Vapora/docs/setup/secretumvault-integration.md
Jesús Pérez d14150da75 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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Markdown

# SecretumVault Integration
VAPORA integrates with **SecretumVault**, a post-quantum ready secrets management system, for secure credential and API key management across all microservices.
## Overview
SecretumVault provides:
- **Post-quantum cryptography** ready for future-proof security
- **Multi-backend storage** (filesystem, SurrealDB, PostgreSQL, etcd)
- **Fine-grained access control** with Cedar policy engine
- **Secrets server** for centralized credential management
- **CLI tools** for operations and development
## Integration Points
SecretumVault is integrated into these VAPORA services:
| Service | Purpose | Features |
|---------|---------|----------|
| **vapora-backend** | REST API credentials, database secrets, JWT keys | Central secrets management |
| **vapora-agents** | Agent authentication, service credentials | Secure agent-to-service auth |
| **vapora-llm-router** | LLM provider API keys (Claude, OpenAI, Gemini, Ollama) | Cost tracking + credential rotation |
## Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ VAPORA Services │
├─────────────┬──────────────────┬────────────────────────────┤
│ Backend API │ Agent Orchestration │ LLM Router │
└──────┬──────┴────────┬─────────┴──────────┬─────────────────┘
│ │ │
└───────────────┼────────────────────┘
┌─────────────────────────────┐
│ SecretumVault Server │
├─────────────────────────────┤
│ • Credential storage │
│ • Policy enforcement │
│ • Audit logging │
│ • Key rotation │
└──────────┬──────────────────┘
┌───────────┴────────────┐
▼ ▼
Storage Layer Policy Engine
(SurrealDB) (Cedar)
```
## Configuration
### Environment Variables
```bash
# SecretumVault server connection
SECRETUMVAULT_URL=http://secretumvault:3030
SECRETUMVAULT_TOKEN=<identity-token>
# Storage backend
SECRETUMVAULT_STORAGE=surrealdb
SURREAL_URL=ws://surrealdb:8000
SURREAL_DB=secretumvault
# Crypto backend
SECRETUMVAULT_CRYPTO=openssl # or aws-lc for post-quantum
```
### Cargo Features
SecretumVault is integrated with these features enabled:
```toml
secretumvault = { workspace = true }
# Automatically uses: "server", "surrealdb-storage"
```
## Usage Examples
### In vapora-backend
```rust
use secretumvault::SecretClient;
// Initialize client
let client = SecretClient::new(
&env::var("SECRETUMVAULT_URL")?,
&env::var("SECRETUMVAULT_TOKEN")?,
).await?;
// Retrieve API key
let api_key = client.get_secret("llm/claude-api-key").await?;
// Store credential securely
client.store_secret(
"database/postgres-password",
&password,
Some("postgres-creds"),
).await?;
```
### In vapora-llm-router
```rust
use secretumvault::SecretClient;
// Get LLM provider credentials
let openai_key = client.get_secret("llm/openai-api-key").await?;
let claude_key = client.get_secret("llm/claude-api-key").await?;
let gemini_key = client.get_secret("llm/gemini-api-key").await?;
// Fallback to Ollama (local, no key needed)
```
## Running SecretumVault
### Local Development
```bash
# Terminal 1: Start SecretumVault server
cd /Users/Akasha/Development/secretumvault
cargo run --bin secretumvault-server --features server,surrealdb-storage
# Terminal 2: Initialize with default policies
cargo run --bin secretumvault-cli -- init-policies
```
### Production (Kubernetes)
```bash
# Will be added to kubernetes/
kubectl apply -f kubernetes/secretumvault/
```
## Security Best Practices
1. **Token Management**
- Use identity-based tokens (not basic auth)
- Rotate tokens regularly
- Store token in `.env.local` (not in git)
2. **Secret Storage**
- Never commit credentials to git
- Use SecretumVault for all sensitive data
- Enable audit logging for compliance
3. **Policy Enforcement**
- Define Cedar policies per role/service
- Restrict access by principle of least privilege
- Review policies during security audits
4. **Crypto Backend**
- Use `aws-lc` for post-quantum readiness
- Plan migration as quantum threats evolve
## Related Documentation
- [SecretumVault Project](../../../../secretumvault/)
- [VAPORA Architecture](vapora-architecture.md)
- [Security & RBAC](../architecture/roles-permissions-profiles.md)
---
**Integration Status**: ✅ Active
**Services**: Backend, Agents, LLM Router
**Features**: server, surrealdb-storage, cedar-policies