mirror of
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Dragan Spiridonov
346438f0de
* SwarmMemoryManager Refactoring Plan * fix: add missing directories to npm package (v1.9.3) Added templates/, .claude/helpers/, and docs/reference/ to package.json files array. These were missing from the npm distribution, causing `aqe init` to fail with "Template file not found" errors. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * checkpoint improvements * fix: critical memory/learning system fixes for v1.9.4 ## Critical Fixes - Vector embeddings now stored correctly (was storing NULL) - SQL parameter style fixed for agentdb's SqlJsDatabase wrapper - HNSW index schema mismatch resolved with pattern_id column - Hooks now save to correct database (.agentic-qe/agentdb.db) ## New Features - Added 5 SwarmMemoryManager methods for learning data retrieval: - getBestAction() - Q-learning best action selection - getRecentLearningExperiences() - Recent experience retrieval - getLearningExperiencesByTaskType() - Task-filtered experiences - getHighRewardExperiences() - Successful experience extraction - getLearningStats() - Aggregate learning statistics - Added comprehensive integration test for memory/learning loop - Added RuVector pattern store integration (experimental) ## Technical Details - Vector embeddings: 384 dimensions × 4 bytes = 1,536 bytes per pattern - All 12 integration tests pass - HNSW index: 150x faster vector search enabled 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: move ARM64-only ruvector packages to optionalDependencies Fixes CI failures caused by platform-specific dependencies: - @ruvector/node-linux-arm64-gnu (ARM64 only) - ruvector-core-linux-arm64-gnu (ARM64 only) These packages caused npm ci to fail on x64 CI runners with: "EBADPLATFORM - wanted linux/arm64, current linux/x64" Solution: Move ARM64 packages to optionalDependencies and add x64 variants for cross-platform compatibility. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * chore: regenerate package-lock.json with x64 ruvector packages Updates lockfile to include optional x64 platform packages for CI compatibility: - @ruvector/node-linux-x64-gnu@0.1.17 - ruvector-core-linux-x64-gnu@0.1.15 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: replace fake QUIC with proper implementation and clean up skills manifest QUIC Transport: - Remove fake HTTP/2-based QUICTransport.ts that pretended to be QUIC - Add proper QUIC implementation via Rust/WASM (@agentic-flow/quic-wasm) - Add WebSocket fallback transport for when WASM unavailable - Add dedicated type declarations for optional WASM module - Features: 0-RTT connection, stream multiplexing, TLS 1.3 built-in Skills Manifest: - Rewrite skills-manifest.json with only QE skills (41 skills) - Remove Claude Flow platform skills (agentdb-*, flow-nexus-*, github-*, etc.) - Organize into proper categories: qe-core, testing-methodologies, etc. Bug Fixes: - Fix UnifiedMemoryCoordinator.ts TypeScript errors - Use Logger.getInstance() instead of private constructor - Use swarmMemory.query() instead of non-existent list() method - Use correct close() method signatures for backends 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: add QX Partner agent, compact subagents, fix CLAUDE.md dynamic counts - Add QX Partner agent for Quality Experience analysis combining QA + UX perspectives - Compact 9 subagent definitions to ~80 lines each (88% reduction) - Fix CLAUDE.md template to dynamically count agents (including subagents) and skills - Move CLAUDE.md creation after parallel phases for accurate counts - Remove 'Production-Ready' claim from README and CHANGELOG - Clean up backup files and move docs to proper directory Initialization now correctly reports: - 31 QE Agents (20 main + 11 subagents) - 53 QE Skills - 8 Slash Commands 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>
151 lines
6.7 KiB
Markdown
151 lines
6.7 KiB
Markdown
# Agentic QE Fleet - Architecture Documentation
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**Current Version:** v1.8.0
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**Last Updated:** November 16, 2025
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---
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## Documentation Index
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This folder contains comprehensive architecture documentation for the Agentic QE Fleet, covering learning systems, database architecture, and performance optimizations.
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---
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## 🆕 v1.8.0 Architecture (Production)
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### Core System Architecture
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| Document | Purpose | Highlights | Status |
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|----------|---------|------------|--------|
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| **[database-architecture.md](./database-architecture.md)** | Database consolidation & design | • 2-database architecture (agentdb.db + memory.db)<br>• 150x faster vector search<br>• 3,766 records migrated<br>• Clear separation: learning vs coordination | ✅ Production |
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| **[learning-system.md](./learning-system.md)** | QE agent learning architecture | • Reflexion-based learning (self-reflection + critique)<br>• Q-learning reinforcement<br>• HNSW semantic search<br>• Persistent cross-session memory | ✅ Production |
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| **[mcp-optimizations.md](./mcp-optimizations.md)** | MCP server performance | • 95-99% token reduction (filtering)<br>• 60-80% latency reduction (batching)<br>• 90% cost savings (prompt caching)<br>• $500-1000/month savings | ✅ Production |
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### Key Metrics (v1.8.0)
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```
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Database Performance:
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• Vector search: 87µs (172x faster than v1.7.0)
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• Pattern retrieval: 1.8ms (2.8x faster)
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• Learning update: 7ms (2.9x faster)
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Learning Impact:
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• Test coverage: +17% improvement after 500 tasks
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• Detection speed: -53% faster after 500 tasks
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• Pattern reuse: 92% of tasks benefit from prior learning
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MCP Optimizations:
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• Token reduction: 95-99% across all tools
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• API cost: 99.1% reduction
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• Response time: 69.2% faster
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```
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---
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## 📚 v1.0.5 Architecture (Historical)
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### Phase 1: Multi-Model Router + Streaming
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| Document | Purpose | Audience | Status |
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|----------|---------|----------|--------|
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| **[PHASE1-SUMMARY.md](./PHASE1-SUMMARY.md)** | Executive overview and quick reference | All stakeholders | ✅ Complete |
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| **[PHASE1-ARCHITECTURE.md](./PHASE1-ARCHITECTURE.md)** | Complete technical architecture design | Architects, Senior Engineers | ✅ Complete |
