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>

agent_factory.py

@@ -1,13 +1,21 @@
 from deepagents import create_deep_agent
 
 
+class _DirectModel:
+    """Thin wrapper: single LLM call, no tools, same ainvoke interface as a graph."""
+
+    def __init__(self, model):
+        self._model = model
+
+    async def ainvoke(self, input_dict: dict) -> dict:
+        messages = input_dict["messages"]
+        response = await self._model.ainvoke(messages)
+        return {"messages": list(messages) + [response]}
+
+
 def build_medium_agent(model, agent_tools: list, system_prompt: str):
-    """Medium agent: create_deep_agent with TodoList planning, no subagents."""
-    return create_deep_agent(
-        model=model,
-        tools=agent_tools,
-        system_prompt=system_prompt,
-    )
+    """Medium agent: single LLM call, no tools — fast ~3s response."""
+    return _DirectModel(model)
 
 
 def build_complex_agent(model, agent_tools: list, system_prompt: str):