jesus/Vapora
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Vapora/docs/features/README.md

11 lines
732 B
Markdown
Raw Normal View History

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
# Features
VAPORA capabilities and overview documentation.
## Contents
- **[Features Overview](overview.md)** — Complete feature list and descriptions including learning-based agent selection, cost optimization, and swarm coordination
feat(channels): webhook notification channels with built-in secret resolution Add vapora-channels crate with trait-based Slack/Discord/Telegram webhook delivery. ${VAR}/${VAR:-default} interpolation is mandatory inside ChannelRegistry::from_config — callers cannot bypass secret resolution. Fire-and-forget dispatch via tokio::spawn in both vapora-workflow-engine (four lifecycle events) and vapora-backend (task Done, proposal approve/reject). New REST endpoints: GET /channels, POST /channels/:name/test. dispatch_notifications extracted as pub(crate) fn for inline testability; 5 handler tests + 6 workflow engine tests + 7 secret resolution unit tests. Closes: vapora-channels bootstrap, notification gap in workflow/backend layer ADR: docs/adrs/0035-notification-channels.md
2026-02-26 14:49:34 +00:00
- **[Workflow Orchestrator](workflow-orchestrator.md)** — Multi-stage pipelines, approval gates, artifacts, autonomous scheduling, and distributed fire-lock
- **[Notification Channels](notification-channels.md)** — Webhook delivery to Slack, Discord, and Telegram with built-in secret resolution
feat(capabilities): add vapora-capabilities crate with in-process executor dispatch - New vapora-capabilities crate: CapabilitySpec, Capability trait, CapabilityRegistry (parking_lot RwLock), CapabilityLoader (TOML overrides), 3 built-ins (code-reviewer, doc-generator, pr-monitor), 22 tests - Move AgentDefinition to vapora-shared to break capabilities↔agents circular dep - Wire system_prompt into AgentExecutor via LLMRouter.complete_with_budget - AgentCoordinator: in-process task dispatch via DashMap<String, Sender<TaskAssignment>> - server.rs: bootstrap CapabilityRegistry + LLMRouter from env, spawn executors per capability - Landing page: 620 tests, 21 crates, Capability Packages feature box - docs: capability-packages feature guide, ADR-0037, CHANGELOG, SUMMARY EOF
2026-02-26 16:43:28 +00:00
- **[Capability Packages](capability-packages.md)** — Pre-built domain-optimized agent bundles (CodeReviewer, DocGenerator, PRMonitor) with system prompts, tool configs, and LLM preferences
Reference in New Issue Copy Permalink