# Timmy Time — Implementation Summary **Date:** 2026-02-25 **Phase:** 1, 2 Complete (MCP, Event Bus, Agents) **Status:** ✅ Ready for Phase 3 (Cascade Router) --- ## What Was Built ### 1. MCP (Model Context Protocol) ✅ **Location:** `src/mcp/` | Component | Purpose | Status | |-----------|---------|--------| | Registry | Tool catalog with health tracking | ✅ Complete | | Server | MCP protocol implementation | ✅ Complete | | Schemas | JSON schema utilities | ✅ Complete | | Bootstrap | Auto-load all tools | ✅ Complete | **Features:** - 6 tools registered with full schemas - Health tracking (healthy/degraded/unhealthy) - Metrics collection (latency, error rates) - Pattern-based discovery - `@register_tool` decorator **Tools Implemented:** ```python web_search # DuckDuckGo search read_file # File reading write_file # File writing (with confirmation) list_directory # Directory listing python # Python execution memory_search # Vector memory search ``` ### 2. Event Bus ✅ **Location:** `src/events/bus.py` **Features:** - Async publish/subscribe - Wildcard pattern matching (`agent.task.*`) - Event history (last 1000 events) - Concurrent handler execution - System-wide singleton **Usage:** ```python from events.bus import event_bus, Event @event_bus.subscribe("agent.task.*") async def handle_task(event): print(f"Task: {event.data}") await event_bus.publish(Event( type="agent.task.assigned", source="timmy", data={"task_id": "123"} )) ``` ### 3. Sub-Agents ✅ **Location:** `src/agents/` | Agent | ID | Role | Key Tools | |-------|-----|------|-----------| | Seer | seer | Research | web_search, read_file, memory_search | | Forge | forge | Code | python, write_file, read_file | | Quill | quill | Writing | write_file, read_file, memory_search | | Echo | echo | Memory | memory_search, read_file, write_file | | Helm | helm | Routing | memory_search | | Timmy | timmy | Orchestrator | All tools | **BaseAgent Features:** - Agno Agent integration - MCP tool registry access - Event bus connectivity - Structured logging - Task execution framework **Orchestrator Logic:** ```python timmy = create_timmy_swarm() # Automatic routing: # - Simple questions → Direct response # - "Remember..." → Echo agent # - Complex tasks → Helm routes to specialist ``` ### 4. Memory System (Previously Complete) ✅ **Three-Tier Architecture:** ``` Tier 1: Hot Memory (MEMORY.md) ↓ Always loaded Tier 2: Vault (memory/) ├── self/identity.md ├── self/user_profile.md ├── self/methodology.md ├── notes/*.md └── aar/*.md Tier 3: Semantic Search └── Vector embeddings over vault ``` **Handoff Protocol:** - `last-session-handoff.md` written at session end - Auto-loaded at next session start --- ## Architecture Diagram ``` ┌─────────────────────────────────────────────────────────────┐ │ USER INTERFACE │ │ (Dashboard/CLI) │ └──────────────────────────┬──────────────────────────────────┘ │ ┌──────────────────────────▼──────────────────────────────────┐ │ TIMMY ORCHESTRATOR │ │ │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Request │ │ Router │ │ Response │ │ │ │ Analysis │→ │ (Helm) │→ │ Synthesis │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ │ └──────────────────────────┬──────────────────────────────────┘ │ ┌──────────────────┼──────────────────┐ │ │ │ ┌───────▼──────┐ ┌───────▼──────┐ ┌───────▼──────┐ │ Seer │ │ Forge │ │ Quill │ │ (Research) │ │ (Code) │ │ (Writing) │ └──────────────┘ └──────────────┘ └──────────────┘ │ ┌───────▼──────┐ ┌───────▼──────┐ │ Echo │ │ Helm │ │ (Memory) │ │ (Routing) │ └──────────────┘ └──────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────┐ │ MCP TOOL REGISTRY │ │ │ │ web_search read_file write_file list_directory │ │ python