f5c4ab50f4
* auto fixes * auto fix pt2 and transitive deps and undefined var checking locals() * manual fixes (ignored or letta-code fixed) * fix circular import * remove all ignores, add FastAPI rules and Ruff rules * add ty and precommit * ruff stuff * ty check fixes * ty check fixes pt 2 * error on invalid
1754 lines
66 KiB
Python
1754 lines
66 KiB
Python
"""
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Tests for agent cancellation at different points in the execution loop.
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These tests use mocking and deterministic control to test cancellation at specific
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points in the agent execution flow, covering all the issues documented in CANCELLATION_ISSUES.md.
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"""
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import asyncio
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import pytest
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from letta.agents.agent_loop import AgentLoop
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from letta.constants import TOOL_CALL_DENIAL_ON_CANCEL
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from letta.schemas.agent import CreateAgent
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from letta.schemas.embedding_config import EmbeddingConfig
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from letta.schemas.enums import MessageRole, RunStatus
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from letta.schemas.letta_request import LettaStreamingRequest
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from letta.schemas.llm_config import LLMConfig
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from letta.schemas.message import MessageCreate
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from letta.schemas.run import Run as PydanticRun
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from letta.server.server import SyncServer
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from letta.services.streaming_service import StreamingService
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|
|
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@pytest.fixture
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async def test_agent_with_tool(server: SyncServer, default_user, print_tool):
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"""Create a test agent with letta_v1_agent type (uses LettaAgentV3)."""
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agent_state = await server.agent_manager.create_agent_async(
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agent_create=CreateAgent(
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name="test_cancellation_agent",
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agent_type="letta_v1_agent",
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memory_blocks=[],
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llm_config=LLMConfig.default_config("gpt-4o-mini"),
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embedding_config=EmbeddingConfig.default_config(provider="openai"),
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tool_ids=[print_tool.id],
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include_base_tools=False,
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),
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actor=default_user,
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)
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yield agent_state
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@pytest.fixture
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async def test_run(server: SyncServer, default_user, test_agent_with_tool):
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"""Create a test run for cancellation testing."""
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run = await server.run_manager.create_run(
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pydantic_run=PydanticRun(
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agent_id=test_agent_with_tool.id,
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status=RunStatus.created,
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),
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actor=default_user,
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)
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yield run
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class TestMessageStateDesyncIssues:
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"""
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Test Issue #2: Message State Desync Issues
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Tests that message state stays consistent between client and server during cancellation.
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"""
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@pytest.mark.asyncio
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async def test_message_state_consistency_after_cancellation(
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self,
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server: SyncServer,
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default_user,
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test_agent_with_tool,
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test_run,
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):
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"""
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Test that message state is consistent after cancellation.
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Verifies:
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- response_messages list matches persisted messages
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- response_messages_for_metadata list matches persisted messages
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- agent.message_ids includes all persisted messages
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"""
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# Load agent loop
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agent_loop = AgentLoop.load(agent_state=test_agent_with_tool, actor=default_user)
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input_messages = [MessageCreate(role=MessageRole.user, content="Call print_tool with 'test'")]
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# Cancel after first step
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call_count = [0]
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async def mock_check_cancellation(run_id):
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call_count[0] += 1
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if call_count[0] > 1:
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await server.run_manager.cancel_run(
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actor=default_user,
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run_id=run_id,
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)
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return True
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return False
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agent_loop._check_run_cancellation = mock_check_cancellation
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# Execute step
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result = await agent_loop.step(
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input_messages=input_messages,
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max_steps=5,
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run_id=test_run.id,
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)
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# Get messages from database
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db_messages = await server.message_manager.list_messages(
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actor=default_user,
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agent_id=test_agent_with_tool.id,
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run_id=test_run.id,
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limit=1000,
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)
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# Verify response_messages count matches result messages
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assert len(agent_loop.response_messages) == len(result.messages), (
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f"response_messages ({len(agent_loop.response_messages)}) should match result.messages ({len(result.messages)})"
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)
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# Verify persisted message count is reasonable
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assert len(db_messages) > 0, "Should have persisted messages from completed step"
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# CRITICAL CHECK: Verify agent state after cancellation
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agent_after_cancel = await server.agent_manager.get_agent_by_id_async(
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agent_id=test_agent_with_tool.id,
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actor=default_user,
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)
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# Verify last_stop_reason is set to cancelled
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assert agent_after_cancel.last_stop_reason == "cancelled", (
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f"Agent's last_stop_reason should be 'cancelled', got '{agent_after_cancel.last_stop_reason}'"
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)
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agent_message_ids = set(agent_after_cancel.message_ids or [])
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db_message_ids = {m.id for m in db_messages}
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# Check for desync: every message in DB must be in agent.message_ids
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messages_in_db_not_in_agent = db_message_ids - agent_message_ids
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assert len(messages_in_db_not_in_agent) == 0, (
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f"MESSAGE DESYNC: {len(messages_in_db_not_in_agent)} messages in DB but not in agent.message_ids\n"
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f"Missing message IDs: {messages_in_db_not_in_agent}\n"
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f"This indicates message_ids was not updated after cancellation."
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)
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@pytest.mark.asyncio
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async def test_agent_can_continue_after_cancellation(
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self,
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server: SyncServer,
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default_user,
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test_agent_with_tool,
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test_run,
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):
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"""
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Test that agent can continue execution after a cancelled run.
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Verifies:
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- Agent state is not corrupted after cancellation
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- Subsequent runs complete successfully
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- Message IDs are properly updated
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"""
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# Load agent loop
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agent_loop = AgentLoop.load(agent_state=test_agent_with_tool, actor=default_user)
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# First run: cancel it
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input_messages_1 = [MessageCreate(role=MessageRole.user, content="First message")]
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# Cancel immediately
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await server.run_manager.cancel_run(
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actor=default_user,
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run_id=test_run.id,
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)
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result_1 = await agent_loop.step(
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input_messages=input_messages_1,
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max_steps=5,
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run_id=test_run.id,
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)
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assert result_1.stop_reason.stop_reason == "cancelled"
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# Get agent state after cancellation
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agent_after_cancel = await server.agent_manager.get_agent_by_id_async(
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agent_id=test_agent_with_tool.id,
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actor=default_user,
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)
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# Verify last_stop_reason is set to cancelled
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assert agent_after_cancel.last_stop_reason == "cancelled", (
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f"Agent's last_stop_reason should be 'cancelled', got '{agent_after_cancel.last_stop_reason}'"
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)
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message_ids_after_cancel = len(agent_after_cancel.message_ids or [])
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# Second run: complete it successfully
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test_run_2 = await server.run_manager.create_run(
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pydantic_run=PydanticRun(
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agent_id=test_agent_with_tool.id,
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status=RunStatus.created,
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),
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actor=default_user,
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)
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# Reload agent loop with fresh state
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agent_loop_2 = AgentLoop.load(
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agent_state=await server.agent_manager.get_agent_by_id_async(
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agent_id=test_agent_with_tool.id,
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actor=default_user,
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include_relationships=["memory", "tools", "sources"],
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),
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actor=default_user,
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)
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input_messages_2 = [MessageCreate(role=MessageRole.user, content="Second message")]
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result_2 = await agent_loop_2.step(
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input_messages=input_messages_2,
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max_steps=5,
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run_id=test_run_2.id,
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)
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# Verify second run completed successfully
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assert result_2.stop_reason.stop_reason != "cancelled", f"Second run should complete, got {result_2.stop_reason.stop_reason}"
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# Get agent state after completion
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agent_after_complete = await server.agent_manager.get_agent_by_id_async(
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agent_id=test_agent_with_tool.id,
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actor=default_user,
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)
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message_ids_after_complete = len(agent_after_complete.message_ids or [])
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# Verify message count increased
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assert message_ids_after_complete >= message_ids_after_cancel, (
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f"Message IDs should increase or stay same: "
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f"after_cancel={message_ids_after_cancel}, after_complete={message_ids_after_complete}"
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)
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# CRITICAL CHECK: Verify agent.message_ids consistency with DB for BOTH runs
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# Check first run (cancelled)
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db_messages_run1 = await server.message_manager.list_messages(
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actor=default_user,
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agent_id=test_agent_with_tool.id,
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run_id=test_run.id,
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limit=1000,
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)
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# Check second run (completed)
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db_messages_run2 = await server.message_manager.list_messages(
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actor=default_user,
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agent_id=test_agent_with_tool.id,
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run_id=test_run_2.id,
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limit=1000,
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)
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agent_message_ids = set(agent_after_complete.message_ids or [])
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all_db_message_ids = {m.id for m in db_messages_run1} | {m.id for m in db_messages_run2}
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# Check for desync: every message in DB must be in agent.message_ids
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messages_in_db_not_in_agent = all_db_message_ids - agent_message_ids
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assert len(messages_in_db_not_in_agent) == 0, (
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f"MESSAGE DESYNC: {len(messages_in_db_not_in_agent)} messages in DB but not in agent.message_ids\n"
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f"Missing message IDs: {messages_in_db_not_in_agent}\n"
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f"Run 1 (cancelled) had {len(db_messages_run1)} messages\n"
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f"Run 2 (completed) had {len(db_messages_run2)} messages\n"
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f"Agent has {len(agent_message_ids)} message_ids total\n"
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f"This indicates message_ids was not updated properly after cancellation or continuation."
