Switch from Bifrost to LiteLLM; add Matrix channel; update rules

Infrastructure:
- docker-compose.yml: replace bifrost container with LiteLLM proxy
  (host.docker.internal:4000); complex model → deepseek-r1:free via
  OpenRouter; add Matrix URL env var; mount logs volume
- bifrost-config.json: add auth_config + postgres config_store (archived)

Routing:
- router.py: full semantic 3-tier classifier rewrite — nomic-embed-text
  centroids for light/medium/complex; regex pre-classifiers for all tiers;
  Russian utterance sets expanded
- agent.py: wire LiteLLM URL; add dry_run support; add Matrix channel

Channels:
- channels.py: add Matrix adapter (_matrix_send via mx- session prefix)

Rules / docs:
- agent-pipeline.md: remove /think prefix requirement; document automatic
  complex tier classification
- llm-inference.md: update BIFROST_URL → LITELLM_URL references; add
  remote model note for complex tier
- ARCHITECTURE.md: deleted (superseded by README.md)

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

.claude/rules/llm-inference.md

@@ -1,7 +1,8 @@
 # LLM Inference Rules
 
-- All LLM calls must use `base_url=BIFROST_URL` with model name `ollama/<model>`. Never call Ollama directly for inference.
+- All LLM calls must use `base_url=LITELLM_URL` (points to LiteLLM at `host.docker.internal:4000/v1`). Never call Ollama directly for inference.
 - `_reply_semaphore` (asyncio.Semaphore(1)) serializes all GPU inference. Never bypass it or add a second semaphore.
-- Model names in code always use the `ollama/` prefix: `ollama/qwen3:4b`, `ollama/qwen3:8b`, `ollama/qwen2.5:1.5b`.
-- Timeout values: router=30s, medium=180s, complex=600s. Do not reduce them — GPU inference under load is slow.
-- `VRAMManager` is the only component that contacts Ollama directly (for flush/prewarm/poll). This is intentional — Bifrost cannot manage VRAM.
+- Local Ollama models use the `ollama/` prefix: `ollama/qwen3:4b`, `ollama/qwen2.5:1.5b`. Remote models (e.g. OpenRouter) use their full LiteLLM name: `openrouter/deepseek-r1`.
+- Timeout values: router=30s, medium=180s, complex=600s. Do not reduce them.
+- `VRAMManager` is the only component that contacts Ollama directly (for flush/prewarm/poll). This is intentional — LiteLLM cannot manage VRAM.
+- Complex tier uses a remote model (`DEEPAGENTS_COMPLEX_MODEL`) — no VRAM management is needed for it.