Phase 1 — Documentation cleanup: - Slim README 303→93 lines (remove duplicated architecture, config tables) - Slim CLAUDE.md 267→80 lines (remove project layout, env vars, CI section) - Slim AGENTS.md 342→72 lines (remove duplicated patterns, running locally) - Delete MEMORY.md, WORKSET_PLAN.md, WORKSET_PLAN_PHASE2.md (session docs) - Archive PLAN.md, IMPLEMENTATION_SUMMARY.md to docs/ - Move QUALITY_ANALYSIS.md, QUALITY_REVIEW_REPORT.md to docs/ - Move apply_security_fixes.py, activate_self_tdd.sh to scripts/ Phase 4 — Config & build cleanup: - Fix wheel build: add 11 missing modules to pyproject.toml include list - Add pytest markers (unit, integration, dashboard, swarm, slow) - Add data/self_modify_reports/ and .handoff/ to .gitignore Phase 6 — Token optimization: - Add docstrings to 15 __init__.py files that were empty - Create __init__.py for events/, memory/, upgrades/ modules Root markdown: 87KB → ~18KB (79% reduction) https://claude.ai/code/session_019oMFNvD8uSGSSmBMGkBfQN
11 KiB
11 KiB
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_tooldecorator
Tools Implemented:
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:
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:
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.mdwritten 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
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
# 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
- ✅ MCP Protocol — Full implementation with schemas, registry, server
- ✅ 6 Production Tools — All with error handling and health tracking
- ✅ Event Bus — Async pub/sub for agent communication
- ✅ 6 Agents — Full roster with specialized roles
- ✅ Orchestrator — Intelligent routing logic
- ✅ Memory System — Three-tier architecture
- ✅ All Tests Pass — No regressions
Ready for Phase 3
The foundation is solid. Next steps:
- Cascade Router for LLM failover
- Self-upgrade loop
- Enhanced dashboard views
- Production hardening