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)
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@pytest.mark.asyncio
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async def test_approval_request_message_ids_desync_with_background_token_streaming(
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self,
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server: SyncServer,
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default_user,
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test_agent_with_tool,
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bash_tool,
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):
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"""
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Test for the specific desync bug with BACKGROUND + TOKEN STREAMING.
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This is the EXACT scenario where the bug occurs in production:
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- background=True (background streaming)
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- stream_tokens=True (token streaming)
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- Agent calls HITL tool requiring approval
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- Run is cancelled during approval
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Bug Scenario:
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1. Agent calls HITL tool requiring approval
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2. Approval request message is persisted to DB
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3. Run is cancelled while processing in background with token streaming
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4. Approval request message ID is NOT in agent.message_ids
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5. Result: "Desync detected - cursor last: X, in-context last: Y"
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"""
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# Add bash_tool to agent (requires approval)
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await server.agent_manager.attach_tool_async(
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agent_id=test_agent_with_tool.id,
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tool_id=bash_tool.id,
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actor=default_user,
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)
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# Get initial message count
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agent_before = await server.agent_manager.get_agent_by_id_async(
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agent_id=test_agent_with_tool.id,
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actor=default_user,
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)
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initial_message_ids = set(agent_before.message_ids or [])
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print(f"\nInitial message_ids count: {len(initial_message_ids)}")
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# Create streaming service
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streaming_service = StreamingService(server)
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# Create request with BACKGROUND + TOKEN STREAMING (the key conditions!)
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request = LettaStreamingRequest(
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messages=[MessageCreate(role=MessageRole.user, content="Please run the bash_tool with operation 'test'")],
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max_steps=5,
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stream_tokens=True, # TOKEN STREAMING - KEY CONDITION
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background=True, # BACKGROUND STREAMING - KEY CONDITION
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)
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print("\n🔥 Starting agent with BACKGROUND + TOKEN STREAMING...")
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print(f" stream_tokens={request.stream_tokens}")
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print(f" background={request.background}")
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# Start the background streaming agent
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run, _stream_response = await streaming_service.create_agent_stream(
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agent_id=test_agent_with_tool.id,
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actor=default_user,
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request=request,
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run_type="test_desync",
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)
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assert run is not None, "Run should be created for background streaming"
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print(f"\n✅ Run created: {run.id}")
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print(f" Status: {run.status}")
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|
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# Cancel almost immediately - we want to interrupt DURING processing, not after
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# The bug happens when cancellation interrupts the approval flow mid-execution
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print("\n⏳ Starting background task, will cancel quickly to catch mid-execution...")
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await asyncio.sleep(0.3) # Just enough time for LLM to start, but not complete
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|
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|
# NOW CANCEL THE RUN WHILE IT'S STILL PROCESSING - This is where the bug happens!
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print("\n❌ CANCELLING RUN while in background + token streaming mode (MID-EXECUTION)...")
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await server.run_manager.cancel_run(
|
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actor=default_user,
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|
run_id=run.id,
|
|
)
|
|
|
|
# Give cancellation time to propagate and background task to react
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print("⏳ Waiting for cancellation to propagate through background task...")
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await asyncio.sleep(2) # Let the background task detect cancellation and clean up
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|
|
|
# Check run status after cancellation
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|
run_status = await server.run_manager.get_run_by_id(run.id, actor=default_user)
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print(f"\n📊 Run status after cancel: {run_status.status}")
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print(f" Stop reason: {run_status.stop_reason}")
|
|
|
|
# Get messages from DB AFTER cancellation
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|
db_messages_after_cancel = await server.message_manager.list_messages(
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actor=default_user,
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agent_id=test_agent_with_tool.id,
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run_id=run.id,
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limit=1000,
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|
)
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print(f"\n📨 Messages in DB after cancel: {len(db_messages_after_cancel)}")
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for msg in db_messages_after_cancel:
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|
print(f" - {msg.id}: role={msg.role}")
|
|
|
|
# Get agent state AFTER cancellation
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agent_after_cancel = await server.agent_manager.get_agent_by_id_async(
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agent_id=test_agent_with_tool.id,
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|
actor=default_user,
|
|
)
|
|
|
|
# Verify last_stop_reason is set to cancelled
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|
print(f"\n🔍 Agent last_stop_reason: {agent_after_cancel.last_stop_reason}")
|
|
assert agent_after_cancel.last_stop_reason == "cancelled", (
|
|
f"Agent's last_stop_reason should be 'cancelled', got '{agent_after_cancel.last_stop_reason}'"
|
|
)
|
|
|
|
agent_message_ids = set(agent_after_cancel.message_ids or [])
|
|
new_message_ids = agent_message_ids - initial_message_ids
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|
print(f"\n📝 Agent message_ids after cancel: {len(agent_message_ids)}")
|
|
print(f" New message_ids in this run: {len(new_message_ids)}")
|
|
|
|
db_message_ids = {m.id for m in db_messages_after_cancel}
|
|
|
|
# CRITICAL CHECK: Every message in DB must be in agent.message_ids
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|
messages_in_db_not_in_agent = db_message_ids - agent_message_ids
|
|
|
|
if messages_in_db_not_in_agent:
|
|
# THIS IS THE DESYNC BUG!
|
|
print("\n❌ DESYNC BUG DETECTED!")
|
|
print(f"🐛 Found {len(messages_in_db_not_in_agent)} messages in DB but NOT in agent.message_ids")
|
|
print(" This bug occurs specifically with: background=True + stream_tokens=True")
|
|
|
|
missing_messages = [m for m in db_messages_after_cancel if m.id in messages_in_db_not_in_agent]
|
|
|
|
print("\n🔍 Missing messages details:")
|
|
for m in missing_messages:
|
|
print(f" - ID: {m.id}")
|
|
print(f" Role: {m.role}")
|
|
print(f" Created: {m.created_at}")
|
|
if hasattr(m, "content"):
|
|
content_preview = str(m.content)[:100] if m.content else "None"
|
|
print(f" Content: {content_preview}...")
