# Timmy Time Integration Architecture: Eight Deep Dives into Real Deployment

> **Source:** PDF attached to issue #946, written during Veloren exploration phase.
> Many patterns are game-agnostic and apply to the Morrowind/OpenClaw pivot.

## Summary of Eight Deep Dives

### 1. Veloren Client Sidecar (Game-Specific)
- WebSocket JSON-line pattern for wrapping game clients
- PyO3 direct binding infeasible; sidecar process wins
- IPC latency negligible (~11us TCP, ~5us pipes) vs LLM inference
- **Status:** Superseded by OpenMW Lua bridge (#964)

### 2. Agno Ollama Tool Calling is Broken
- Agno issues #2231, #2625, #1419, #1612, #4715 document persistent breakage
- Root cause: Agno's Ollama model class doesn't robustly parse native tool_calls
- **Fix:** Use Ollama's `format` parameter with Pydantic JSON schemas directly
- Recommended models: qwen3-coder:32b (top), glm-4.7-flash, gpt-oss:20b
- Critical settings: temperature 0.0-0.2, stream=False for tool calls
- **Status:** Covered by #966 (three-tier router)

### 3. MCP is the Right Abstraction
- FastMCP averages 26.45ms per tool call (TM Dev Lab benchmark, Feb 2026)
- Total MCP overhead per cycle: ~20-60ms (<3% of 2-second budget)
- Agno has first-class bidirectional MCP integration (MCPTools, MultiMCPTools)
- Use stdio transport for near-zero latency; return compressed JPEG not base64
- **Status:** Covered by #984 (MCP restore)

### 4. Human + AI Co-op Architecture (Game-Specific)
- Headless client treated identically to graphical client by server
- Leverages party system, trade API, and /tell for communication
- Mode switching: solo autonomous play when human absent, assist when present
- **Status:** Defer until after tutorial completion

### 5. Real Latency Numbers
- All-local M3 Max pipeline: 4-9 seconds per full cycle
- Groq hybrid pipeline: 3-7 seconds per full cycle
- VLM inference is 50-70% of total pipeline time (bottleneck)
- Dual-model Ollama on 96GB M3 Max: ~11-14GB, ~70GB free
- **Status:** Superseded by API-first perception (#963)

### 6. Content Moderation (Three-Layer Defense)
- Layer 1: Game-context system prompts (Morrowind themes as game mechanics)
- Layer 2: Llama Guard 3 1B at <30ms/sentence for real-time filtering
- Layer 3: Per-game moderation profiles with vocabulary whitelists
- Run moderation + TTS preprocessing in parallel for zero added latency
- Neuro-sama incident (Dec 2022) is the cautionary tale
- **Status:** New issue created → #1056

### 7. Model Selection (Qwen3-8B vs Hermes 3)
- Three-role architecture: Perception (Qwen3-VL 8B), Decision (Qwen3-8B), Narration (Hermes 3 8B)
- Qwen3-8B outperforms Qwen2.5-14B on 15 benchmarks
- Hermes 3 best for narration (steerability, roleplaying)
- Both use identical Hermes Function Calling standard
- **Status:** Partially covered by #966 (three-tier router)

### 8. Split Hetzner + Mac Deployment
- Hetzner GEX44 (RTX 4000 SFF Ada, €184/month) for rendering/streaming
- Mac M3 Max for all AI inference via Tailscale
- Use FFmpeg x11grab + NVENC, not OBS (no headless support)
- Use headless Xorg, not Xvfb (GPU access required for Vulkan)
- Total cost: ~$200/month
- **Status:** Referenced in #982 sprint plan

## Cross-Reference to Active Issues

| Research Topic | Active Issue | Status |
|---------------|-------------|--------|
| Pydantic structured output for Ollama | #966 (three-tier router) | In progress |
| FastMCP tool server | #984 (MCP restore) | In progress |
| Content moderation pipeline | #1056 (new) | Created from this research |
| Split Hetzner + Mac deployment | #982 (sprint plan) | Referenced |
| VLM latency / perception | #963 (perception bottleneck) | API-first approach |
| OpenMW bridge (replaces Veloren sidecar) | #964 | In progress |