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