|
|
|
|
# Get the last message IDs for the exact error message format
|
|
cursor_last = list(db_message_ids)[-1] if db_message_ids else None
|
|
in_context_last = list(agent_message_ids)[-1] if agent_message_ids else None
|
|
|
|
print("\n💥 This causes the EXACT error reported:")
|
|
print(f" 'Desync detected - cursor last: {cursor_last},")
|
|
print(f" in-context last: {in_context_last}'")
|
|
|
|
assert False, (
|
|
f"🐛 DESYNC DETECTED IN BACKGROUND + TOKEN STREAMING MODE\n\n"
|
|
f"Found {len(messages_in_db_not_in_agent)} messages in DB but not in agent.message_ids\n\n"
|
|
f"This reproduces the reported bug:\n"
|
|
f" 'Desync detected - cursor last: {cursor_last},\n"
|
|
f" in-context last: {in_context_last}'\n\n"
|
|
f"Missing message IDs: {messages_in_db_not_in_agent}\n\n"
|
|
f"Root cause: With background=True + stream_tokens=True, approval request messages\n"
|
|
f"are persisted to DB but NOT added to agent.message_ids when cancellation occurs\n"
|
|
f"during HITL approval flow.\n\n"
|
|
f"Fix location: Check approval flow in letta_agent_v3.py:442-486 and background\n"
|
|
f"streaming wrapper in streaming_service.py:138-146"
|
|
)
|
|
|
|
# Also check reverse: agent.message_ids shouldn't have messages not in DB
|
|
messages_in_agent_not_in_db = agent_message_ids - db_message_ids
|
|
messages_in_agent_not_in_db = messages_in_agent_not_in_db - initial_message_ids
|
|
|
|
if messages_in_agent_not_in_db:
|
|
print("\n❌ REVERSE DESYNC DETECTED!")
|
|
print(f"Found {len(messages_in_agent_not_in_db)} message IDs in agent.message_ids but NOT in DB")
|
|
|
|
assert False, (
|
|
f"REVERSE DESYNC: {len(messages_in_agent_not_in_db)} messages in agent.message_ids but not in DB\n"
|
|
f"Message IDs: {messages_in_agent_not_in_db}"
|
|
)
|
|
|
|
# If we get here, message IDs are consistent!
|
|
print("\n✅ No desync detected - message IDs are consistent between DB and agent state")
|
|
print(f" DB message count: {len(db_message_ids)}")
|
|
print(f" Agent message_ids count: {len(agent_message_ids)}")
|
|
print("\n Either the bug is fixed, or we need to adjust test timing/conditions.")
|
|
|
|
|
|
class TestStreamingCancellation:
|
|
"""
|
|
Test cancellation during different streaming modes.
|
|
"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_token_streaming_cancellation(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
test_run,
|
|
):
|
|
"""
|
|
Test cancellation during token streaming mode.
|
|
|
|
This tests Issue #3: Cancellation During LLM Streaming (token mode).
|
|
|
|
Verifies:
|
|
- Cancellation can be detected during token streaming
|
|
- Partial messages are handled correctly
|
|
- Stop reason is set to 'cancelled'
|
|
"""
|
|
# Load agent loop
|
|
agent_loop = AgentLoop.load(agent_state=test_agent_with_tool, actor=default_user)
|
|
|
|
input_messages = [MessageCreate(role=MessageRole.user, content="Hello")]
|
|
|
|
# Cancel after first chunk
|
|
cancel_triggered = [False]
|
|
|
|
async def mock_check_cancellation(run_id):
|
|
if cancel_triggered[0]:
|
|
return True
|
|
return False
|
|
|
|
agent_loop._check_run_cancellation = mock_check_cancellation
|
|
|
|
# Mock streaming
|
|
async def cancel_during_stream():
|
|
"""Generator that simulates streaming and cancels mid-stream."""
|
|
chunks_yielded = 0
|
|
stream = agent_loop.stream(
|
|
input_messages=input_messages,
|
|
max_steps=5,
|
|
stream_tokens=True,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
async for chunk in stream:
|
|
chunks_yielded += 1
|
|
yield chunk
|
|
|
|
# Cancel after a few chunks
|
|
if chunks_yielded == 2 and not cancel_triggered[0]:
|
|
cancel_triggered[0] = True
|
|
await server.run_manager.cancel_run(
|
|
actor=default_user,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Consume the stream
|
|
chunks = []
|
|
try:
|
|
async for chunk in cancel_during_stream():
|
|
chunks.append(chunk)
|
|
except Exception:
|
|
# May raise exception on cancellation
|
|
pass
|
|
|
|
# Verify we got some chunks before cancellation
|
|
assert len(chunks) > 0, "Should receive at least some chunks before cancellation"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_step_streaming_cancellation(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
test_run,
|
|
):
|
|
"""
|
|
Test cancellation during step streaming mode (not token streaming).
|
|
|
|
Verifies:
|
|
- Cancellation detected between steps
|
|
- Completed steps are streamed fully
|
|
- Partial step is not streamed
|
|
"""
|
|
# Load agent loop
|
|
agent_loop = AgentLoop.load(agent_state=test_agent_with_tool, actor=default_user)
|
|
|
|
input_messages = [MessageCreate(role=MessageRole.user, content="Call print_tool with 'message'")]
|
|
|
|
# Cancel after first step
|
|
call_count = [0]
|
|
|
|
async def mock_check_cancellation(run_id):
|
|
call_count[0] += 1
|
|
if call_count[0] > 1:
|
|
await server.run_manager.cancel_run(
|
|
actor=default_user,
|
|
run_id=run_id,
|
|
)
|
|
return True
|
|
return False
|
|
|
|
agent_loop._check_run_cancellation = mock_check_cancellation
|
|
|
|
# Stream with step streaming (not token streaming)
|
|
chunks = []
|
|
stream = agent_loop.stream(
|
|
input_messages=input_messages,
|
|
max_steps=5,
|
|
stream_tokens=False, # Step streaming
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
async for chunk in stream:
|
|
chunks.append(chunk)
|
|
|
|
# Verify we got chunks from the first step
|
|
assert len(chunks) > 0, "Should receive chunks from first step before cancellation"
|
|
|
|
# Verify cancellation was detected
|
|
assert agent_loop.stop_reason.stop_reason == "cancelled"
|
|
|
|
|
|
class TestToolExecutionCancellation:
|
|
"""
|
|
Test cancellation during tool execution.
|
|
This tests Issue #2C: Token streaming tool return desync.
|
|
"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_cancellation_during_tool_execution(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
test_run,
|
|
print_tool,
|
|
):
|
|
"""
|
|
Test cancellation while tool is executing.
|
|
|
|
Verifies:
|
|
- Tool execution completes or is interrupted cleanly
|
|
- Tool return messages are consistent
|
|
- Database state matches client state
|
|
"""
|
|
# Load agent loop
|
|
agent_loop = AgentLoop.load(agent_state=test_agent_with_tool, actor=default_user)
|
|
|
|
input_messages = [MessageCreate(role=MessageRole.user, content="Call print_tool with 'test message'")]
|
|
|
|
# Mock the tool execution to detect cancellation
|
|
tool_execution_started = [False]
|
|
tool_execution_completed = [False]
|
|
|
|
original_execute = agent_loop._execute_tool
|
|
|
|
async def mock_execute_tool(target_tool, tool_args, agent_state, agent_step_span, step_id):
|
|
tool_execution_started[0] = True
|
|
|
|
# Cancel during tool execution
|
|
await server.run_manager.cancel_run(
|
|
actor=default_user,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Call original (tool execution should complete)
|
|
result = await original_execute(target_tool, tool_args, agent_state, agent_step_span, step_id)
|
|
|
|
tool_execution_completed[0] = True
|
|
return result
|
|
|
|
agent_loop._execute_tool = mock_execute_tool
|
|
|
|
# Execute step
|
|
result = await agent_loop.step(
|
|
input_messages=input_messages,
|
|
max_steps=5,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Verify tool execution started
|
|
assert tool_execution_started[0], "Tool execution should have started"
|
|
|
|
# Verify cancellation was eventually detected
|
|
# (may be after tool completes, at next step boundary)
|
|
assert result.stop_reason.stop_reason == "cancelled"
|
|
|
|
# If tool completed, verify its messages are persisted
|
|
if tool_execution_completed[0]:
|
|
db_messages = await server.message_manager.list_messages(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
)
|
|
run_messages = [m for m in db_messages if m.run_id == test_run.id]
|
|
tool_returns = [m for m in run_messages if m.role == "tool"]
|
|
|
|
# If tool executed, should have a tool return message
|
|
assert len(tool_returns) > 0, "Should have persisted tool return message"
|
|
|
|
|
|
class TestResourceCleanupAfterCancellation:
|
|
"""
|
|
Test Issue #6: Resource Cleanup Issues
|
|
Tests that resources are properly cleaned up after cancellation.
