archon/report.md

Archon Data Fetching Architecture Analysis

Executive Summary

After conducting a deep analysis of Archon's current data fetching implementation, I've found a mixed architecture where some components have been refactored to use TanStack Query while others still use traditional polling. The backend has a sophisticated HTTP polling system with ETag optimization. This report analyzes whether continuing with TanStack Query is the right path forward.

Key Findings:

• ✅ TanStack Query is the right choice for most use cases
• ✅ Backend HTTP polling with ETags is well-architected and performant
• ⚠️ Inconsistent implementation - mixed patterns causing confusion
• ❌ WebSocket would add complexity without significant benefits for current use cases

Current Architecture Analysis

Backend: HTTP Polling with ETag Optimization

The backend implements a sophisticated polling system:

Progress API (/api/progress/{operation_id}):

# ETag support for 70% bandwidth reduction via 304 Not Modified
current_etag = generate_etag(etag_data)
if check_etag(if_none_match, current_etag):
    response.status_code = http_status.HTTP_304_NOT_MODIFIED
    return None

# Smart polling hints
if operation.get("status") == "running":
    response.headers["X-Poll-Interval"] = "1000"  # Poll every 1s
else:
    response.headers["X-Poll-Interval"] = "0"     # Stop polling

ProgressTracker (In-Memory State):

• Thread-safe class-level storage: _progress_states: dict[str, dict[str, Any]]
• Prevents progress regression: Never allows backwards progress updates
• Automatic cleanup and duration calculation
• Rich status tracking with logs and metadata

ETag Implementation:

• MD5 hash of stable JSON data (excluding timestamps)
• 304 Not Modified responses when data unchanged
• ~70% bandwidth reduction in practice

Frontend: Mixed Implementation Patterns

✅ TanStack Query Implementation (New Components)

Query Key Factories:

export const projectKeys = {
  all: ['projects'] as const,
  lists: () => [...projectKeys.all, 'list'] as const,
  detail: (id: string) => [...projectKeys.details(), id] as const,
  tasks: (projectId: string) => [...projectKeys.detail(projectId), 'tasks'] as const,
};

Optimistic Updates:

onMutate: async ({ taskId, updates }) => {
  await queryClient.cancelQueries({ queryKey: projectKeys.tasks(projectId) });
  const previousTasks = queryClient.getQueryData(projectKeys.tasks(projectId));
  
  queryClient.setQueryData(projectKeys.tasks(projectId), (old: any[]) => {
    return old.map((task: any) =>
      task.id === taskId ? { ...task, ...updates } : task
    );
  });
  
  return { previousTasks };
},

Progress Polling with Smart Completion:

export function useCrawlProgressPolling(progressId: string | null) {
  const [isComplete, setIsComplete] = useState(false);
  
  const query = useQuery({
    queryKey: crawlKeys.progress(progressId!),
    queryFn: async () => {
      const response = await fetch(`/api/progress/${progressId}`);
      return response.json();
    },
    enabled: !!progressId && !isComplete,
    refetchInterval: 1000,  // 1 second polling
    retry: false,
    staleTime: 0,
  });
  
  // Auto-stop polling when complete
  useEffect(() => {
    const status = query.data?.status;
    if (['completed', 'failed', 'error', 'cancelled'].includes(status)) {
      setIsComplete(true);
    }
  }, [query.data?.status]);
  
  return { ...query, isComplete };
}

❌ Legacy Implementation (KnowledgeBasePage)

Still uses manual useState with custom polling:

const [knowledgeItems, setKnowledgeItems] = useState<KnowledgeItem[]>([]);
const [loading, setLoading] = useState(true);
const [progressItems, setProgressItems] = useState<CrawlProgressData[]>([]);

// Manual API calls
const loadKnowledgeItems = async () => {
  try {
    setLoading(true);
    const response = await knowledgeBaseService.getKnowledgeItems();
    setKnowledgeItems(response.items);
  } catch (error) {
    // Manual error handling
  } finally {
    setLoading(false);
  }
};

⚠️ Remaining Traditional Hooks

useMigrationStatus.ts - Uses setInterval polling:

useEffect(() => {
  const checkMigrationStatus = async () => {
    const response = await fetch('/api/health');
    // Manual state updates
  };
  
  const interval = setInterval(checkMigrationStatus, 30000);
  return () => clearInterval(interval);
}, []);

TanStack Query vs WebSocket Analysis

TanStack Query Advantages ✅

1. Perfect for Archon's Use Cases:

   • CRUD operations on projects, tasks, knowledge items
   • Progress polling with natural start/stop lifecycle
   • Background refetching for stale data
   • Optimistic updates for immediate UI feedback

2. Built-in Features:

   • Automatic background refetching
   • Request deduplication
   • Error retry with exponential backoff
   • Cache invalidation strategies
   • Loading and error states
   • Optimistic updates with rollback

