fix: empty response handling, /no_think model gating, per-model profiles

- Detect empty LLM responses (no content, no tool calls) instead of
  silently treating them as task completion. Retries once without tools
  before warning the user.
- Gate /no_think system message and chat_template_kwargs to Qwen/QwQ
  models only — sending /no_think to llama3.x caused empty responses.
- Add model_profiles config section for per-model overrides (token
  budget, thinking, temperature, max_tokens) matched by name prefix.
  Applied at startup and on /model switch.
- Update SessionManager on /model switch so session files record the
  correct model.
- Add NDJSON fallback in SSE stream parser for Ollama compatibility.
- Improve read_file error to suggest find_files on FileNotFoundError.
- Add diagnostic logging for empty streams and empty results.

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

app/models/config.py

@@ -17,6 +17,23 @@ class AgentMode(StrEnum):
     AUTO = "auto"
 
 
+class ModelProfile(BaseModel):
+    """Per-model overrides applied when switching models."""
+
+    max_conversation_tokens: int | None = Field(
+        default=None, description="Token budget override for this model's context window"
+    )
+    thinking: bool | None = Field(
+        default=None, description="Override thinking mode for this model"
+    )
+    temperature: float | None = Field(
+        default=None, description="Override sampling temperature"
+    )
+    max_tokens: int | None = Field(
+        default=None, description="Override max response tokens"
+    )
+
+
 class LLMConfig(BaseModel):
     """LLM backend configuration."""
 
@@ -145,6 +162,10 @@ class AppConfig(BaseModel):
     session: SessionConfig = Field(default_factory=SessionConfig)
     debug: DebugConfig = Field(default_factory=DebugConfig)
     skills: SkillsConfig = Field(default_factory=SkillsConfig)
+    model_profiles: dict[str, ModelProfile] = Field(
+        default_factory=dict,
+        description="Per-model overrides keyed by model name prefix",
+    )
 
     @model_validator(mode="after")
     def resolve_workspace_root(self) -> "AppConfig":
@@ -152,6 +173,39 @@ class AppConfig(BaseModel):
         self.agent.workspace_root = self.agent.workspace_root.resolve()
         return self
 
+    def get_model_profile(self, model: str) -> ModelProfile | None:
+        """Find the best matching model profile by prefix.
+
+        Matches the longest prefix first (e.g., "llama3.1" beats "llama3"
+        for model "llama3.1:latest"). Returns None if no profile matches.
+        """
+        model_lower = model.lower().split(":")[0]  # strip tag
+        best_match: str | None = None
+        for key in self.model_profiles:
+            key_lower = key.lower()
+            if model_lower == key_lower or model_lower.startswith(key_lower):
+                if best_match is None or len(key) > len(best_match):
+                    best_match = key
+        return self.model_profiles.get(best_match) if best_match else None
+
+    def apply_model_profile(self, model: str) -> ModelProfile | None:
+        """Apply the matching model profile overrides to the active config.
+
+        Returns the applied profile, or None if no profile matched.
+        """
+        profile = self.get_model_profile(model)
+        if profile is None:
+            return None
+        if profile.max_conversation_tokens is not None:
+            self.agent.max_conversation_tokens = profile.max_conversation_tokens
+        if profile.thinking is not None:
+            self.llm.thinking = profile.thinking
+        if profile.temperature is not None:
+            self.llm.temperature = profile.temperature
+        if profile.max_tokens is not None:
+            self.llm.max_tokens = profile.max_tokens
+        return profile
+
 
 # Default config file location relative to project root
 _DEFAULT_CONFIG_PATH = Path("config/config.yaml")