Transformar Neuro Agent en un asistente CLI de programación de nivel enterprise que compita directamente con Claude Code y GitHub Copilot en características, performance y experiencia de usuario.
Goal: Hacer que Neuro se sienta tan rápido y transparente como Claude Code
Status: ✅ COMPLETADO - 4 commits realizados
Commit 1: 37a23da - Cache + Progress (40%)
-
Classification Cache con Fuzzy Matching
- LRU cache (capacidad 100)
- Jaccard similarity (umbral 0.85)
- 20-40x speedup en queries similares
- 5 tests pasando
-
Real-time Progress Tracking
- 5 stages: Classifying → SearchingContext → ExecutingTool → Generating → Complete
- Feedback detallado con timing
- Integración con TUI
- Canal mpsc no-bloqueante
Commit 2: 5c19c3a - Parallel Execution (20%) 3. Parallel Tool Execution
- Ejecutar herramientas independientes en paralelo
tokio::spawn()+futures::join_all()- 2-3x speedup para multi-tool queries
- 6 tests pasando (100%)
- Análisis de dependencias inteligente
Commit 3: c97db5b - Cleanup (20%) 4. PlanningOrchestrator Removal
- Convertido en stub con panic!()
- main.rs solo RouterOrchestrator
- task_progress.rs módulo independiente
- -1,611 líneas eliminadas
- 114 tests pasando
Commit 4: 905b65f - Streaming (20%) 5. Streaming Responses in TUI
- Display token-by-token vía Ollama streaming API
- streaming.rs módulo (171 lines)
- 200-500ms first token, 30-50 tokens/sec
- BackgroundMessage::Chunk para UI
- HTTP streaming con reqwest
Goal: Comprender el proyecto tan bien como GitHub Copilot
Status: ✅ 100% completado (139 tests passing, +17 desde Sprint 1)
Metrics:
- Total commits: 6 (e43d98a, bd4aca0, bdace15, 2a95d20, 92a49f7, docs)
- Lines added: ~1,240+ (191 + 215 + 406 + 314 + 522)
- Tests added: +17 (2 + 9 + 7 = 18 total from Sprint 2)
- Performance: Incremental RAPTOR <5s vs 30-60s full rebuild
Commit 1: e43d98a - Related Files Core (30%)
- RelatedFilesDetector Core
- src/context/related_files.rs (191 lines)
- 4 relation types: Import, Test, Documentation, Dependency
- Confidence scores (0.0-1.0)
- Language-aware detection (.rs, .py, .js, .ts, .go, etc.)
- 2 unit tests
Commit 2: bd4aca0 - Related Files Integration (30%) 2. RouterOrchestrator Integration
- get_context_files() method (215 lines)
- Confidence filtering (threshold ≥0.7)
- Incremental additions to router_orchestrator.rs
Commit 3: bdace15 - Auto-include in Process (30%) 3. Auto-include Related Files in process()
- enrich_with_related_files() method (130+ lines)
- 7 regex patterns (Spanish + English)
- File detection: analiza, lee, revisa, muestra, file, etc.
- 4-step enrichment pipeline
Commit 4: 2a95d20 - Git-Aware Context (30%) 4. Git-Aware Context System
- src/context/git_context.rs (299 lines)
- GitChangeType enum (Added, Modified, Deleted, Untracked)
- Cache with 60s TTL (reduce git command overhead)
- Methods: current_branch(), get_recently_modified(days), get_uncommitted_changes()
- Priority boost system: +0.3 uncommitted, +0.2 recent (7d), +0.1 very recent (24h)
- enrich_with_git_context() in RouterOrchestrator (116 lines)
- 7 unit tests + 2 integration tests
Commit 5: 92a49f7 - Incremental RAPTOR (30%) 5. Incremental RAPTOR Updates
- src/raptor/incremental.rs (463 lines)
- FileTracker: Modification time tracking (HashMap<PathBuf, SystemTime>)
- IncrementalUpdater: Selective re-indexing (only changed files)
- Extension filtering: .rs, .py, .js, .ts, .tsx, .jsx, .go, .java, .c, .cpp, .h, .hpp
- Ignore patterns: target/, node_modules/, .git/, dist/, .venv/, .cache/, build/
- Performance: <5s incremental vs 30-60s full rebuild
- Public methods: incremental_update(), incremental_stats()
- 6 unit tests + 1 integration test
Achievements:
- ✅ Related files detection with confidence scoring
- ✅ Git-aware context with priority boosting
