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/agent/loop.py

@@ -105,17 +105,25 @@ class AgentLoop:
             )
         return prompt
 
+    # Models whose chat templates understand /no_think directives.
+    _THINKING_MODEL_PREFIXES = ("qwen", "qwq")
+
+    def _model_supports_no_think(self) -> bool:
+        """Check if the current model uses a thinking chat template."""
+        model_lower = self._config.llm.model.lower()
+        return any(model_lower.startswith(p) for p in self._THINKING_MODEL_PREFIXES)
+
     def _get_messages_with_system_prompt(self) -> list[Message]:
         """Prepend the system prompt to conversation history.
 
-        When thinking is disabled, appends a system-level /no_think directive
-        after the last user message so Qwen 3.x (and similar) chat templates
-        see it, without polluting the user's actual message content.
+        When thinking is disabled on a model that supports it, appends a
+        system-level /no_think directive after the last user message so
+        Qwen 3.x (and similar) chat templates see it.
         """
         system_msg = Message(role="system", content=self._system_prompt)
         history = self._ctx.get_history()
 
-        if not self._config.llm.thinking and history:
+        if not self._config.llm.thinking and self._model_supports_no_think() and history:
             history = list(history)
             # Find last user message and insert a system hint after it
             for i in range(len(history) - 1, -1, -1):
@@ -140,6 +148,7 @@ class AgentLoop:
 
         max_iter = self._config.agent.max_iterations
         reasoning_only_streak = 0
+        empty_streak = 0
         for iteration in range(1, max_iter + 1):
             if self._cancelled:
                 if self._display:
@@ -230,6 +239,36 @@ class AgentLoop:
             # Successful response — reset streak
             reasoning_only_streak = 0
 
+            # Detect completely empty response (no content, no tool calls)
+            if not assistant_msg.content and not assistant_msg.tool_calls:
+                empty_streak += 1
+                self._ctx.pop_last_message()  # Don't keep empty messages
+                if empty_streak >= 2:
+                    if self._display:
+                        self._display.write_warning(
+                            "Model returned repeated empty responses — "
+                            "try a different model or check Ollama logs."
+                        )
+                    break
+                if self._display:
+                    self._display.write_warning("Model returned empty response. Retrying without tools...")
+                # Retry without tool schemas — some models return empty when
+                # tools are in the payload but the model can't handle them.
+                assistant_msg = await self._llm_step(skip_tools=True)
+                if assistant_msg is None:
+                    break
+                if assistant_msg.content:
+                    self._ctx.add_message("assistant", assistant_msg.content)
+                    if self._display:
+                        self._display.write_assistant_message(assistant_msg.content)
+                    self._handler.reset()
+                    break
+                # Still empty even without tools
+                self._handler.reset()
+                continue
+
+            empty_streak = 0  # reset on successful non-empty response
+
             # Display any assistant text content (even if tool calls follow)
             if self._display and assistant_msg.content:
                 self._display.write_assistant_message(assistant_msg.content)
@@ -263,21 +302,25 @@ class AgentLoop:
             if self._display:
                 self._display.write_warning(f"Agent reached maximum iterations ({max_iter}). Stopping.")
 
-    async def _llm_step(self) -> Message | None:
+    async def _llm_step(self, *, skip_tools: bool = False) -> Message | None:
         """Stream one LLM response and return the accumulated Message.
 
         Uses retry-enabled streaming. On mid-stream errors, attempts to recover
         partial content if available.
 
+        Args:
+            skip_tools: If True, send the request without tool schemas (fallback mode).
+
         Returns:
             The assistant Message, or None if an error occurred.
         """
         messages = self._get_messages_with_system_prompt()
         if self._debug:
             self._debug.log_request(messages, self._config.llm.model)
+        tools = None if skip_tools else self._tools_schema
         t0 = time.monotonic()
         try:
-            chunk_iter = self._client.stream_chat_with_retry(messages, tools=self._tools_schema)
+            chunk_iter = self._client.stream_chat_with_retry(messages, tools=tools)
             result = await self._handler.process_stream(chunk_iter)
             if result and self._debug:
                 elapsed = (time.monotonic() - t0) * 1000

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")

app/services/llm.py

@@ -151,8 +151,9 @@ class LLMClient:
         if tools:
             payload["tools"] = tools
 
-        # When thinking is disabled, inject chat_template_kwargs for backends that support it
-        if not self._config.thinking:
+        # When thinking is disabled, inject chat_template_kwargs for backends
+        # that support it (Qwen 3.x thinking models).
+        if not self._config.thinking and self._config.model.lower().startswith(("qwen", "qwq")):
             payload.setdefault("chat_template_kwargs", {})["enable_thinking"] = False
 
         # Merge model-specific extra parameters (e.g., reasoning_effort)
@@ -170,20 +171,32 @@ class LLMClient:
                         status_code=response.status_code,
                     )
 
+                chunk_count = 0
                 async for line in response.aiter_lines():
-                    if not line.startswith("data: "):
+                    line = line.strip()
+                    if not line:
                         continue
 
