jpmschweitzerandClaude Opus 4.6
a3c7fcf8c3
- Move docs to docs/ (philosophy, roadmap, orchestration scenarios, claude integration, testing improvements) - Strip completed phases from roadmap and claude integration docs - Move dependencies from requirements*.txt into pyproject.toml - Move pytest config from pytest.ini into pyproject.toml - Add Makefile replacing wakeup.sh (setup, run, test, lint, etc.) - Add CI test gate in Gitea Actions workflow - Consolidate caches into .cache/ (pytest, mypy, ruff) - Consolidate build output into build/ (coverage, logs) - Update Dockerfile for pyproject.toml install - Update cross-references in README, AGENTS.md, CLAUDE.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Tatlock Implementation Roadmap
Reference: See philosophy.md for the target architecture and vision
This document tracks open/planned work. Completed phases have been removed.
Current State (v2.0.5)
What we have:
- OpenAI-compatible API (Responses API + Chat Completions)
- Two-tier architecture (Steward → Tatlock)
- Household staff: Tatlock (Butler), Steward, Librarian, Biographer
- Core tools: Calculator, Date/Time, Web search (SearXNG)
- Memory system: Qdrant (vector), Redis (session cache), multi-tenancy via ContextVar
- Dual backend: Claude (preferred) + Ollama (fallback)
- 439 tests with good coverage
Phase 4: Expert Household Staff — Remaining Agents
Goal: Implement remaining domain-specific expert agents
Planned Agents
-
The Developer (Software Development)
- Code generation assistance
- Debugging support
- Documentation generation
- Architecture guidance
-
The Handyman (System Maintenance)
- System status queries
- Log analysis
- Basic troubleshooting
- Infrastructure monitoring
-
The Secretary (Scheduling & Organization)
- Calendar integration
- Task management
- Reminder system
- Schedule conflict detection
-
The Housekeeper (Home Automation)
- Home Assistant integration
- Device control interface
- Status queries
- Automation triggers
Each Agent Includes
- Specialized prompt and personality
- Domain-specific tools
- MCP integration points (where applicable)
- Integration with Butler orchestration
Success Criteria
- Each agent implemented as separate module
- Agents callable via tool framework
- Can invoke specialized models (e.g., Codestral for Developer)
Phase 5: Persistence Layer — Database & Multi-Tenancy
Goal: Add persistent storage and multi-user support
Deliverables
-
PostgreSQL Integration
- Docker compose configuration
- Database schema with tenant isolation
- Alembic migrations
- SQLAlchemy models
-
Multi-Tenant Architecture
- Tenant identification middleware
- Tenant-scoped database sessions
- User authentication system
- Per-tenant data isolation
-
Core Data Models
- Users and tenants
- Conversations and messages (migrate from in-memory)
- Agent interactions log
- System configuration and preferences
Success Criteria
- PostgreSQL container running
- Multiple users authenticate separately
- Each user sees only their own data
- Conversations persist across restarts
- Database migrations work correctly
Phase 7: MCP (Model Context Protocol) Integration
Goal: Enable rich tool integrations via MCP
See also claude-integration.md for MCP server implementation details.
Deliverables
-
MCP Server Framework
- MCP server implementation
- Tool registration via MCP
- Schema validation
- Error handling
-
MCP Client in Agents
- PydanticAI MCP integration
- Tool discovery from MCP servers
- Dynamic tool loading
-
Initial MCP Tools
- File system operations
- Database queries
- API integrations
- System commands
Success Criteria
- MCP server running
- Tools exposed via MCP protocol
- Agents can discover and use MCP tools
- New tools addable without code changes
- MCP tools visible in Steward recommendations
Phase 8: Advanced Memory & Context — Remaining Work
Goal: Implement sophisticated context management and personalization
Open Deliverables
-
Context Management
- Smart context window trimming
- Conversation branching
- Topic tracking
-
Personalization
- User preference learning
- Interaction pattern analysis
- Adaptive responses
- Custom agent personalities per user
Success Criteria
- Conversations automatically embedded to Qdrant
- Memory improves over time (learning from interactions)
Phase 9: Extended Household Staff
Goal: Add specialized agents for additional domains
Future Agents
- The Accountant — Expense tracking, budgets, financial reports
- The Chef — Meal planning, recipes, nutrition tracking
- Others as needs emerge
Phase 10: User Experience Refinement
Goal: Polish the interaction experience
- Personality tuning and consistency
- Better progress indicators
- Response time improvements
- Streaming smoothness
Phase 11: Production Hardening
Goal: Make the system production-ready for homelab deployment
- Complete docker-compose stack
- Health checks and monitoring
- Authentication hardening and rate limiting
- Installation and troubleshooting documentation
Dependencies
Phase 4 (Remaining Agents)
↓
Phase 5 (Database/Multi-Tenancy) ← Can be deferred
↓
Phase 7 (MCP) → Phase 8 (Advanced Memory)
↓
Phase 9 (Extended Staff) → Phase 10 (UX) → Phase 11 (Production)
Can Be Deferred: Phase 5 until you need persistence Parallel Opportunities: Phases 7 and 8 can overlap; 9 and 10 ongoing
Next Steps
- Implement The Developer agent for code assistance
- Add Home Assistant integration for The Housekeeper
- Integrate scheduling service for The Secretary
- MCP server for external Claude access