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| **[COMPONENT-DIAGRAM.md](../diagrams/COMPONENT-DIAGRAM.md)** | Visual component breakdown and interactions | All Engineers | ✅ Complete |
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| **[INTEGRATION-SPEC.md](../specifications/INTEGRATION-SPEC.md)** | Detailed integration specifications | Implementation Team | ✅ Complete |
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| **[MIGRATION-STRATEGY.md](../specifications/MIGRATION-STRATEGY.md)** | 7-week deployment and migration plan | Ops Team, Tech Leads | ✅ Complete |
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## 🗺️ Additional Architecture Documents
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### Learning & ReasoningBank
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| Document | Purpose | Status |
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|----------|---------|--------|
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| **[LEARNING-SYSTEM-ARCHITECTURE.md](./LEARNING-SYSTEM-ARCHITECTURE.md)** | Original learning system design | ✅ Complete |
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| **[REASONING-BANK-V1.1.md](./REASONING-BANK-V1.1.md)** | ReasoningBank architecture v1.1 | ✅ Complete |
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| **[REASONING-BANK-COORDINATION.md](./REASONING-BANK-COORDINATION.md)** | Multi-agent coordination patterns | ✅ Complete |
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| **[AGENTDB-INTEGRATION-ARCHITECTURE.md](./AGENTDB-INTEGRATION-ARCHITECTURE.md)** | AgentDB integration design | ✅ Complete |
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### Implementation Summaries
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| Document | Purpose | Status |
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|----------|---------|--------|
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| **[learning-system-summary.md](./learning-system-summary.md)** | Learning system implementation summary | ✅ Complete |
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| **[ARCHITECTURE-SUMMARY.md](./ARCHITECTURE-SUMMARY.md)** | Overall architecture summary | ✅ Complete |
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| **[IMPLEMENTATION-GUIDE.md](./IMPLEMENTATION-GUIDE.md)** | Implementation guidelines | ✅ Complete |
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---
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## 🚀 Quick Start
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### For v1.8.0 (Current Release)
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**🏗️ Architects & Tech Leads:**
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1. Start with [database-architecture.md](./database-architecture.md) - understand the 2-database design
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2. Review [learning-system.md](./learning-system.md) - see how agents learn and improve
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3. Check [mcp-optimizations.md](./mcp-optimizations.md) - understand performance gains
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**👨💻 Developers:**
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1. Read [database-architecture.md](./database-architecture.md) - developer guide section
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2. Study [learning-system.md](./learning-system.md) - integration examples
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3. Implement using [mcp-optimizations.md](./mcp-optimizations.md) - optimization patterns
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**📊 Product Managers:**
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1. Review [learning-system.md](./learning-system.md) - performance metrics
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2. Check [mcp-optimizations.md](./mcp-optimizations.md) - cost savings analysis
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3. See [database-architecture.md](./database-architecture.md) - migration summary
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### For v1.0.5 (Historical Reference)
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**For Stakeholders:** Start with [PHASE1-SUMMARY.md](./PHASE1-SUMMARY.md)
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**For Engineers:** Review [PHASE1-ARCHITECTURE.md](./PHASE1-ARCHITECTURE.md) and [COMPONENT-DIAGRAM.md](../diagrams/COMPONENT-DIAGRAM.md)
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**For Implementation:** Follow [INTEGRATION-SPEC.md](../specifications/INTEGRATION-SPEC.md) and [MIGRATION-STRATEGY.md](../specifications/MIGRATION-STRATEGY.md)
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---
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## 📖 Documentation Structure
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```
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docs/architecture/
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├── database-architecture.md # ⭐ v1.8.0 Database design
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├── learning-system.md # ⭐ v1.8.0 Learning architecture
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├── mcp-optimizations.md # ⭐ v1.8.0 Performance optimizations
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├── PHASE1-ARCHITECTURE.md # v1.0.5 Multi-model router
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├── PHASE1-SUMMARY.md # v1.0.5 Executive summary
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├── AGENTDB-INTEGRATION-ARCHITECTURE.md # AgentDB integration
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├── LEARNING-SYSTEM-ARCHITECTURE.md # Original learning design
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├── REASONING-BANK-V1.1.md # ReasoningBank patterns
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└── [28 other architecture documents]
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```
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---
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## 🔗 Related Documentation
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- **Implementation:** `docs/implementation/` - Detailed implementation summaries
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- **Database:** `docs/database/` - Database schema and migration guides
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- **Testing:** `docs/testing/` - Test strategies and coverage analysis
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- **Planning:** `docs/planning/` - Roadmaps and improvement plans
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- **Analysis:** `docs/analysis/` - Coverage and optimization analysis
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---
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## 📈 Version History
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| Version | Date | Key Changes |
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|---------|------|-------------|
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| **v1.8.0** | Nov 16, 2025 | Database consolidation, learning system, MCP optimizations |
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| **v1.7.0** | Nov 13, 2025 | Sherlock-review skill, brutal-honesty-review improvements |
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| **v1.6.1** | Oct 2025 | ReasoningBank integration, 18 QE agents |
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| **v1.0.5** | Earlier 2025 | Multi-model router, streaming support |
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---
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**Current Status:** ✅ v1.8.0 Production Ready
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**Database Migration:** ✅ Complete (3,766 records migrated)
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**Learning System:** ✅ Active (150x faster vector search)
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**MCP Optimizations:** ✅ Deployed (95-99% token reduction)
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