- ✅ Incremental RAPTOR with file tracking
- ✅ Auto-enrichment in process() pipeline
- ✅ Performance optimizations (cache, incremental)
- ✅ Test coverage: +17 tests (from 122 → 139)
Goal: Manejar tareas complejas como un programador senior
-
Multi-step Task Execution
- Descomponer tareas grandes automáticamente
- Ejecutar steps con checkpoints
- Rollback en caso de error
# User: "migra de reqwest a hyper" # Neuro ejecuta: # 1. [✓] Analizar uso actual de reqwest # 2. [✓] Generar plan de migración # 3. [⏸️] Reemplazar imports... (checkpoint) # 4. [ ] Adaptar código cliente # 5. [ ] Ejecutar tests
-
Interactive Diff Preview
- Mostrar cambios antes de aplicar (como
git diff) - Opciones: [y]es / [n]o / [e]dit / [s]plit
- Modo safe-by-default
# Before applying file_write --- a/src/config/mod.rs +++ b/src/config/mod.rs @@ -45,7 +45,10 @@ pub fn load() -> Result<AppConfig> { - let path = "config.json"; + let path = std::env::var("NEURO_CONFIG") + .unwrap_or_else(|_| "config.json".to_string()); serde_json::from_str(&std::fs::read_to_string(path)?) } Apply changes? [y/n/e/s] █
- Mostrar cambios antes de aplicar (como
-
Undo/Redo Stack
- Revertir operaciones de archivo
- Stack de 10 operaciones
/undoy/redoslash commands
/undo # Revierte último write_file # "Revertido: write_file src/main.rs (150 lines)"
-
Session Management
- Guardar conversación con contexto
- Resumir sesión previa
- Continuar donde dejaste
# Retomar sesión neuro --session refactoring-2025-01-07 # "Continuando desde: 'refactor config module'"
Goal: Experiencia profesional lista para producción
-
Smart Error Recovery
- Auto-fix errores comunes (import missing, type mismatch)
- Sugerir correcciones en lugar de solo reportar
- Retry con contexto mejorado
# Error: "cannot find function `parse_json`" # Neuro: "❌ Error de compilación detectado # 💡 Sugerencias: # 1. Agregar import: use serde_json::from_str as parse_json; # 2. ¿Quisiste decir `serde_json::from_str`? # [1] Aplicar fix automáticamente"
-
Code Review Mode
- Análisis profundo pre-commit
- Detectar code smells
- Sugerir mejoras de performance
/code-review src/agent/ # "📊 Análisis de 5 archivos: # ✓ Estilo: 98/100 # ⚠ Complejidad: 3 funciones >50 lines # ⚠ Tests: Cobertura 67% (objetivo: 80%)"
-
Context Preloading
- Pre-cargar RAPTOR al iniciar
- Mantener embeddings en memoria
- Reduce latencia first-query de 5s a 500ms
-
Performance Benchmarks
- Medir tiempo por operación
- Comparar con baselines
- Alertar si regresiones
-
Production Monitoring
- Logs estructurados con tracing
- Métricas de uso (cache hit rate, avg latency)
- Error tracking
| Feature | Claude Code | GitHub Copilot | Neuro Agent | Status |
|---|---|---|---|---|
| Context Understanding | ||||
| Whole project context | ✅ | ✅ | ✅ RAPTOR | Done |
| Git-aware context | ✅ | ✅ | 🚧 | Sprint 2 |
| Auto-include related files | ✅ | 🚧 | Sprint 2 | |
| Incremental indexing | ✅ | ✅ | 🚧 | Sprint 2 |
| Performance | ||||
| Streaming responses | ✅ | ✅ | 🚧 | Sprint 1 |
| Cache similar queries | ✅ Fuzzy | Done | ||
| Parallel tool exec | ✅ | N/A | 🚧 | Sprint 1 |
| Sub-second first response | ✅ | ✅ | 🚧 | Sprint 4 |
| Workflows | ||||
| Multi-step tasks | ✅ | 🚧 | Sprint 3 | |
| Interactive diff | ✅ | 🚧 | Sprint 3 | |
| Undo/redo | ✅ | ❌ | 🚧 | Sprint 3 |
| Session persistence | ✅ | 🚧 | Sprint 3 | |
| Developer Experience | ||||
| Real-time progress | ✅ | ✅ 5 stages | Done | |
| Code review mode | ✅ | 🚧 | Sprint 4 | |
| Error recovery | ✅ | 🚧 | Sprint 4 | |
| Slash commands | ✅ 20+ | ❌ | ✅ 15+ | Done |
| Technical | ||||
| Local models | ❌ Cloud | ❌ Cloud | ✅ Ollama | Advantage |
| Provider choice | ❌ Anthropic | ❌ OpenAI | ✅ 4 providers | Advantage |
| Full control | ❌ | ❌ | ✅ Open source | Advantage |
| API cost | $$ Medium | $$$ High | $ Ollama free | Advantage |