|
|
"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_stop_reason_set_correctly_on_cancellation(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
test_run,
|
|
):
|
|
"""
|
|
Test that stop_reason is set to 'cancelled' not 'end_turn' or other.
|
|
|
|
This tests Issue #6: Resource Cleanup Issues.
|
|
The finally block should set stop_reason to 'cancelled' when appropriate.
|
|
|
|
Verifies:
|
|
- stop_reason is 'cancelled' when run is cancelled
|
|
- stop_reason is not 'end_turn' or 'completed' for cancelled runs
|
|
"""
|
|
# Load agent loop
|
|
agent_loop = AgentLoop.load(agent_state=test_agent_with_tool, actor=default_user)
|
|
|
|
# Cancel before execution
|
|
await server.run_manager.cancel_run(
|
|
actor=default_user,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
input_messages = [MessageCreate(role=MessageRole.user, content="Hello")]
|
|
|
|
result = await agent_loop.step(
|
|
input_messages=input_messages,
|
|
max_steps=5,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Verify stop reason is cancelled, not end_turn
|
|
assert result.stop_reason.stop_reason == "cancelled", f"Stop reason should be 'cancelled', got '{result.stop_reason.stop_reason}'"
|
|
|
|
# Verify run status in database
|
|
run = await server.run_manager.get_run_by_id(run_id=test_run.id, actor=default_user)
|
|
assert run.status == RunStatus.cancelled, f"Run status should be cancelled, got {run.status}"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_response_messages_cleared_after_cancellation(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
test_run,
|
|
):
|
|
"""
|
|
Test that internal message buffers are properly managed after cancellation.
|
|
|
|
Verifies:
|
|
- response_messages list is in expected state after cancellation
|
|
- No memory leaks from accumulated messages
|
|
"""
|
|
# Load agent loop
|
|
agent_loop = AgentLoop.load(agent_state=test_agent_with_tool, actor=default_user)
|
|
|
|
# Execute and cancel
|
|
call_count = [0]
|
|
|
|
async def mock_check_cancellation(run_id):
|
|
call_count[0] += 1
|
|
if call_count[0] > 1:
|
|
await server.run_manager.cancel_run(
|
|
actor=default_user,
|
|
run_id=run_id,
|
|
)
|
|
return True
|
|
return False
|
|
|
|
agent_loop._check_run_cancellation = mock_check_cancellation
|
|
|
|
input_messages = [MessageCreate(role=MessageRole.user, content="Call print_tool with 'test'")]
|
|
|
|
await agent_loop.step(
|
|
input_messages=input_messages,
|
|
max_steps=5,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Verify response_messages is not empty (contains messages from completed step)
|
|
# or is properly cleared depending on implementation
|
|
response_msg_count = len(agent_loop.response_messages)
|
|
|
|
# The exact behavior may vary, but we're checking that the state is reasonable
|
|
assert response_msg_count >= 0, "response_messages should be in valid state"
|
|
|
|
# Verify no excessive accumulation
|
|
assert response_msg_count < 100, "response_messages should not have excessive accumulation"
|
|
|
|
|
|
class TestApprovalFlowCancellation:
|
|
"""
|
|
Test Issue #5: Approval Flow + Cancellation
|
|
Tests edge cases with HITL tool approvals and cancellation.
|
|
"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_cancellation_while_waiting_for_approval(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
test_run,
|
|
bash_tool,
|
|
):
|
|
"""
|
|
Test cancellation while agent is waiting for tool approval.
|
|
|
|
This tests the scenario where:
|
|
1. Agent calls a tool requiring approval
|
|
2. Run is cancelled while waiting for approval
|
|
3. Agent should detect cancellation and not process approval
|
|
|
|
Verifies:
|
|
- Run status is cancelled
|
|
- Agent does not process approval after cancellation
|
|
- No tool execution happens
|
|
"""
|
|
# Add bash_tool which requires approval to agent
|
|
await server.agent_manager.attach_tool_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
tool_id=bash_tool.id,
|
|
actor=default_user,
|
|
)
|
|
|
|
# Reload agent with new tool
|
|
test_agent_with_tool = await server.agent_manager.get_agent_by_id_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
include_relationships=["memory", "tools", "sources"],
|
|
)
|
|
|
|
# Load agent loop
|
|
agent_loop = AgentLoop.load(agent_state=test_agent_with_tool, actor=default_user)
|
|
|
|
input_messages = [MessageCreate(role=MessageRole.user, content="Call bash_tool with operation 'test'")]
|
|
|
|
# Execute step - should stop at approval request
|
|
result = await agent_loop.step(
|
|
input_messages=input_messages,
|
|
max_steps=5,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Verify we got approval request
|
|
assert result.stop_reason.stop_reason == "requires_approval", f"Should stop for approval, got {result.stop_reason.stop_reason}"
|
|
|
|
# Now cancel the run while "waiting for approval"
|
|
await server.run_manager.cancel_run(
|
|
actor=default_user,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Reload agent loop with fresh state
|
|
agent_loop_2 = AgentLoop.load(
|
|
agent_state=await server.agent_manager.get_agent_by_id_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
include_relationships=["memory", "tools", "sources"],
|
|
),
|
|
actor=default_user,
|
|
)
|
|
|
|
# Try to continue - should detect cancellation
|
|
result_2 = await agent_loop_2.step(
|
|
input_messages=[MessageCreate(role=MessageRole.user, content="Hello")], # No new input, just continuing
|
|
max_steps=5,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Should detect cancellation
|
|
assert result_2.stop_reason.stop_reason == "cancelled", f"Should detect cancellation, got {result_2.stop_reason.stop_reason}"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_agent_state_after_cancelled_approval(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
test_run,
|
|
bash_tool,
|
|
):
|
|
"""
|
|
Test that agent state is consistent after approval request is cancelled.
|
|
|
|
This addresses the issue where agents say they are "awaiting approval"
|
|
even though the run is cancelled.