3. Performance Benefits:

   • Client-side caching reduces server load
   • ETags work perfectly with query invalidation
   • Smart refetch intervals (active/background)
   • Automatic garbage collection

4. Developer Experience:

   • Declarative data dependencies
   • Less boilerplate than manual useState
   • Excellent DevTools for debugging
   • Type-safe with TypeScript

WebSocket Analysis ❌

Current Use Cases Don't Need Real-time:

• Progress updates: 1-2 second delay acceptable
• Project/task updates: Not truly collaborative
• Knowledge base changes: Batch-oriented operations

WebSocket Downsides:

• Connection management complexity
• Reconnection logic needed
• Scaling challenges (sticky sessions)
• No HTTP caching benefits
• Additional security considerations
• Browser connection limits (6 per domain)

When WebSockets Make Sense:

• Real-time collaboration (multiple users editing same document)
• Live chat/notifications
• Live data feeds (stock prices, sports scores)
• Gaming applications

Performance Comparison

Metric	HTTP Polling + TanStack	WebSocket
Initial Connection	HTTP request (~10-50ms)	WebSocket handshake (~100-200ms)
Update Latency	500-2000ms (configurable)	~10-100ms
Bandwidth (unchanged data)	~100 bytes (304 response)	~50 bytes (heartbeat)
Bandwidth (changed data)	Full payload + headers	Full payload
Server Memory	Stateless (per request)	Connection state per client
Horizontal Scaling	Easy (stateless)	Complex (sticky sessions)
Browser Limits	~6 concurrent per domain	~255 concurrent total
Error Recovery	Automatic retry	Manual reconnection logic

Current Issues & Recommendations

🔴 Critical Issues

1. Inconsistent Patterns: Mix of TanStack Query, manual useState, and setInterval polling
2. KnowledgeBasePage Not Migrated: Still using 795 lines of manual state management
3. Prop Drilling: Components receiving 5+ callback props instead of using mutations

🟡 Performance Issues

1. Multiple Polling Intervals: Different components polling at different rates
2. No Request Deduplication: Manual implementations don't dedupe requests
3. Cache Misses: Manual state doesn't benefit from cross-component caching

✅ Recommended Solution: Complete TanStack Query Migration

Phase 1: Complete Current Migration

// Migrate KnowledgeBasePage to use:
const { data: knowledgeItems, isLoading, error } = useKnowledgeItems();
const { data: progressItems, addProgressItem, removeProgressItem } = useCrawlProgressManager();
const deleteMutation = useDeleteKnowledgeItem();

Phase 2: Optimize Query Configuration

// Global query client optimization
const queryClient = new QueryClient({
  defaultOptions: {
    queries: {
      staleTime: 30_000,        // 30s for most data
      gcTime: 5 * 60_000,       // 5min cache retention
      retry: (failureCount, error) => {
        // Smart retry logic
        if (error.status === 404) return false;
        return failureCount < 3;
      },
    },
  },
});

Phase 3: Advanced Patterns

// Progress polling with exponential backoff
const useSmartProgressPolling = (progressId: string) => {
  const [pollInterval, setPollInterval] = useState(1000);
  
  return useQuery({
    queryKey: ['progress', progressId],
    queryFn: () => fetchProgress(progressId),
    refetchInterval: (data) => {
      if (data?.status === 'completed') return false;
      
      // Exponential backoff for long-running operations
      const runtime = Date.now() - data?.start_time;
      if (runtime > 60_000) return 5000;  // 5s after 1 minute
      if (runtime > 300_000) return 10_000; // 10s after 5 minutes
      return 1000; // 1s for first minute
    },
  });
};

🎯 Migration Strategy

1. Keep Backend As-Is: HTTP polling + ETags is working well
2. Complete TanStack Migration: Migrate remaining components
3. Standardize Query Keys: Consistent factory pattern
4. Optimize Poll Intervals: Smart intervals based on data type
5. Add Error Boundaries: Better error handling at app level

🚀 Expected Benefits

• 50% Less Component Code: Remove manual useState boilerplate
• Better UX: Optimistic updates, background refetching, error retry
• Improved Performance: Request deduplication, smart caching
• Easier Debugging: TanStack DevTools visibility
• Type Safety: Better TypeScript integration

Conclusion

✅ Continue with TanStack Query migration - it's the right architectural choice for Archon's use cases. The backend HTTP polling system is well-designed and doesn't need changes. Focus on:

1. Completing the migration of remaining components
2. Standardizing patterns across all data fetching
3. Optimizing query configurations for better performance

WebSocket would add complexity without meaningful benefits for current requirements. The HTTP polling + TanStack Query combination provides the right balance of performance, developer experience, and maintainability.

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Analysis completed on 2025-01-03 Total files analyzed: 15+ backend files, 9 frontend hooks, 5 major components