fix(ai): offload model resolution from async paths

Wrap blocking _resolve_model calls in asyncio.to_thread across async model interaction paths so endpoint/model resolution does not stall the event loop. Preserve owner-scoped resolution and add focused regression coverage.

src/agent_tools/model_interaction_tools.py

@@ -10,6 +10,7 @@ Shared helpers that still live in ``src.ai_interaction`` and are used by tools
 not yet migrated (``_resolve_model``, ``AI_CHAT_TIMEOUT``) are imported lazily
 inside the functions to avoid an import cycle at module load.
 """
+import asyncio
 import logging
 from typing import Dict, Optional
 
@@ -46,7 +47,7 @@ async def chat_with_model(content: str, session_id: Optional[str] = None, owner:
         return {"error": "No message provided (line 2+ is the message)"}
 
     try:
-        url, model, headers = _resolve_model(model_spec, owner=owner)
+        url, model, headers = await asyncio.to_thread(_resolve_model, model_spec, owner=owner)
     except ValueError as e:
         return {"error": str(e)}
 
@@ -90,7 +91,7 @@ async def ask_teacher(content: str, session_id: Optional[str] = None, owner: Opt
             return {"error": "No teacher model configured. Specify a model name or set teacher_model in settings."}
 
     try:
-        url, model, headers = _resolve_model(model_spec, owner=owner)
+        url, model, headers = await asyncio.to_thread(_resolve_model, model_spec, owner=owner)
     except ValueError as e:
         return {"error": str(e)}
 

src/agent_tools/session_tools.py

@@ -8,6 +8,7 @@ The session manager is a runtime-set singleton in src.ai_interaction, so each
 function fetches it via get_session_manager() (imported here); _resolve_model and
 AI_CHAT_TIMEOUT are reused from there too.
 """
+import asyncio
 import json
 import logging
 import uuid
@@ -40,7 +41,7 @@ async def create_session(content: str, session_id: Optional[str] = None, owner:
         return {"error": "Session name cannot be empty"}
 
     try:
-        url, model, headers = _resolve_model(model_spec, owner=owner)
+        url, model, headers = await asyncio.to_thread(_resolve_model, model_spec, owner=owner)
     except ValueError as e:
         return {"error": str(e)}
 

src/ai_interaction.py

@@ -14,6 +14,7 @@ These are agent tools — the LLM writes fenced code blocks and they execute
 through the standard agent_tools.py pipeline.
 """
 
+import asyncio
 import json
 import logging
 import uuid
@@ -229,7 +230,7 @@ async def do_pipeline(content: str, session_id: Optional[str] = None, owner: Opt
         if not model_spec or not instruction:
             return {"error": f"Step {i + 1}: both 'model' and 'instruction' are required"}
         try:
-            url, model, headers = _resolve_model(model_spec, owner=owner)
+            url, model, headers = await asyncio.to_thread(_resolve_model, model_spec, owner=owner)
             resolved.append((url, model, headers, instruction))
         except ValueError as e:
             return {"error": f"Step {i + 1}: {e}"}
@@ -624,7 +625,7 @@ async def do_ui_control(content: str, session_id: Optional[str] = None, owner: O
 
         # Resolve the model to validate it exists
         try:
-            url, model_id, headers = _resolve_model(model_spec, owner=owner)
+            url, model_id, headers = await asyncio.to_thread(_resolve_model, model_spec, owner=owner)
         except ValueError as e:
             return {"error": str(e)}
 
@@ -914,7 +915,7 @@ async def do_generate_image(content: str, session_id: Optional[str] = None, owne
     if not model_spec:
         for candidate in ("gpt-image-1.5", "gpt-image-1", "dall-e-3"):
             try:
-                _resolve_model(candidate, owner=owner)
+                await asyncio.to_thread(_resolve_model, candidate, owner=owner)
                 model_spec = candidate
                 break
             except ValueError:
@@ -958,7 +959,7 @@ async def do_generate_image(content: str, session_id: Optional[str] = None, owne
 
     # Resolve the model to find the right endpoint
     try:
-        url, model_id, headers = _resolve_model(model_spec, owner=owner)
+        url, model_id, headers = await asyncio.to_thread(_resolve_model, model_spec, owner=owner)
     except ValueError:
         return {"error": f"No endpoint found with image model '{model_spec}'. "
                 "Configure an OpenAI-compatible endpoint with image generation support."}

src/teacher_escalation.py

@@ -235,7 +235,7 @@ async def _call_teacher(teacher_model_spec: str, prompt: str,
     from src.llm_core import llm_call_async
     from src.ai_interaction import _resolve_model, _TEACHER_SYSTEM_PROMPT
     try:
-        url, model, headers = _resolve_model(teacher_model_spec, owner=owner)
+        url, model, headers = await asyncio.to_thread(_resolve_model, teacher_model_spec, owner=owner)
     except Exception as e:
         logger.warning(f"teacher endpoint not resolvable ({teacher_model_spec!r}): {e}")
         return None
@@ -619,7 +619,7 @@ async def run_teacher_inline(
     # Resolve teacher endpoint
     try:
         from src.ai_interaction import _resolve_model
-        teacher_url, teacher_model, teacher_headers = _resolve_model(teacher_spec, owner=owner)
+        teacher_url, teacher_model, teacher_headers = await asyncio.to_thread(_resolve_model, teacher_spec, owner=owner)
     except Exception as e:
         logger.warning(f"teacher endpoint not resolvable ({teacher_spec!r}): {e}")
         yield (