-                    data = line[6:]  # strip "data: " prefix
-
-                    if data.strip() == "[DONE]":
-                        return
+                    # SSE format: "data: {json}" or "data: [DONE]"
+                    if line.startswith("data: "):
+                        data = line[6:]
+                        if data.strip() == "[DONE]":
+                            break
+                    elif line.startswith("{"):
+                        # Plain NDJSON fallback (some Ollama versions)
+                        data = line
+                    else:
+                        continue
 
                     try:
                         yield json.loads(data)
+                        chunk_count += 1
                     except json.JSONDecodeError:
                         logger.warning("malformed_sse_chunk", data=data[:200])
 
+                if chunk_count == 0:
+                    logger.warning("empty_stream", model=self._config.model)
+
         except httpx.ConnectError as e:
             raise LLMConnectionError(f"Cannot connect to LLM endpoint: {e}") from e
         except httpx.TimeoutException as e:

app/services/session.py

@@ -52,6 +52,10 @@ class SessionManager:
         self._session_dir = workspace_root / config.session_dir
         self._session_id = f"{self._workspace_hash}_{datetime.now(UTC).strftime('%Y%m%d_%H%M%S')}"
 
+    def update_model(self, model: str) -> None:
+        """Update the model name for session metadata."""
+        self._model = model
+
     def save(self, ctx: "SessionContext") -> Path:
         """Save session state to a JSON file via atomic write.
 

app/services/streaming.py

@@ -60,8 +60,10 @@ class StreamHandler:
         """
         thinking_notified = False
         last_update_time = 0.0
+        chunk_count = 0
 
         async for chunk in chunk_iter:
+            chunk_count += 1
             self._process_chunk(chunk)
 
             if not self._display_config.stream_output:
@@ -96,6 +98,14 @@ class StreamHandler:
             self._on_done()
 
         tool_calls = self._build_tool_calls() or None
+
+        if chunk_count > 0 and not self._accumulated_content and not tool_calls:
+            logger.debug(
+                "stream_empty_result",
+                chunks_received=chunk_count,
+                had_reasoning=bool(self._accumulated_reasoning),
+            )
+
         return Message(
             role="assistant",
             content=self._accumulated_content or None,

app/tools/filesystem.py

@@ -65,11 +65,12 @@ class ReadFileTool(BaseTool):
                 error=str(exc),
             )
         except FileNotFoundError as exc:
+            filename = Path(file_path).name
             return ToolResult(
                 tool_call_id=tool_call_id,
                 tool_name=self.name,
                 status=ToolResultStatus.ERROR,
-                error=str(exc),
+                error=f"{exc}. Use find_files to locate it, e.g. find_files(pattern=\"{filename}\")",
             )
         except FileSizeError as exc:
             return ToolResult(

app/ui/app.py

@@ -75,6 +75,9 @@ class SneakyCodeApp(App):
         """Initialize agent components after the app is mounted."""
         setup_logging_for_tui()
 
+        # Apply model profile for the initial model before creating context
+        self._config.apply_model_profile(self._config.llm.model)
+
         self._ctx = SessionContext(self._config)
 
         # Create long-lived agent dependencies (reused across turns)
@@ -255,8 +258,29 @@ class SneakyCodeApp(App):
             else:
                 new_model = parts[1].strip()
                 self._config.llm.model = new_model
+                if self._session_mgr:
+                    self._session_mgr.update_model(new_model)
+                # Apply model-specific profile overrides
+                profile = self._config.apply_model_profile(new_model)
+                if profile and self._ctx:
+                    # Update token budget if the profile overrides it
+                    self._ctx.token_counter.budget = self._config.agent.max_conversation_tokens
                 self.query_one(HeaderPanel).update_model(new_model)
-                log.write(Text(f"Switched to model: {new_model}", style="bold green"))
+                header = self.query_one(HeaderPanel)
+                header.update_tokens(
+                    self._ctx.estimated_tokens if self._ctx else 0,
+                    self._config.agent.max_conversation_tokens,
+                )
+                msg = f"Switched to model: {new_model}"
+                if profile:
+                    overrides = []
+                    if profile.max_conversation_tokens is not None:
+                        overrides.append(f"tokens={profile.max_conversation_tokens:,}")
+                    if profile.thinking is not None:
+                        overrides.append(f"thinking={'on' if profile.thinking else 'off'}")
+                    if overrides:
+                        msg += f" ({', '.join(overrides)})"
+                log.write(Text(msg, style="bold green"))
         elif cmd.split()[0] == "/mode":
             parts = command.split(maxsplit=1)
             if len(parts) == 1:

app/utils/token_counter.py

@@ -36,6 +36,11 @@ class TokenCounter:
         """The configured token budget."""
         return self._budget
 
+    @budget.setter
+    def budget(self, value: int) -> None:
+        """Update the token budget (e.g., when switching models)."""
+        self._budget = value
+
     @property
     def cumulative_usage(self) -> TokenUsage:
         """Cumulative token usage across all tracked calls."""