|
|
|
|
Verifies:
|
|
- Agent can continue after cancelled approval
|
|
- No phantom "awaiting approval" state
|
|
- Messages reflect actual state
|
|
"""
|
|
# Add bash_tool which requires approval
|
|
await server.agent_manager.attach_tool_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
tool_id=bash_tool.id,
|
|
actor=default_user,
|
|
)
|
|
|
|
# Reload agent with new tool
|
|
test_agent_with_tool = await server.agent_manager.get_agent_by_id_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
include_relationships=["memory", "tools", "sources"],
|
|
)
|
|
|
|
agent_loop = AgentLoop.load(agent_state=test_agent_with_tool, actor=default_user)
|
|
|
|
# First run: trigger approval request then cancel
|
|
input_messages_1 = [MessageCreate(role=MessageRole.user, content="Call bash_tool with operation 'test'")]
|
|
|
|
result_1 = await agent_loop.step(
|
|
input_messages=input_messages_1,
|
|
max_steps=5,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
assert result_1.stop_reason.stop_reason == "requires_approval"
|
|
|
|
# Cancel the run
|
|
await server.run_manager.cancel_run(
|
|
actor=default_user,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Get messages to check for "awaiting approval" state
|
|
messages_after_cancel = await server.message_manager.list_messages(
|
|
actor=default_user,
|
|
agent_id=test_agent_with_tool.id,
|
|
run_id=test_run.id,
|
|
limit=1000,
|
|
)
|
|
|
|
# Check for approval request messages
|
|
[m for m in messages_after_cancel if m.role == "approval_request"]
|
|
|
|
# Second run: try to execute normally (should work, not stuck in approval)
|
|
test_run_2 = await server.run_manager.create_run(
|
|
pydantic_run=PydanticRun(
|
|
agent_id=test_agent_with_tool.id,
|
|
status=RunStatus.created,
|
|
),
|
|
actor=default_user,
|
|
)
|
|
|
|
agent_loop_2 = AgentLoop.load(
|
|
agent_state=await server.agent_manager.get_agent_by_id_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
include_relationships=["memory", "tools", "sources"],
|
|
),
|
|
actor=default_user,
|
|
)
|
|
|
|
# Call a different tool that doesn't require approval
|
|
input_messages_2 = [MessageCreate(role=MessageRole.user, content="Call print_tool with message 'hello'")]
|
|
|
|
result_2 = await agent_loop_2.step(
|
|
input_messages=input_messages_2,
|
|
max_steps=5,
|
|
run_id=test_run_2.id,
|
|
)
|
|
|
|
# Should complete normally, not be stuck in approval state
|
|
assert result_2.stop_reason.stop_reason != "requires_approval", "Agent should not be stuck in approval state from cancelled run"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_approval_state_persisted_correctly_after_cancel(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
test_run,
|
|
bash_tool,
|
|
):
|
|
"""
|
|
Test that approval state is correctly persisted/cleaned after cancellation.
|
|
|
|
This addresses the specific issue mentioned:
|
|
"agents say they are awaiting approval despite the run not being shown as pending approval"
|
|
|
|
Verifies:
|
|
- Run status matches actual state
|
|
- No phantom "pending approval" status
|
|
- Messages accurately reflect cancellation
|
|
"""
|
|
# Add bash_tool
|
|
await server.agent_manager.attach_tool_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
tool_id=bash_tool.id,
|
|
actor=default_user,
|
|
)
|
|
|
|
test_agent_with_tool = await server.agent_manager.get_agent_by_id_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
include_relationships=["memory", "tools", "sources"],
|
|
)
|
|
|
|
agent_loop = AgentLoop.load(agent_state=test_agent_with_tool, actor=default_user)
|
|
|
|
# Trigger approval
|
|
result = await agent_loop.step(
|
|
input_messages=[MessageCreate(role=MessageRole.user, content="Call bash_tool with 'test'")],
|
|
max_steps=5,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
assert result.stop_reason.stop_reason == "requires_approval"
|
|
|
|
# Cancel the run
|
|
await server.run_manager.cancel_run(
|
|
actor=default_user,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Verify run status is cancelled, NOT pending_approval
|
|
run_after_cancel = await server.run_manager.get_run_by_id(run_id=test_run.id, actor=default_user)
|
|
assert run_after_cancel.status == RunStatus.cancelled, f"Run status should be cancelled, got {run_after_cancel.status}"
|
|
|
|
# Agent should be able to start fresh run
|
|
test_run_3 = await server.run_manager.create_run(
|
|
pydantic_run=PydanticRun(
|
|
agent_id=test_agent_with_tool.id,
|
|
status=RunStatus.created,
|
|
),
|
|
actor=default_user,
|
|
)
|
|
|
|
agent_loop_3 = AgentLoop.load(
|
|
agent_state=await server.agent_manager.get_agent_by_id_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
include_relationships=["memory", "tools", "sources"],
|
|
),
|
|
actor=default_user,
|
|
)
|
|
|
|
# Should be able to make normal call
|
|
result_3 = await agent_loop_3.step(
|
|
input_messages=[MessageCreate(role=MessageRole.user, content="Call print_tool with 'test'")],
|
|
max_steps=5,
|
|
run_id=test_run_3.id,
|
|
)
|
|
|
|
# Should complete normally
|
|
assert result_3.stop_reason.stop_reason != "requires_approval", "New run should not be stuck in approval state"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_approval_request_message_ids_desync(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
test_run,
|
|
bash_tool,
|
|
):
|
|
"""
|
|
Test for the specific desync bug reported:
|
|
"Desync detected - cursor last: message-X, in-context last: message-Y"
|
|
|
|
Bug Scenario:
|
|
1. Agent calls HITL tool requiring approval
|
|
2. Approval request message is persisted to DB
|
|
3. Run is cancelled
|
|
4. Approval request message ID is NOT in agent.message_ids
|
|
5. Result: cursor desync between DB and agent state
|
|
|
|
This is the root cause of the reported error:
|
|
"Desync detected - cursor last: message-c07fa1ec..., in-context last: message-a2615dc3..."
|
|
|
|
The bug happens because:
|
|
- Database contains the approval_request message
|
|
- Agent's message_ids list does NOT contain the approval_request message ID
|
|
- Causes cursor/pagination to fail
|
|
|
|
Verifies:
|
|
- If approval request is in DB, it must be in agent.message_ids
|
|
- Cancellation doesn't cause partial message persistence
|
|
- Cursor consistency between DB and agent state
|
|
"""
|
|
# Add bash_tool which requires approval
|
|
await server.agent_manager.attach_tool_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
tool_id=bash_tool.id,
|
|
actor=default_user,
|
|
)
|
|
|
|
# Get initial message count
|
|
agent_before = await server.agent_manager.get_agent_by_id_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
)
|
|
initial_message_ids = set(agent_before.message_ids or [])
|
|
|
|
# Reload agent with new tool
|
|
test_agent_with_tool = await server.agent_manager.get_agent_by_id_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
include_relationships=["memory", "tools", "sources"],
|
|
)
|
|
|
|
agent_loop = AgentLoop.load(agent_state=test_agent_with_tool, actor=default_user)
|
|
|
|
# Trigger approval request
|
|
result = await agent_loop.step(
|
|
input_messages=[MessageCreate(role=MessageRole.user, content="Call bash_tool with 'test'")],
|
|
max_steps=5,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
assert result.stop_reason.stop_reason == "requires_approval", f"Expected requires_approval, got {result.stop_reason.stop_reason}"
|
|
|
|
# Get all messages from database for this run
|
|
await server.message_manager.list_messages(
|
|
actor=default_user,
|
|
agent_id=test_agent_with_tool.id,
|
|
run_id=test_run.id,
|
|
limit=1000,
|
|
)
|
|
|
|
# Cancel the run
|
|
await server.run_manager.cancel_run(
|
|
actor=default_user,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Get agent state after cancellation
|
|
agent_after_cancel = await server.agent_manager.get_agent_by_id_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
)
|
|
|
|
agent_message_ids = set(agent_after_cancel.message_ids or [])
|
|
|
|
# Get all messages from database again
|
|
db_messages_after = await server.message_manager.list_messages(
|
|
actor=default_user,
|
|
agent_id=test_agent_with_tool.id,
|
|
run_id=test_run.id,
|
|
limit=1000,
|
|
)
|
|
|
|
db_message_ids = {m.id for m in db_messages_after}
|
|
|
|
# CRITICAL CHECK: Every message in DB must be in agent.message_ids
|
|
messages_in_db_not_in_agent = db_message_ids - agent_message_ids
|
|
|
|
if messages_in_db_not_in_agent:
