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
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@@ -5,6 +5,7 @@ import re
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import pytest
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from deerflow.runtime import RunManager, RunStatus
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from deerflow.runtime.runs.store.memory import MemoryRunStore
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ISO_RE = re.compile(r"^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}")
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@@ -141,3 +142,53 @@ async def test_create_defaults(manager: RunManager):
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assert record.kwargs == {}
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assert record.multitask_strategy == "reject"
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assert record.assistant_id is None
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@pytest.mark.anyio
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async def test_model_name_create_or_reject():
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"""create_or_reject should accept and persist model_name."""
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from deerflow.runtime.runs.schemas import DisconnectMode
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store = MemoryRunStore()
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mgr = RunManager(store=store)
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record = await mgr.create_or_reject(
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"thread-1",
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assistant_id="lead_agent",
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on_disconnect=DisconnectMode.cancel,
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metadata={"key": "val"},
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kwargs={"input": {}},
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multitask_strategy="reject",
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model_name="anthropic.claude-sonnet-4-20250514-v1:0",
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)
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assert record.model_name == "anthropic.claude-sonnet-4-20250514-v1:0"
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assert record.status == RunStatus.pending
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# Verify model_name was persisted to store
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stored = await store.get(record.run_id)
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assert stored is not None
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assert stored["model_name"] == "anthropic.claude-sonnet-4-20250514-v1:0"
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# Verify retrieval returns the model_name via in-memory record
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fetched = mgr.get(record.run_id)
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assert fetched is not None
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assert fetched.model_name == "anthropic.claude-sonnet-4-20250514-v1:0"
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@pytest.mark.anyio
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async def test_model_name_default_is_none():
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"""create_or_reject without model_name should default to None."""
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from deerflow.runtime.runs.schemas import DisconnectMode
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store = MemoryRunStore()
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mgr = RunManager(store=store)
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record = await mgr.create_or_reject(
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"thread-1",
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on_disconnect=DisconnectMode.cancel,
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model_name=None,
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)
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assert record.model_name is None
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stored = await store.get(record.run_id)
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assert stored["model_name"] is None
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