Integrate Bifrost LLM gateway, add test suite, implement memory pipeline

- Add Bifrost (maximhq/bifrost) as LLM gateway: all inference routes through
  bifrost:8080/v1 with retry logic and observability; VRAMManager keeps direct
  Ollama access for VRAM flush/prewarm operations
- Switch medium model from qwen3:4b to qwen2.5:1.5b (direct call, no tools)
  via _DirectModel wrapper; complex keeps create_deep_agent with qwen3:8b
- Implement out-of-agent memory pipeline: _retrieve_memories pre-fetches
  relevant context (injected into all tiers), _store_memory runs as background
  task after each reply writing to openmemory/Qdrant
- Add tests/unit/ with 133 tests covering router, channels, vram_manager,
  agent helpers; move integration test to tests/integration/
- Add bifrost-config.json with GPU Ollama (qwen2.5:0.5b/1.5b, qwen3:4b/8b,
  gemma3:4b) and CPU Ollama providers
- Integration test 28/29 pass (only grammy fails — no TELEGRAM_BOT_TOKEN)

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

openmemory/server.py

@@ -6,6 +6,7 @@ from mem0 import Memory
 # Extraction LLM — GPU Ollama (qwen3:4b, same model as medium agent)
 # Runs after reply when GPU is idle; spin-wait in agent.py prevents contention
 OLLAMA_GPU_URL = os.getenv("OLLAMA_GPU_URL", "http://host.docker.internal:11436")
+EXTRACTION_MODEL = os.getenv("OLLAMA_EXTRACTION_MODEL", "qwen2.5:1.5b")
 
 # Embedding — CPU Ollama (nomic-embed-text, 137 MB RAM)
 # Used for both search (50-150ms, acceptable) and store-time embedding
@@ -94,7 +95,7 @@ config = {
     "llm": {
         "provider": "ollama",
         "config": {
-            "model": "qwen3:4b",
+            "model": EXTRACTION_MODEL,
             "ollama_base_url": OLLAMA_GPU_URL,
             "temperature": 0.1,  # consistent JSON output
         },