Jesús Pérez
75e5ebd9a2
chore: ontology sync + 4 NCL ADRs + landing page update
...
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on+re:
- core.ncl: 5 new Practice nodes (notification-channels,
vapora-capabilities, agent-hot-reload-stable-identity,
merkle-audit-trail, notification-channels) + 5 new edges;
knowledge-graph-execution-history updated with HNSW+BM25+RRF
- state.ncl: production-readiness blocker/catalyst updated (hot-reload
complete, BudgetManager/LLMRouter still require restart);
ontoref-integration catalyst updated (vapora-ontology/reflection
crates, api-catalog.json, nickel contracts)
ADRs (NCL):
- adr-013: KG hybrid search — HNSW+BM25+RRF, rejected in-process scan
- adr-014: capability packages — AgentDefinition→vapora-shared,
DashMap shard-before-await constraint
- adr-015: Merkle audit trail — SHA-256 hash chain, rejected HMAC
- adr-016: agent hot-reload — stable_id=role, learning_profiles survive
drain, BudgetManager excluded from reload scope
landing page:
- 2 new feature boxes: VCS-Agnostic Worktree (jj/git), Ontology Protocol
- KG box: 20→28 tests, HNSW+BM25+RRF description
- Agents box: 71→82 tests, hot-reload + stable_id
- tech stack: Rust 21→23 crates, added jj, Radicle, ontoref badges
- status badge: 620→691 tests
2026-04-07 21:06:48 +01:00
Jesús Pérez
c5f4caa2ab
feat(agents): stable identity + hot-reload for zero learning loss on config change
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Introduce stable_id = role on AgentMetadata so learning profiles and KG
execution records survive process restarts and hot-reloads. Previously
every Uuid::new_v4() rotation orphaned accumulated expertise.
- registry: add stable_id field (serde default, backward-compatible),
stable_id_or_role() fallback helper, drain_role(), list_roles()
- coordinator: profile lookup and KG writes use stable_id_or_role()
instead of the ephemeral UUID; drain_role() drops Sender to close
mpsc channels after in-flight messages drain; registry_arc() accessor
- executor: agent_id written to KG now uses stable_id_or_role()
- server: reload_agents() drain-and-respawn function; SIGHUP handler
via while sighup.recv().await.is_some(); POST /reload endpoint;
AppState extended with config_path, router, cap_registry
- fix: SIGHUP recv() spin-loop guard (is_some())
- fix: io_other_error clippy lint in vapora-agents, vapora-llm-router,
vapora-workflow-engine (std::io::Error::other instead of Error::new)
- docs: ADR-0040, CHANGELOG entry, README hot-reload section
2026-03-02 22:54:28 +00:00
Jesús Pérez
27a290b369
feat(kg,channels): hybrid search + agent-inactive notifications
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- KG: HNSW + BM25 + RRF(k=60) hybrid search via SurrealDB 3 native indexes
- Fix schema bug: kg_executions missing agent_role/provider/cost_cents (silent empty reads)
- channels: on_agent_inactive hook (AgentStatus::Inactive → Message::error)
- migration 012: adds missing fields + HNSW + BM25 indexes
- docs: ADR-0036, update ADR-0035 + notification-channels feature doc
2026-02-26 15:32:44 +00:00
Jesús Pérez
4efea3053e
chore: add A2A y RLM
2026-02-16 05:09:51 +00:00
Jesús Pérez
b6a4d77421
feat: add Leptos UI library and modularize MCP server
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2026-02-14 20:10:55 +00:00
Jesús Pérez
cc55b97678
chore: update README and CHANGELOG with workflow orchestrator features
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2026-01-24 02:07:45 +00:00
Jesús Pérez
6e0a706cdb
chore: add cd/ci ops provisioning
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2026-01-12 03:37:18 +00:00
Jesús Pérez
4cbbf3f864
chore: add setup md files
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2026-01-12 03:17:04 +00:00
Jesús Pérez
ff98adba88
chore: fix README.md
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2026-01-11 21:53:30 +00:00
Jesús Pérez
8f6a884f6e
docs: Update README and CHANGELOG with web assets optimization
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- Document web assets restructuring with minification pipeline
* 32% compression (26KB → 18KB)
* Bilingual support (EN/ES) preserved
* Automated minify.sh script for version sync
* Complete README.md guide with examples
- Add web assets structure to project directory layout in README
- New assets/web/ section with source and production versions
- Reference to minification script and documentation
- Include Just recipes documentation for local development
- Show `just help` commands for discovering available recipes
- Document 50+ recipes for build, test, and CI operations
- Update CHANGELOG with infrastructure improvements
- Web assets optimization (32% compression)
- Just recipes CI/CD system (50+ commands)
- Code quality improvements and bug fixes
- Markdown linting compliance achieved
- All tests passing (55/55 in vapora-backend)
- Add build and test results to unreleased changes section
- Clean compilation with 0 warnings in vapora-backend
- 55 tests passing
- Clippy compliance achieved
2026-01-11 20:12:56 +00:00
Jesús Pérez
d14150da75
feat: Phase 5.3 - Multi-Agent Learning Infrastructure
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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
Jesús Pérez
ca3fa91d5d
chore: fix graphs
2025-11-10 12:24:13 +00:00
Jesús Pérez
748606325a
chore: fix graphs
2025-11-10 12:23:35 +00:00
Jesús Pérez
e7264f069d
chore: fix graphs
2025-11-10 12:21:03 +00:00
Jesús Pérez
d095e520f3
chore: fix graphs
2025-11-10 12:20:33 +00:00
Jesús Pérez
5fde6a87da
chore: fix graphs
2025-11-10 12:19:39 +00:00
Jesús Pérez
c97f712573
chore: fix graphs
2025-11-10 12:18:34 +00:00
Jesús Pérez
2669e2822f
chore: fix version
2025-11-10 12:13:05 +00:00
Jesús Pérez
cd8bc02944
chore: fix content
2025-11-10 11:45:23 +00:00
Jesús Pérez
d89a2bc26f
chore: fix content
2025-11-10 11:41:29 +00:00
Jesús Pérez
f9dbd54ca6
init project
2025-11-09 12:27:37 +00:00
