feat(runner): support fallback candidates

Resolve fallbackModels as preset references or explicit inline provider configs so failover uses complete model settings without exposing fallback logic to the agent loop.

Co-authored-by: Cursor <cursoragent@cursor.com>

nanobot/config/schema.py

@@ -74,6 +74,20 @@ class DreamConfig(Base):
         return f"every {hours}h"
 
 
+class InlineFallbackConfig(Base):
+    """One inline fallback model configuration."""
+
+    model: str
+    provider: str
+    max_tokens: int | None = None
+    context_window_tokens: int | None = None
+    temperature: float | None = None
+    reasoning_effort: str | None = None
+
+
+FallbackCandidate = str | InlineFallbackConfig
+
+
 class ModelPresetConfig(Base):
     """A named set of model + generation parameters for quick switching."""
 
@@ -83,7 +97,6 @@ class ModelPresetConfig(Base):
     context_window_tokens: int = 65_536
     temperature: float = 0.1
     reasoning_effort: str | None = None
-    fallback_models: list[str] = Field(default_factory=list)
 
     def to_generation_settings(self) -> Any:
         from nanobot.providers.base import GenerationSettings
@@ -107,6 +120,7 @@ class AgentDefaults(Base):
     context_window_tokens: int = 65_536
     context_block_limit: int | None = None
     temperature: float = 0.1
+    fallback_models: list[FallbackCandidate] = Field(default_factory=list)
     max_tool_iterations: int = 200
     max_concurrent_subagents: int = Field(default=1, ge=1)
     max_tool_result_chars: int = 16_000
@@ -288,6 +302,9 @@ class Config(BaseSettings):
         name = self.agents.defaults.model_preset
         if name and name != "default" and name not in self.model_presets:
             raise ValueError(f"model_preset {name!r} not found in model_presets")
+        for fallback in self.agents.defaults.fallback_models:
+            if isinstance(fallback, str) and fallback not in self.model_presets:
+                raise ValueError(f"fallback_models entry {fallback!r} not found in model_presets")
         return self
 
     def resolve_default_preset(self) -> ModelPresetConfig: