Add three-tier model routing with VRAM management and benchmark suite

- Three-tier routing: light (router answers directly ~3s), medium (qwen3:4b
  + tools ~60s), complex (/think prefix → qwen3:8b + subagents ~140s)
- Router: qwen2.5:1.5b, temp=0, regex pre-classifier + raw-text LLM classify
- VRAMManager: explicit flush/poll/prewarm to prevent Ollama CPU-spill bug
- agent_factory: build_medium_agent and build_complex_agent using deepagents
  (TodoListMiddleware + SubAgentMiddleware with research/memory subagents)
- Fix: split Telegram replies >4000 chars into multiple messages
- Benchmark: 30 questions (easy/medium/hard) — 10/10/10 verified passing
  easy→light, medium→medium, hard→complex with VRAM flush confirmed

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.12-slim
+
+WORKDIR /app
+
+RUN pip install --no-cache-dir deepagents langchain-ollama langgraph \
+    fastapi uvicorn langchain-mcp-adapters langchain-community httpx
+
+COPY agent.py vram_manager.py router.py agent_factory.py hello_world.py .
+
+CMD ["uvicorn", "agent:app", "--host", "0.0.0.0", "--port", "8000"]