool: return self.remaining() > 1 # At least 1 minute left @dataclass class Evidence: finding: str source: str url: str confidence: float timestamp: float context: str verification_status: str @dataclass class Citation: text: str source: str url: str author: Optional[str] date: Optional[str] relevance_score: float ``` ## Progressive Result Delivery ### Immediate Results (0-30 seconds) - Basic discovery from cache - Previous research on similar topics - Quick relevance assessment ### Early Results (1-3 minutes) - Initial community discoveries - Basic scoring and ranking - Preliminary recommendations ### Intermediate Results (3-10 minutes) - Detailed community analysis - Quality assessments - Resource collections - Trend identification ### Final Results (10-30 minutes) - Complete synthesis - Full evidence documentation - Comprehensive recommendations - Confidence assessments - Future research suggestions ## Implementation Strategy ### Entry Point ```python # tools/deep_research_reddit.py from agents import Agent, Runner, RunConfig from src.agents.research_planner import research_planner from src.models.deep_research_context import DeepResearchContext async def deep_research_reddit( query: str, time_budget_minutes: int = 10, research_depth: Literal["standard", "comprehensive", "exhaustive"] = "standard", progressive_updates: bool = True ) -> DeepResearchReport: """ Conduct deep research on Reddit communities and topics. Args: query: Research question or topic time_budget_minutes: Maximum time for research (5-30) research_depth: Level of research thoroughness progressive_updates: Deliver results progressively Returns: Comprehensive research report with citations """ # Initialize context context = DeepResearchContext( reddit_client=get_reddit_client(), time_budget=TimeBudget(total_minutes=time_budget_minutes), research_plan=ResearchPlan(query=query) ) # Start with research planner result = await Runner.run( starting_agent=research_planner, input=f"Deep research request: {query}", context=context, run_config=RunConfig( max_turns=100, # Allow extensive research workflow_name="Deep Reddit Research", trace_metadata={ "query": query, "depth": research_depth, "time_budget": time_budget_minutes } ) ) return result.final_output ``` ## Advantages Over Traditional Approach ### Quality Improvements 1. **Evidence-Based**: Every finding backed by specific examples 2. **Comprehensive Coverage**: Explores entire topic landscape 3. **Nuanced Understanding**: Goes beyond surface metrics 4. **Verified Information**: Cross-references claims 5. **Contextual Intelligence**: Understands community dynamics ### Capability Enhancements 1. **Adaptive Research**: Pivots based on discoveries 2. **Pattern Recognition**: Identifies non-obvious connections 3. **Trend Analysis**: Temporal understanding 4. **Resource Compilation**: Gathers valuable materials 5. **Expert Extraction**: Finds and cites authorities ### User Benefits 1. **Time Savings**: Hours of research in minutes 2. **Better Decisions**: Evidence-based recommendations 3. **Deeper Insights**: Patterns humans might miss 4. **Complete Documentation**: Full citation trail 5. **Progressive Results**: Value delivered incrementally ## Performance Considerations ### API Management ```python class APIBudget: def __init__(self, max_calls_per_minute: int = 60): self.max_calls_per_minute = max_calls_per_minute self.calls_this_minute = 0 self.minute_start = time.time() async def wait_if_needed(self): """Implement intelligent rate limiting.""" if self.calls_this_minute >= self.max_calls_per_minute: wait_time = 60 - (time.time() - self.minute_start) if wait_time > 0: await asyncio.sleep(wait_time) self.reset_minute() def reset_minute(self): self.calls_this_minute = 0 self.minute_start = time.time() ``` ### Caching Strategy ```python class ResearchCache: def __init__(self, ttl_minutes: int = 60): self.cache = {} self.ttl = ttl_minutes * 60 def get_or_fetch(self, key: str, fetcher: Callable): """Cache research components for reuse.""" if key in self.cache: entry = self.cache[key] if time.time() - entry['timestamp'] < self.ttl: return entry['data'] data = fetcher() self.cache[key] = { 'data': data, 'timestamp': time.time() } return data ``` ### Parallel Execution ```python async def parallel_community_analysis(communities: List[str], context: DeepResearchContext): """Analyze multiple communities in parallel.""" tasks = [] for community in communities: task = asyncio.create_task( analyze_community(community, context) ) tasks.append(task) results = await asyncio.gather(*tasks) return results ``` ## Migration Path ### Phase 1: Parallel Systems (Weeks 1-2) - Build deep research system alongside existing - Share base components (Reddit client, models) - Test with subset of queries ### Phase 2: Progressive Enhancement (Weeks 3-4) - Add deep research as optional mode - Default to quick discovery for simple queries - Activate deep research for complex questions ### Phase 3: Integration (Weeks 5-6) - Unified entry point with intelligent routing - Automatic depth selection based on query - Seamless fallback between modes ### Phase 4: Optimization (Weeks 7-8) - Performance tuning - Cache optimization - Cost analysis and budgeting ## Example Research Scenarios ### Scenario 1: "Best Python communities for data science beginners" **Research Plan**: 1. **Discovery Phase**: Find all Python and data science communities 2. **Analysis Phase**: - Evaluate beginner-friendliness - Assess learning resource quality - Analyze mentor availability 3. **Synthesis Phase**: - Rank communities by criteria - Create learning path recommendations - Compile resource library **Expected Output**: - Top 5 communities with detailed justifications - Learning progression path - Curated resource collection - Weekly activity schedule recommendations - Key people to follow ### Scenario 2: "Emerging AI safety discussions on Reddit" **Research Plan**: 1. **Discovery Phase**: Map AI safety ecosystem 2. **Trend Analysis Phase**: - Track topic evolution - Identify emerging concerns - Find thought leaders 3. **Synthesis Phase**: - Summarize key debates - Highlight consensus/disagreements - Predict future discussions **Expected Output**: - Current state of AI safety discourse - Emerging topics and concerns - Key voices and their positions - Community sentiment analysis - Recommended reading order ## Future Enhancements ### Near-term (3-6 months) 1. **Visual Analysis**: Process images and infographics 2. **Sentiment Analysis**: Deeper emotional understanding 3. **User Profiling**: Analyze influential users 4. **Cross-Platform**: Extend beyond Reddit ### Medium-term (6-12 months) 1. **Predictive Analytics**: Forecast community trends 2. **Personalization**: Learn user preferences 3. **Real-time Monitoring**: Continuous research updates 4. **Integration APIs**: Connect with other tools ### Long-term (12+ months) 1. **Autonomous Research**: Self-directed exploration 2. **Knowledge Graph**: Build comprehensive topic maps 3. **Research Collaboration**: Multi-agent research teams 4. **Scientific Method**: Hypothesis testing and validation ## Conclusion This deep research architecture transforms Reddit discovery from a simple search tool into a comprehensive research system. By embracing longer execution times and multi-phase investigation, we can deliver insights that match or exceed human research quality while maintaining the speed advantage of automation. The key innovation is treating Reddit discovery as a research problem rather than a search problem. This shift enables: - Thorough understanding over quick results - Evidence-based recommendations over assumptions - Adaptive strategies over fixed pipelines - Comprehensive insights over surface metrics This architecture positions the Reddit discovery tool as a true research assistant capable of conducting hours of human-equivalent research in minutes, with full documentation and citations for every finding.