|
|
# THIS IS THE DESYNC BUG!
|
|
missing_messages = [m for m in db_messages_after if m.id in messages_in_db_not_in_agent]
|
|
missing_details = [f"ID: {m.id}, Role: {m.role}, Created: {m.created_at}" for m in missing_messages]
|
|
|
|
# Get the cursor values that would cause the error
|
|
cursor_last = list(db_message_ids)[-1] if db_message_ids else None
|
|
in_context_last = list(agent_message_ids)[-1] if agent_message_ids else None
|
|
|
|
assert False, (
|
|
f"DESYNC DETECTED: {len(messages_in_db_not_in_agent)} messages in DB but not in agent.message_ids\n\n"
|
|
f"This is the reported bug:\n"
|
|
f" 'Desync detected - cursor last: {cursor_last}, in-context last: {in_context_last}'\n\n"
|
|
f"Missing messages:\n" + "\n".join(missing_details) + "\n\n"
|
|
f"Agent message_ids count: {len(agent_message_ids)}\n"
|
|
f"DB messages count: {len(db_message_ids)}\n\n"
|
|
f"Root cause: Approval request message was persisted to DB but not added to agent.message_ids\n"
|
|
f"when cancellation occurred during HITL approval flow."
|
|
)
|
|
|
|
# Also check the inverse: agent.message_ids shouldn't have messages not in DB
|
|
messages_in_agent_not_in_db = agent_message_ids - db_message_ids
|
|
messages_in_agent_not_in_db = messages_in_agent_not_in_db - initial_message_ids
|
|
|
|
if messages_in_agent_not_in_db:
|
|
assert False, (
|
|
f"REVERSE DESYNC: {len(messages_in_agent_not_in_db)} messages in agent.message_ids but not in DB\n"
|
|
f"Message IDs: {messages_in_agent_not_in_db}"
|
|
)
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_parallel_tool_calling_cancellation_with_denials(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
bash_tool,
|
|
):
|
|
"""
|
|
Test that parallel tool calls receive proper denial messages on cancellation.
|
|
|
|
This tests the scenario where:
|
|
1. Agent has parallel tool calling enabled
|
|
2. Agent calls a tool 3 times in parallel (requiring approval)
|
|
3. Run is cancelled while waiting for approval
|
|
4. All 3 tool calls receive denial messages with TOOL_CALL_DENIAL_ON_CANCEL
|
|
5. Agent can still be messaged again (creating a new run)
|
|
|
|
Verifies:
|
|
- All parallel tool calls get proper denial messages
|
|
- Denial messages contain TOOL_CALL_DENIAL_ON_CANCEL reason
|
|
- Agent state is not corrupted
|
|
- New runs can be created after cancellation
|
|
"""
|
|
# Create agent with parallel tool calling enabled
|
|
config = LLMConfig.default_config("gpt-4o-mini")
|
|
config.parallel_tool_calls = True
|
|
agent_state = await server.agent_manager.create_agent_async(
|
|
agent_create=CreateAgent(
|
|
name="test_parallel_tool_calling_agent",
|
|
agent_type="letta_v1_agent",
|
|
memory_blocks=[],
|
|
llm_config=LLMConfig.default_config("gpt-4o-mini"),
|
|
embedding_config=EmbeddingConfig.default_config(provider="openai"),
|
|
tool_ids=[bash_tool.id],
|
|
include_base_tools=False,
|
|
),
|
|
actor=default_user,
|
|
)
|
|
|
|
# Create a run
|
|
test_run = await server.run_manager.create_run(
|
|
pydantic_run=PydanticRun(
|
|
agent_id=agent_state.id,
|
|
status=RunStatus.created,
|
|
),
|
|
actor=default_user,
|
|
)
|
|
|
|
# Load agent loop
|
|
agent_loop = AgentLoop.load(agent_state=agent_state, actor=default_user)
|
|
|
|
# Prompt the agent to call bash_tool 3 times
|
|
# The agent should make parallel tool calls since parallel_tool_calls is enabled
|
|
input_messages = [
|
|
MessageCreate(
|
|
role=MessageRole.user,
|
|
content="Please call bash_tool three times with operations: 'ls', 'pwd', and 'echo test'",
|
|
)
|
|
]
|
|
|
|
# Execute step - should stop at approval request with multiple tool calls
|
|
result = await agent_loop.step(
|
|
input_messages=input_messages,
|
|
max_steps=5,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Verify we got approval request
|
|
assert result.stop_reason.stop_reason == "requires_approval", f"Should stop for approval, got {result.stop_reason.stop_reason}"
|
|
|
|
# Get the approval request message to see how many tool calls were made
|
|
await server.message_manager.list_messages(
|
|
actor=default_user,
|
|
agent_id=agent_state.id,
|
|
run_id=test_run.id,
|
|
limit=1000,
|
|
)
|
|
|
|
# should not be possible to message the agent (Pending approval)
|
|
from letta.errors import PendingApprovalError
|
|
|
|
with pytest.raises(PendingApprovalError):
|
|
test_run2 = await server.run_manager.create_run(
|
|
pydantic_run=PydanticRun(
|
|
agent_id=agent_state.id,
|
|
status=RunStatus.created,
|
|
),
|
|
actor=default_user,
|
|
)
|
|
await agent_loop.step(
|
|
input_messages=[MessageCreate(role=MessageRole.user, content="Hello, how are you?")],
|
|
max_steps=5,
|
|
run_id=test_run2.id,
|
|
)
|
|
|
|
from letta.schemas.letta_message import ApprovalRequestMessage
|
|
|
|
approval_request_messages = [m for m in result.messages if isinstance(m, ApprovalRequestMessage)]
|
|
assert len(approval_request_messages) > 0, "Should have at least one approval request message"
|
|
|
|
# Get the last approval request message (should have the tool calls)
|
|
approval_request = approval_request_messages[-1]
|
|
tool_calls = approval_request.tool_calls if hasattr(approval_request, "tool_calls") else []
|
|
num_tool_calls = len(tool_calls)
|
|
|
|
print(f"\nFound {num_tool_calls} tool calls in approval request")
|
|
|
|
# The agent might not always make exactly 3 parallel calls depending on the LLM,
|
|
# but we should have at least 1 tool call. For the test to be meaningful,
|
|
# we want multiple tool calls, but we'll verify whatever the LLM decides
|
|
assert num_tool_calls >= 1, f"Should have at least 1 tool call, got {num_tool_calls}"
|
|
|
|
# Now cancel the run while "waiting for approval"
|
|
await server.run_manager.cancel_run(
|
|
actor=default_user,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Get messages after cancellation
|
|
db_messages_after_cancel = await server.message_manager.list_messages(
|
|
actor=default_user,
|
|
agent_id=agent_state.id,
|
|
run_id=test_run.id,
|
|
limit=1000,
|
|
)
|
|
|
|
# Find tool return messages (these should be the denial messages)
|
|
tool_return_messages = [m for m in db_messages_after_cancel if m.role == "tool"]
|
|
|
|
print(f"Found {len(tool_return_messages)} tool return messages after cancellation")
|
|
|
|
# Verify we got denial messages for all tool calls
|
|
assert len(tool_return_messages) == num_tool_calls, (
|
|
f"Should have {num_tool_calls} tool return messages (one per tool call), got {len(tool_return_messages)}"
|
|
)
|
|
|
|
# Verify each tool return message contains the denial reason
|
|
for tool_return_msg in tool_return_messages:
|
|
# Check if message has tool_returns (new format) or tool_return (old format)
|
|
print("TOOL RETURN MESSAGE:\n\n", tool_return_msg)
|
|
if hasattr(tool_return_msg, "tool_returns") and tool_return_msg.tool_returns:
|
|
# New format: list of tool returns
|
|
for tool_return in tool_return_msg.tool_returns:
|
|
assert TOOL_CALL_DENIAL_ON_CANCEL in tool_return.func_response, (
|
|
f"Tool return should contain denial message, got: {tool_return.tool_return}"
|
|
)
|
|
elif hasattr(tool_return_msg, "tool_return"):
|
|
# Old format: single tool_return field
|
|
assert TOOL_CALL_DENIAL_ON_CANCEL in tool_return_msg.content, (
|
|
f"Tool return should contain denial message, got: {tool_return_msg.tool_return}"
|
|
)
|
|
elif hasattr(tool_return_msg, "content"):
|
|
# Check content field
|
|
content_str = str(tool_return_msg.content)
|
|
assert TOOL_CALL_DENIAL_ON_CANCEL in content_str, f"Tool return content should contain denial message, got: {content_str}"
|
|
|
|
# Verify run status is cancelled
|
|
run_after_cancel = await server.run_manager.get_run_by_id(run_id=test_run.id, actor=default_user)
|
|
assert run_after_cancel.status == RunStatus.cancelled, f"Run status should be cancelled, got {run_after_cancel.status}"
|
|
|
|
# Verify agent can be messaged again (create a new run)
|
|
test_run_2 = await server.run_manager.create_run(
|
|
pydantic_run=PydanticRun(
|
|
agent_id=agent_state.id,
|
|
status=RunStatus.created,
|
|
),
|
|
actor=default_user,
|
|
)
|
|
|
|
# Reload agent loop with fresh state
|
|
agent_loop_2 = AgentLoop.load(
|
|
agent_state=await server.agent_manager.get_agent_by_id_async(
|
|
agent_id=agent_state.id,
|
|
actor=default_user,
|
|
include_relationships=["memory", "tools", "sources"],
|
|
),
|
|
actor=default_user,
|
|
)
|
|
|
|
# Send a simple message that doesn't require approval
|
|
input_messages_2 = [MessageCreate(role=MessageRole.user, content="Hello, how are you?")]
