feat(run): Propagates model_name from the gateway request through the runtime and persistence stack to the SQLite database. (#2775)

* feat(run): propagate model_name from gateway request context to persistence layer

Pass model_name through the full run creation pipeline — from
RunCreateRequest.context in the gateway, through RunManager, to the
RunStore interface and SQL persistence. This enables client-specified
model selection to be recorded per-run in the database.

* feat(run): add model allowlist validation and effective model name capture

- Validate model_name against allowlist in gateway services.py using
  get_app_config().get_model_config()
- Truncate model_name to 128 chars to match DB column constraint
- In worker.py, capture effective model name from agent.metadata after
  agent creation and persist if resolved differently than requested

* feat(run): add defense-in-depth model_name normalization and round-trip persistence tests

- Add _normalize_model_name() to RunRepository for whitespace stripping
  and 128-char truncation before DB writes.
- Add round-trip unit tests for model_name creation and default None
  in test_run_manager.py.

* fix(run): coerce non-string model_name values before strip/truncate in _normalize_model_name

* fix(gateway): add runtime type guard for model_name coercion in gateway services

Add isinstance check and str() coercion before calling .strip() to prevent
AttributeError when non-string types (int, None, etc.) flow through the
gateway. Paired with SQL integration test for end-to-end model_name
persistence across gateway → langgraph → persistence layer.

* fix(run): drop Alembic migration for model_name (no-op) and expose public update method on RunManager

- Drop a1b2c3d4e5f6 migration: model_name already exists in RunRow schema
  and is auto-created via Base.metadata.create_all() at startup
- Add update_model_name() public method to RunManager to replace the private
  _persist_to_store call in worker.py, preserving internal locking/persistence

backend/packages/harness/deerflow/runtime/runs/worker.py

@@ -230,6 +230,17 @@ async def run_agent(
         else:
             agent = agent_factory(config=runnable_config)
 
+        # Capture the effective (resolved) model name from the agent's metadata.
+        # _resolve_model_name in agent.py may return the default model if the
+        # requested name is not in the allowlist — this update ensures the
+        # persisted model_name reflects the actual model used.
+        if record.model_name is not None:
+            resolved = getattr(agent, "metadata", {}) or {}
+            if isinstance(resolved, dict):
+                effective = resolved.get("model_name")
+                if effective and effective != record.model_name:
+                    await run_manager.update_model_name(record.run_id, effective)
+
         # 4. Attach checkpointer and store
         if checkpointer is not None:
             agent.checkpointer = checkpointer