fix journey setup: honor FE session_id, seed LLM history, and force template on max turns

- Use session_id from the FE frame so replies match the listener key
- Seed conversation with a user message for LLM provider compatibility
- On max turns, nudge the LLM and immediately re-invoke to force
  prompt_template generation instead of deferring to next message
- Fix display_message extraction to safely check for template markers

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

app/api/routes/agent_setup.py

@@ -252,7 +252,9 @@ async def handle_journey_start(
     data_types = frame.get("data_types", [])
     existing_template = frame.get("existing_template")
 
-    session_id = str(uuid.uuid4())
+    # Use the session_id provided by the FE so the reply matches the
+    # listener key; fall back to a generated one if absent.
+    session_id = frame.get("session_id") or str(uuid.uuid4())
     system_prompt = _build_system_prompt(directory, data_types, existing_template)
 
     session = JourneySession(
@@ -266,12 +268,18 @@ async def handle_journey_start(
 
     # The LLM will explore the directory using FILESYSTEM_TOOLS via the
     # ws_context executor (already set by the WS handler before calling us).
+    # Seed with an initial user message — some providers (e.g. GitHub Copilot)
+    # require at least one user/input message to be present.
+    seed_history: list[dict[str, Any]] = [
+        {"role": "user", "content": "Hi, I'm ready to set up my agent. Please explore my directory and ask me your first question."},
+    ]
     ai_reply = await _call_llm_with_tools(
         system_prompt=system_prompt,
-        history=[],
+        history=seed_history,
         tools=list(FILESYSTEM_TOOLS),
     )
 
+    session.history.extend(seed_history)
     session.history.append({"role": "assistant", "content": ai_reply})
     _sessions[session_id] = session
 
@@ -341,25 +349,38 @@ async def handle_journey_message(
     prompt_template = _extract_template(ai_reply)
     done = prompt_template is not None
 
+    # If the LLM didn't produce a template but we've hit max turns, nudge it
+    # and call the LLM one more time to force template generation.
+    if not done:
+        turns = sum(1 for t in session.history if t["role"] == "user")
+        if turns >= _MAX_TURNS:
+            nudge_content = (
+                "[System: You have enough information. Please generate the final "
+                f"prompt_template now, wrapped in {_TEMPLATE_START} / {_TEMPLATE_END} markers.]"
+            )
+            session.history.append({"role": "user", "content": nudge_content})
+
+            nudge_reply = await _call_llm_with_tools(
+                system_prompt=session.system_prompt,
+                history=session.history,
+                tools=list(FILESYSTEM_TOOLS),
+            )
+            session.history.append({"role": "assistant", "content": nudge_reply})
+
+            prompt_template = _extract_template(nudge_reply)
+            if prompt_template is not None:
+                done = True
+                ai_reply = nudge_reply
+
     display_message = ai_reply
     if done:
         display_message = (
             ai_reply[: ai_reply.index(_TEMPLATE_START)].strip()
-            or "Here is your agent configuration. You can save it or continue refining."
+            if _TEMPLATE_START in ai_reply
+            else "Here is your agent configuration. You can save it or continue refining."
         )
         _sessions.pop(session_id, None)
         logger.info("agent_setup: journey session %s completed for user %s", session_id, user_id)
-    else:
-        # Nudge the LLM to wrap up after max turns.
-        turns = sum(1 for t in session.history if t["role"] == "user")
-        if turns >= _MAX_TURNS:
-            session.history.append({
-                "role": "user",
-                "content": (
-                    "[System: You have enough information. Please generate the final "
-                    f"prompt_template now, wrapped in {_TEMPLATE_START} / {_TEMPLATE_END} markers.]"
-                ),
-            })
 
     return {
         "type": "journey_reply",