|
|
|
|
result_2 = await agent_loop_2.step(
|
|
input_messages=input_messages_2,
|
|
max_steps=5,
|
|
run_id=test_run_2.id,
|
|
)
|
|
|
|
# Verify second run completed successfully (not cancelled, not stuck in approval)
|
|
assert result_2.stop_reason.stop_reason != "cancelled", (
|
|
f"Second run should not be cancelled, got {result_2.stop_reason.stop_reason}"
|
|
)
|
|
assert result_2.stop_reason.stop_reason != "requires_approval", (
|
|
f"Second run should not require approval for simple message, got {result_2.stop_reason.stop_reason}"
|
|
)
|
|
|
|
# Verify the second run has messages
|
|
db_messages_run2 = await server.message_manager.list_messages(
|
|
actor=default_user,
|
|
agent_id=agent_state.id,
|
|
run_id=test_run_2.id,
|
|
limit=1000,
|
|
)
|
|
assert len(db_messages_run2) > 0, "Second run should have messages"
|
|
|
|
|
|
class TestEdgeCases:
|
|
"""
|
|
Test edge cases and boundary conditions for cancellation.
|
|
"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_cancellation_with_max_steps_reached(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
test_run,
|
|
):
|
|
"""
|
|
Test interaction between max_steps and cancellation.
|
|
|
|
Verifies:
|
|
- If both max_steps and cancellation occur, correct stop_reason is set
|
|
- Cancellation takes precedence over max_steps
|
|
"""
|
|
# Load agent loop
|
|
agent_loop = AgentLoop.load(agent_state=test_agent_with_tool, actor=default_user)
|
|
|
|
# Cancel after second step, but max_steps=2
|
|
call_count = [0]
|
|
|
|
async def mock_check_cancellation(run_id):
|
|
call_count[0] += 1
|
|
if call_count[0] >= 2:
|
|
await server.run_manager.cancel_run(
|
|
actor=default_user,
|
|
run_id=run_id,
|
|
)
|
|
return True
|
|
return False
|
|
|
|
agent_loop._check_run_cancellation = mock_check_cancellation
|
|
|
|
input_messages = [MessageCreate(role=MessageRole.user, content="Call print_tool with 'test'")]
|
|
|
|
result = await agent_loop.step(
|
|
input_messages=input_messages,
|
|
max_steps=2, # Will hit max_steps around the same time as cancellation
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Stop reason could be either cancelled or max_steps depending on timing
|
|
# Both are acceptable in this edge case
|
|
assert result.stop_reason.stop_reason in ["cancelled", "max_steps"], (
|
|
f"Stop reason should be cancelled or max_steps, got {result.stop_reason.stop_reason}"
|
|
)
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_double_cancellation(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
test_run,
|
|
):
|
|
"""
|
|
Test that cancelling an already-cancelled run is handled gracefully.
|
|
|
|
Verifies:
|
|
- No errors when checking already-cancelled run
|
|
- State remains consistent
|
|
"""
|
|
# Cancel the run
|
|
await server.run_manager.cancel_run(
|
|
actor=default_user,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Load agent loop
|
|
agent_loop = AgentLoop.load(agent_state=test_agent_with_tool, actor=default_user)
|
|
|
|
input_messages = [MessageCreate(role=MessageRole.user, content="Hello")]
|
|
|
|
# First execution - should detect cancellation
|
|
result_1 = await agent_loop.step(
|
|
input_messages=input_messages,
|
|
max_steps=5,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
assert result_1.stop_reason.stop_reason == "cancelled"
|
|
|
|
# Try to execute again with same cancelled run - should handle gracefully
|
|
agent_loop_2 = AgentLoop.load(
|
|
agent_state=await server.agent_manager.get_agent_by_id_async(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
include_relationships=["memory", "tools", "sources"],
|
|
),
|
|
actor=default_user,
|
|
)
|
|
|
|
result_2 = await agent_loop_2.step(
|
|
input_messages=input_messages,
|
|
max_steps=5,
|
|
run_id=test_run.id,
|
|
)
|
|
|
|
# Should still detect as cancelled
|
|
assert result_2.stop_reason.stop_reason == "cancelled"
|
|
|
|
|
|
class TestErrorDataPersistence:
|
|
"""
|
|
Test that error data is properly stored in run metadata when runs fail.
|
|
This ensures errors can be debugged by inspecting the run's metadata field.
|
|
"""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_error_data_stored_in_run_metadata_on_background_streaming_llm_error(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
):
|
|
"""
|
|
Test that when a background streaming run fails due to an LLM error,
|
|
the error details are stored in the run's metadata field.
|
|
|
|
This test validates the fix for the issue where failed runs showed
|
|
empty metadata in the database, making it impossible to debug errors.
|
|
|
|
The test patches LettaAgentV3.stream to raise an LLMError, simulating
|
|
what happens when the LLM provider returns an error during streaming.
