feat: Phase 5.3 - Multi-Agent Learning Infrastructure
Implement intelligent agent learning from Knowledge Graph execution history
with per-task-type expertise tracking, recency bias, and learning curves.
## Phase 5.3 Implementation
### Learning Infrastructure (✅ Complete)
- LearningProfileService with per-task-type expertise metrics
- TaskTypeExpertise model tracking success_rate, confidence, learning curves
- Recency bias weighting: recent 7 days weighted 3x higher (exponential decay)
- Confidence scoring prevents overfitting: min(1.0, executions / 20)
- Learning curves computed from daily execution windows
### Agent Scoring Service (✅ Complete)
- Unified AgentScore combining SwarmCoordinator + learning profiles
- Scoring formula: 0.3*base + 0.5*expertise + 0.2*confidence
- Rank agents by combined score for intelligent assignment
- Support for recency-biased scoring (recent_success_rate)
- Methods: rank_agents, select_best, rank_agents_with_recency
### KG Integration (✅ Complete)
- KGPersistence::get_executions_for_task_type() - query by agent + task type
- KGPersistence::get_agent_executions() - all executions for agent
- Coordinator::load_learning_profile_from_kg() - core KG→Learning integration
- Coordinator::load_all_learning_profiles() - batch load for multiple agents
- Convert PersistedExecution → ExecutionData for learning calculations
### Agent Assignment Integration (✅ Complete)
- AgentCoordinator uses learning profiles for task assignment
- extract_task_type() infers task type from title/description
- assign_task() scores candidates using AgentScoringService
- Fallback to load-based selection if no learning data available
- Learning profiles stored in coordinator.learning_profiles RwLock
### Profile Adapter Enhancements (✅ Complete)
- create_learning_profile() - initialize empty profiles
- add_task_type_expertise() - set task-type expertise
- update_profile_with_learning() - update swarm profiles from learning
## Files Modified
### vapora-knowledge-graph/src/persistence.rs (+30 lines)
- get_executions_for_task_type(agent_id, task_type, limit)
- get_agent_executions(agent_id, limit)
### vapora-agents/src/coordinator.rs (+100 lines)
- load_learning_profile_from_kg() - core KG integration method
- load_all_learning_profiles() - batch loading for agents
- assign_task() already uses learning-based scoring via AgentScoringService
### Existing Complete Implementation
- vapora-knowledge-graph/src/learning.rs - calculation functions
- vapora-agents/src/learning_profile.rs - data structures and expertise
- vapora-agents/src/scoring.rs - unified scoring service
- vapora-agents/src/profile_adapter.rs - adapter methods
## Tests Passing
- learning_profile: 7 tests ✅
- scoring: 5 tests ✅
- profile_adapter: 6 tests ✅
- coordinator: learning-specific tests ✅
## Data Flow
1. Task arrives → AgentCoordinator::assign_task()
2. Extract task_type from description
3. Query KG for task-type executions (load_learning_profile_from_kg)
4. Calculate expertise with recency bias
5. Score candidates (SwarmCoordinator + learning)
6. Assign to top-scored agent
7. Execution result → KG → Update learning profiles
## Key Design Decisions
✅ Recency bias: 7-day half-life with 3x weight for recent performance
✅ Confidence scoring: min(1.0, total_executions / 20) prevents overfitting
✅ Hierarchical scoring: 30% base load, 50% expertise, 20% confidence
✅ KG query limit: 100 recent executions per task-type for performance
✅ Async loading: load_learning_profile_from_kg supports concurrent loads
## Next: Phase 5.4 - Cost Optimization
Ready to implement budget enforcement and cost-aware provider selection.
2026-01-11 13:03:53 +00:00
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// Agents marketplace page
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2026-01-11 21:32:56 +00:00
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use crate::api::{Agent, ApiClient};
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use crate::components::{Badge, Button, Card, GlowColor, NavBar};
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use crate::config::AppConfig;
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feat: Phase 5.3 - Multi-Agent Learning Infrastructure
Implement intelligent agent learning from Knowledge Graph execution history
with per-task-type expertise tracking, recency bias, and learning curves.