Legend: ✅ Full support |
- Classification cache with fuzzy matching
- Real-time progress tracking
- Parallel tool execution (2 days)
- Streaming responses (2 days)
- Auto-include related files (3 days)
- Git-aware context (2 days)
- Incremental RAPTOR updates (3 days)
- Interactive diff preview (2 days)
- Multi-step task execution (3 days)
- Undo/redo stack (1 day)
- Session management (1 day)
- Smart error recovery (3 days)
- Code review mode (2 days)
- Context preloading (2 days)
- Performance benchmarks (1 day)
- Production monitoring (2 days)
- Sin enviar código a la nube
- Compliance-friendly (GDPR, SOC2)
- Funciona offline
- Ollama (local gratis)
- OpenAI, Anthropic, Groq (cloud)
- Cambio dinámico de providers
- Ver decisiones del router en debug mode
- Cache hit/miss stats visibles
- Logs estructurados con tracing
- Mejor comprensión de proyectos grandes
- Resumen jerárquico automático
- Menos falsos positivos que flat embeddings
- No requiere IDE específico
- Funciona en SSH/remote
- Scripts automatizables
| Metric | Current | Target | Improvement |
|---|---|---|---|
| First query latency | 3-5s | <1s | 5x faster |
| Similar query latency | 50-100ms | <50ms | 2x faster |
| Cache hit rate | N/A | 25-35% | New capability |
| Parallel tool speedup | 1x | 2-3x | 3x faster |
| Context loading | 5-10s | <1s | 10x faster |
| Metric | Current | Target |
|---|---|---|
| Time to value (TTV) | 30s+ | <10s |
| User satisfaction | N/A | 8/10+ |
| Task completion rate | N/A | 90%+ |
| Undo usage | 0% | 10-15% |
| Metric | Current | Target |
|---|---|---|
| Test coverage | ~60% | 80%+ |
| Code quality (Clippy) | Good | Excellent |
| Documentation | Basic | Comprehensive |
| Error recovery | Manual | 80% auto |
-
Remove PlanningOrchestrator (deprecated)
- Migration guide already exists
- Full RouterOrchestrator adoption
- Target: Feb 2026
-
Standardize Error Types
- Use
thiserrorconsistently - Better error messages
- Error codes for automation
- Use
-
Async Tool Trait
- All tools should be async
- Remove blocking calls
- Better cancellation support
-
Tool Registry Refactor
- Dynamic tool loading
- Plugin system for custom tools
- MCP server integration
-
State Management
- More structured AgentState
- Better serialization
- Version migrations
- Architecture deep dive
- Tool development guide
- Provider integration guide
- Testing best practices
- Quick start guide
- Slash command reference
- Configuration examples
- Troubleshooting guide
- Rust API docs (rustdoc)
- MCP protocol docs
- WebSocket streaming docs
- Rust Async: tokio.rs
- TUI Development: ratatui.rs
- LLM Agents: rig-rs docs
- Embeddings: fastembed docs
- Ollama Setup: ollama.ai/docs
- RAPTOR Paper: arxiv.org/abs/2401.18059
- Model Context Protocol: modelcontextprotocol.io
- Target: Week 1
- Features: Cache + Progress + Parallel + Streaming
- Users: Internal team only
- Feedback: GitHub issues
- Target: Week 3
- Features: + Context intelligence
- Users: Open beta (100+ users)
- Feedback: User surveys
- Target: Week 5
- Features: + Workflows
- Users: Public RC
- Feedback: Bug bounty program
- Target: Week 7
- Features: Complete feature set
- Users: General availability
- Support: Official docs + Discord
- .github/copilot-instructions.md - AI agent guidance
- SPRINT_1_REPORT.md - Sprint 1 detailed report
- TUI_ROUTER_INTEGRATION.md - TUI integration guide
- CONTRIBUTING.md - Contribution guidelines
- tests/README.md - Testing documentation
Last Updated: 2025-01-07 Status: Sprint 1 at 60% completion Next Milestone: Parallel tool execution (2 days ETA)
GitHub Issues: https://github.com/madkoding/neuro-agent/issues Discord: [Coming soon] Email: [Contact maintainers]
Let's build the best local AI coding assistant! 🚀