memory_search │ │ │ └──────────────────────────┬──────────────────────────────────┘ │ ┌──────────────────────────▼──────────────────────────────────┐ │ EVENT BUS │ │ (Async pub/sub, wildcard patterns) │ └──────────────────────────┬──────────────────────────────────┘ │ ┌──────────────────────────▼──────────────────────────────────┐ │ MEMORY SYSTEM │ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ Hot │ │ Vault │ │ Semantic │ │ │ │ MEMORY │ │ Files │ │ Search │ │ │ └──────────┘ └──────────┘ └──────────┘ │ │ │ └─────────────────────────────────────────────────────────────┘ ``` --- ## Testing Results ``` All 973 tests pass ✅ Manual verification: - MCP Bootstrap: ✅ 6 tools loaded - Tool Registry: ✅ web_search, file_ops, etc. - Event Bus: ✅ Events published/subscribed - Agent Imports: ✅ All agents loadable ``` --- ## Files Created ``` src/ ├── mcp/ │ ├── __init__.py │ ├── bootstrap.py # Auto-load tools │ ├── registry.py # Tool catalog │ ├── server.py # MCP protocol │ └── schemas/ │ └── base.py # Schema utilities │ ├── tools/ │ ├── web_search.py # DuckDuckGo search │ ├── file_ops.py # File operations │ ├── code_exec.py # Python execution │ └── memory_tool.py # Memory search │ ├── events/ │ └── bus.py # Event bus │ └── agents/ ├── __init__.py ├── base.py # Base agent class ├── timmy.py # Orchestrator ├── seer.py # Research ├── forge.py # Code ├── quill.py # Writing ├── echo.py # Memory └── helm.py # Routing MEMORY.md # Hot memory memory/ # Vault structure ``` --- ## Usage Example ```python from agents import create_timmy_swarm # Create fully configured Timmy timmy = create_timmy_swarm() # Simple chat (handles directly) response = await timmy.orchestrate("What is your name?") # Research (routes to Seer) response = await timmy.orchestrate("Search for Bitcoin news") # Code (routes to Forge) response = await timmy.orchestrate("Write a Python script to...") # Memory (routes to Echo) response = await timmy.orchestrate("What did we discuss yesterday?") ``` --- ## Next: Phase 3 (Cascade Router) To complete the brief, implement: ### 1. Cascade LLM Router ```yaml # config/providers.yaml providers: - name: ollama-local type: ollama url: http://localhost:11434 priority: 1 models: [llama3.2, deepseek-r1] - name: openai-backup type: openai api_key: ${OPENAI_API_KEY} priority: 2 models: [gpt-4o-mini] ``` Features: - Priority-ordered fallback - Latency/error tracking - Cost accounting - Health checks ### 2. Self-Upgrade Loop - Detect failures from logs - Propose fixes via Forge - Present to user for approval - Apply changes with rollback ### 3. Dashboard Integration - Tool registry browser - Agent activity feed - Memory browser - Upgrade queue --- ## Success Criteria Status | Criteria | Status | |----------|--------| | Start with `python main.py` | 🟡 Need entry point | | Dashboard at localhost | ✅ Exists | | Timmy responds to questions | ✅ Working | | Routes to sub-agents | ✅ Implemented | | MCP tool discovery | ✅ Working | | LLM failover | 🟡 Phase 3 | | Search memory | ✅ Working | | Self-upgrade proposals | 🟡 Phase 3 | | Lightning payments | ✅ Mock exists | --- ## Key Achievements 1. ✅ **MCP Protocol** — Full implementation with schemas, registry, server 2. ✅ **6 Production Tools** — All with error handling and health tracking 3. ✅ **Event Bus** — Async pub/sub for agent communication 4. ✅ **6 Agents** — Full roster with specialized roles 5. ✅ **Orchestrator** — Intelligent routing logic 6. ✅ **Memory System** — Three-tier architecture 7. ✅ **All Tests Pass** — No regressions --- ## Ready for Phase 3 The foundation is solid. Next steps: 1. Cascade Router for LLM failover 2. Self-upgrade loop 3. Enhanced dashboard views 4. Production hardening