## Phase 5.3 Implementation
### Learning Infrastructure (✅ Complete)
- LearningProfileService with per-task-type expertise metrics
- TaskTypeExpertise model tracking success_rate, confidence, learning curves
- Recency bias weighting: recent 7 days weighted 3x higher (exponential decay)
- Confidence scoring prevents overfitting: min(1.0, executions / 20)
- Learning curves computed from daily execution windows
### Agent Scoring Service (✅ Complete)
- Unified AgentScore combining SwarmCoordinator + learning profiles
- Scoring formula: 0.3*base + 0.5*expertise + 0.2*confidence
- Rank agents by combined score for intelligent assignment
- Support for recency-biased scoring (recent_success_rate)
- Methods: rank_agents, select_best, rank_agents_with_recency
### KG Integration (✅ Complete)
- KGPersistence::get_executions_for_task_type() - query by agent + task type
- KGPersistence::get_agent_executions() - all executions for agent
- Coordinator::load_learning_profile_from_kg() - core KG→Learning integration
- Coordinator::load_all_learning_profiles() - batch load for multiple agents
- Convert PersistedExecution → ExecutionData for learning calculations
### Agent Assignment Integration (✅ Complete)
- AgentCoordinator uses learning profiles for task assignment
- extract_task_type() infers task type from title/description
- assign_task() scores candidates using AgentScoringService
- Fallback to load-based selection if no learning data available
- Learning profiles stored in coordinator.learning_profiles RwLock
### Profile Adapter Enhancements (✅ Complete)
- create_learning_profile() - initialize empty profiles
- add_task_type_expertise() - set task-type expertise
- update_profile_with_learning() - update swarm profiles from learning
## Files Modified
### vapora-knowledge-graph/src/persistence.rs (+30 lines)
- get_executions_for_task_type(agent_id, task_type, limit)
- get_agent_executions(agent_id, limit)
### vapora-agents/src/coordinator.rs (+100 lines)
- load_learning_profile_from_kg() - core KG integration method
- load_all_learning_profiles() - batch loading for agents
- assign_task() already uses learning-based scoring via AgentScoringService
### Existing Complete Implementation
- vapora-knowledge-graph/src/learning.rs - calculation functions
- vapora-agents/src/learning_profile.rs - data structures and expertise
- vapora-agents/src/scoring.rs - unified scoring service
- vapora-agents/src/profile_adapter.rs - adapter methods
## Tests Passing
- learning_profile: 7 tests ✅
- scoring: 5 tests ✅
- profile_adapter: 6 tests ✅
- coordinator: learning-specific tests ✅
## Data Flow
1. Task arrives → AgentCoordinator::assign_task()
2. Extract task_type from description
3. Query KG for task-type executions (load_learning_profile_from_kg)
4. Calculate expertise with recency bias
5. Score candidates (SwarmCoordinator + learning)
6. Assign to top-scored agent
7. Execution result → KG → Update learning profiles
## Key Design Decisions
✅ Recency bias: 7-day half-life with 3x weight for recent performance
✅ Confidence scoring: min(1.0, total_executions / 20) prevents overfitting
✅ Hierarchical scoring: 30% base load, 50% expertise, 20% confidence
✅ KG query limit: 100 recent executions per task-type for performance
✅ Async loading: load_learning_profile_from_kg supports concurrent loads
## Next: Phase 5.4 - Cost Optimization
Ready to implement budget enforcement and cost-aware provider selection.
2026-01-11 13:03:53 +00:00
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use leptos::prelude::*;
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use leptos::task::spawn_local;
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use log::warn;
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/// Agents marketplace page
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#[component]
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pub fn AgentsPage() -> impl IntoView {
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let api_client = ApiClient::new(&AppConfig::load());
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let (agents, set_agents) = signal(Vec::<Agent>::new());
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let (loading, set_loading) = signal(true);
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let (error, set_error) = signal(None::<String>);
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// Fetch agents on mount
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Effect::new(move |_| {
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let api = api_client.clone();
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spawn_local(async move {
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match api.fetch_agents().await {
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Ok(a) => {
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set_agents.set(a);
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set_loading.set(false);
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}
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Err(e) => {
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warn!("Failed to fetch agents: {}", e);
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set_error.set(Some(e));
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set_loading.set(false);
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}
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}
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});
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});
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view! {
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<div class="min-h-screen bg-gradient-to-br from-slate-900 via-slate-800 to-slate-900">
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<NavBar />
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<div class="container mx-auto px-6 py-8">
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<h1 class="text-3xl font-bold text-white mb-8">"Agent Marketplace"</h1>
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<Show
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when=move || !loading.get()
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fallback=|| view! {
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<div class="text-center py-12">
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<div class="text-xl text-white">"Loading agents..."</div>
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</div>
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}
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>
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{move || {
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if let Some(err) = error.get() {
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view! {
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<div class="bg-red-500/20 border border-red-500/50 rounded-lg p-6 text-center">
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<div class="text-red-400 font-semibold mb-2">"Error loading agents"</div>
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<div class="text-sm text-red-300">{err}</div>
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</div>
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}.into_any()
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} else if agents.get().is_empty() {
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view! {
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<div class="text-center py-12">
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<div class="text-xl text-gray-400">"No agents available"</div>
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</div>
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}.into_any()
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} else {
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view! {
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<div class="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-6">
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<For
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each=move || agents.get()
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key=|agent| agent.id.clone()
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children=move |agent| {
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let name = agent.name.clone();
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let role = format!("{:?}", agent.role);
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let llm_info = format!("LLM: {} {}", agent.llm_provider, agent.llm_model);
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let capabilities = agent.capabilities.clone();
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view! {
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<Card glow=GlowColor::Cyan hover_effect=true>
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<div class="flex items-start justify-between mb-3">
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<h3 class="text-lg font-semibold text-white">
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{name}
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</h3>
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<Badge class="bg-cyan-500/20 text-cyan-400 text-xs">
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{role}
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</Badge>
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</div>
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<p class="text-gray-400 text-sm mb-4">
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{llm_info}
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</p>
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<div class="flex gap-2 flex-wrap mb-4">
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{capabilities.iter().take(3).map(|cap| {
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let capability = cap.clone();
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view! {
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<Badge class="bg-green-500/20 text-green-400 text-xs">
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{capability}
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</Badge>
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}
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}).collect_view()}
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</div>
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<Button class="w-full">
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"View Agent"
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</Button>
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</Card>
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}
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}
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/>
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</div>
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}.into_any()
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}
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}}
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</Show>
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</div>
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</div>
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}
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}
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