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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.
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// vapora-agents: Agent configuration module
// Load and parse agent definitions from TOML
use serde::{Deserialize, Serialize};
use std::path::Path;
use thiserror::Error;
#[derive(Debug, Error)]
pub enum ConfigError {
#[error("Failed to read config file: {0}")]
ReadError(#[from] std::io::Error),
#[error("Failed to parse TOML: {0}")]
ParseError(#[from] toml::de::Error),
#[error("Invalid configuration: {0}")]
ValidationError(String),
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AgentConfig {
pub registry: RegistryConfig,
pub agents: Vec<AgentDefinition>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RegistryConfig {
#[serde(default = "default_max_agents")]
pub max_agents_per_role: u32,
#[serde(default = "default_health_check_interval")]
pub health_check_interval: u64,
#[serde(default = "default_agent_timeout")]
pub agent_timeout: u64,
}
fn default_max_agents() -> u32 {
5
}
fn default_health_check_interval() -> u64 {
30
}
fn default_agent_timeout() -> u64 {
300
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AgentDefinition {
pub role: String,
pub description: String,
pub llm_provider: String,
pub llm_model: String,
#[serde(default)]
pub parallelizable: bool,
#[serde(default = "default_priority")]
pub priority: u32,
#[serde(default)]
pub capabilities: Vec<String>,
}
fn default_priority() -> u32 {
50
}
impl AgentConfig {
/// Load configuration from TOML file
pub fn load<P: AsRef<Path>>(path: P) -> Result<Self, ConfigError> {
let content = std::fs::read_to_string(path)?;
let config: Self = toml::from_str(&content)?;
config.validate()?;
Ok(config)
}
/// Load configuration from environment or default file
pub fn from_env() -> Result<Self, ConfigError> {
let config_path = std::env::var("VAPORA_AGENT_CONFIG")
.unwrap_or_else(|_| "/etc/vapora/agents.toml".to_string());
if Path::new(&config_path).exists() {
Self::load(&config_path)
} else {
// Return default config if file doesn't exist
Ok(Self::default())
}
}
/// Validate configuration
fn validate(&self) -> Result<(), ConfigError> {
// Check that all agent roles are unique
let mut roles = std::collections::HashSet::new();
for agent in &self.agents {
if !roles.insert(&agent.role) {
return Err(ConfigError::ValidationError(format!(
"Duplicate agent role: {}",
agent.role
)));
}
}
// Check that we have at least one agent
if self.agents.is_empty() {
return Err(ConfigError::ValidationError(
"No agents defined in configuration".to_string(),
));
}
Ok(())
}
/// Get agent definition by role
pub fn get_by_role(&self, role: &str) -> Option<&AgentDefinition> {
self.agents.iter().find(|a| a.role == role)
}
/// List all agent roles
pub fn list_roles(&self) -> Vec<String> {
self.agents.iter().map(|a| a.role.clone()).collect()
}
}
impl Default for AgentConfig {
fn default() -> Self {
Self {
registry: RegistryConfig {
max_agents_per_role: default_max_agents(),
health_check_interval: default_health_check_interval(),
agent_timeout: default_agent_timeout(),
},
agents: vec![AgentDefinition {
role: "developer".to_string(),
description: "Code developer".to_string(),
llm_provider: "claude".to_string(),
llm_model: "claude-sonnet-4".to_string(),
parallelizable: true,
priority: 80,
capabilities: vec!["coding".to_string()],
}],
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.
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}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_default_values() {
let config = AgentConfig {
registry: RegistryConfig {
max_agents_per_role: 5,
health_check_interval: 30,
agent_timeout: 300,
},
agents: vec![AgentDefinition {
role: "developer".to_string(),
description: "Code developer".to_string(),
llm_provider: "claude".to_string(),
llm_model: "claude-sonnet-4".to_string(),
parallelizable: true,
priority: 80,
capabilities: vec!["coding".to_string()],
}],
};
assert!(config.validate().is_ok());
}
#[test]
fn test_duplicate_roles() {
let config = AgentConfig {
registry: RegistryConfig {
max_agents_per_role: 5,
health_check_interval: 30,
agent_timeout: 300,
},
agents: vec![
AgentDefinition {
role: "developer".to_string(),
description: "Code developer 1".to_string(),
llm_provider: "claude".to_string(),
llm_model: "claude-sonnet-4".to_string(),
parallelizable: true,
priority: 80,
capabilities: vec![],
},
AgentDefinition {
role: "developer".to_string(),
description: "Code developer 2".to_string(),
llm_provider: "claude".to_string(),
llm_model: "claude-sonnet-4".to_string(),
parallelizable: true,
priority: 80,
capabilities: vec![],
},
],
};
assert!(config.validate().is_err());
}
#[test]
fn test_get_by_role() {
let config = AgentConfig {
registry: RegistryConfig {
max_agents_per_role: 5,
health_check_interval: 30,
agent_timeout: 300,
},
agents: vec![AgentDefinition {
role: "architect".to_string(),
description: "System architect".to_string(),
llm_provider: "claude".to_string(),
llm_model: "claude-opus-4".to_string(),
parallelizable: false,
priority: 100,
capabilities: vec!["architecture".to_string()],
}],
};
let agent = config.get_by_role("architect");
assert!(agent.is_some());
assert_eq!(agent.unwrap().description, "System architect");
assert!(config.get_by_role("nonexistent").is_none());
}
}