feat(agent): auto compact — proactive session compression to reduce token cost and latency (#2982)

When a user is idle for longer than a configured TTL, nanobot **proactively** compresses the session context into a summary. This reduces token cost and first-token latency when the user returns — instead of re-processing a long stale context with an expired KV cache, the model receives a compact summary and fresh input.

nanobot/nanobot.py

@@ -82,6 +82,7 @@ class Nanobot:
             mcp_servers=config.tools.mcp_servers,
             timezone=defaults.timezone,
             unified_session=defaults.unified_session,
+            session_ttl_minutes=defaults.session_ttl_minutes,
         )
         return cls(loop)