|
|
|
|
Verifies:
|
|
- Run status is set to 'failed'
|
|
- Run metadata contains 'error' key with error details
|
|
"""
|
|
from unittest.mock import patch
|
|
|
|
from letta.agents.letta_agent_v3 import LettaAgentV3
|
|
from letta.errors import LLMError
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from letta.services.streaming_service import StreamingService
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|
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# Create streaming service
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streaming_service = StreamingService(server)
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# Create request with background streaming - NOT background for simplicity
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# Background streaming adds Redis complexity, so we test foreground streaming
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# which still exercises the same error handling in _create_error_aware_stream
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request = LettaStreamingRequest(
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messages=[MessageCreate(role=MessageRole.user, content="Hello, please respond")],
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max_steps=1,
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stream_tokens=True,
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background=False,
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)
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# Mock stream method that raises an error
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async def mock_stream_raises_llm_error(*args, **kwargs):
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raise LLMError("Simulated LLM error for testing")
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yield # Make it a generator
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# Use patch to simulate the error during streaming
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with patch.object(LettaAgentV3, "stream", mock_stream_raises_llm_error):
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# Start the streaming request
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run, stream_response = await streaming_service.create_agent_stream(
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agent_id=test_agent_with_tool.id,
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actor=default_user,
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request=request,
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run_type="test_error_persistence",
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)
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assert run is not None, "Run should be created"
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|
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# Consume the stream to trigger error handling
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collected_chunks = []
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async for chunk in stream_response.body_iterator:
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collected_chunks.append(chunk)
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# Give any async handling time to complete
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await asyncio.sleep(0.2)
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|
|
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# Fetch the run from the database
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fetched_run = await server.run_manager.get_run_by_id(run.id, actor=default_user)
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# Verify the run status is failed
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assert fetched_run.status == RunStatus.failed, f"Expected status 'failed', got '{fetched_run.status}'"
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|
|
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# Verify metadata contains error information
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assert fetched_run.metadata is not None, (
|
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f"Run metadata should not be None after error. "
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f"Run ID: {run.id}, Status: {fetched_run.status}, Stop reason: {fetched_run.stop_reason}"
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)
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assert "error" in fetched_run.metadata, f"Run metadata should contain 'error' key, got: {fetched_run.metadata}"
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|
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error_info = fetched_run.metadata["error"]
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# The error is stored as a dict from LettaErrorMessage.model_dump()
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assert isinstance(error_info, dict), f"Error info should be a dict, got: {type(error_info)}"
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assert "error_type" in error_info, f"Error info should contain 'error_type', got: {error_info}"
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assert error_info["error_type"] == "llm_error", f"Expected error_type 'llm_error', got: {error_info['error_type']}"
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|
|
|
@pytest.mark.asyncio
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|
async def test_error_data_stored_on_streaming_timeout_error(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
):
|
|
"""
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|
Test that timeout errors during streaming store error data.
|
|
|
|
Verifies:
|
|
- Timeout errors are properly captured in run metadata
|
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- Run can be queried from DB and error details are available
|
|
"""
|
|
from unittest.mock import patch
|
|
|
|
from letta.agents.letta_agent_v3 import LettaAgentV3
|
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from letta.errors import LLMTimeoutError
|
|
from letta.services.streaming_service import StreamingService
|
|
|
|
streaming_service = StreamingService(server)
|
|
|
|
request = LettaStreamingRequest(
|
|
messages=[MessageCreate(role=MessageRole.user, content="Hello")],
|
|
max_steps=1,
|
|
stream_tokens=True,
|
|
background=False,
|
|
)
|
|
|
|
async def mock_stream_raises_timeout(*args, **kwargs):
|
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raise LLMTimeoutError("Request timed out after 30 seconds")
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|
yield
|
|
|
|
with patch.object(LettaAgentV3, "stream", mock_stream_raises_timeout):
|
|
run, stream_response = await streaming_service.create_agent_stream(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
request=request,
|
|
run_type="test_timeout_error",
|
|
)
|
|
|
|
# Consume the stream
|
|
async for _ in stream_response.body_iterator:
|
|
pass
|
|
|
|
await asyncio.sleep(0.2)
|
|
|
|
fetched_run = await server.run_manager.get_run_by_id(run.id, actor=default_user)
|
|
|
|
assert fetched_run.status == RunStatus.failed
|
|
assert fetched_run.metadata is not None, f"Run metadata should contain error info for run {run.id}"
|
|
assert "error" in fetched_run.metadata
|
|
assert fetched_run.metadata["error"]["error_type"] == "llm_timeout"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_error_data_stored_on_streaming_rate_limit_error(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
):
|
|
"""
|
|
Test that rate limit errors during streaming store error data.
|
|
|
|
Verifies:
|
|
- Rate limit errors are properly captured in run metadata
|
|
"""
|
|
from unittest.mock import patch
|
|
|
|
from letta.agents.letta_agent_v3 import LettaAgentV3
|
|
from letta.errors import LLMRateLimitError
|
|
from letta.services.streaming_service import StreamingService
|
|
|
|
streaming_service = StreamingService(server)
|
|
|
|
request = LettaStreamingRequest(
|
|
messages=[MessageCreate(role=MessageRole.user, content="Hello")],
|
|
max_steps=1,
|
|
stream_tokens=True,
|
|
background=False,
|
|
)
|
|
|
|
async def mock_stream_raises_rate_limit(*args, **kwargs):
|
|
raise LLMRateLimitError("Rate limit exceeded: 100 requests per minute")
|
|
yield
|
|
|
|
with patch.object(LettaAgentV3, "stream", mock_stream_raises_rate_limit):
|
|
run, stream_response = await streaming_service.create_agent_stream(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
request=request,
|
|
run_type="test_rate_limit_error",
|
|
)
|
|
|
|
async for _ in stream_response.body_iterator:
|
|
pass
|
|
|
|
await asyncio.sleep(0.2)
|
|
|
|
fetched_run = await server.run_manager.get_run_by_id(run.id, actor=default_user)
|
|
|
|
assert fetched_run.status == RunStatus.failed
|
|
assert fetched_run.metadata is not None
|
|
assert "error" in fetched_run.metadata
|
|
assert fetched_run.metadata["error"]["error_type"] == "llm_rate_limit"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_error_data_stored_on_streaming_auth_error(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
):
|
|
"""
|
|
Test that authentication errors during streaming store error data.
|
|
|
|
Verifies:
|
|
- Auth errors are properly captured in run metadata
|
|
"""
|
|
from unittest.mock import patch
|
|
|
|
from letta.agents.letta_agent_v3 import LettaAgentV3
|
|
from letta.errors import LLMAuthenticationError
|
|
from letta.services.streaming_service import StreamingService
|
|
|
|
streaming_service = StreamingService(server)
|
|
|
|
request = LettaStreamingRequest(
|
|
messages=[MessageCreate(role=MessageRole.user, content="Hello")],
|
|
max_steps=1,
|
|
stream_tokens=True,
|
|
background=False,
|
|
)
|
|
|
|
async def mock_stream_raises_auth_error(*args, **kwargs):
|
|
raise LLMAuthenticationError("Invalid API key")
|
|
yield
|
|
|
|
with patch.object(LettaAgentV3, "stream", mock_stream_raises_auth_error):
|
|
run, stream_response = await streaming_service.create_agent_stream(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
request=request,
|
|
run_type="test_auth_error",
|
|
)
|
|
|
|
async for _ in stream_response.body_iterator:
|
|
pass
|
|
|
|
await asyncio.sleep(0.2)
|
|
|
|
fetched_run = await server.run_manager.get_run_by_id(run.id, actor=default_user)
|
|
|
|
assert fetched_run.status == RunStatus.failed
|
|
assert fetched_run.metadata is not None
|
|
assert "error" in fetched_run.metadata
|
|
assert fetched_run.metadata["error"]["error_type"] == "llm_authentication"
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_error_data_stored_on_generic_exception(
|
|
self,
|
|
server: SyncServer,
|
|
default_user,
|
|
test_agent_with_tool,
|
|
):
|
|
"""
|
|
Test that generic exceptions during streaming store error data.
|
|
|
|
Verifies:
|
|
- Generic exceptions result in error data being stored
|
|
- Error details are preserved in metadata with 'internal_error' type
|
|
"""
|
|
from unittest.mock import patch
|
|
|
|
from letta.agents.letta_agent_v3 import LettaAgentV3
|
|
from letta.services.streaming_service import StreamingService
|
|
|
|
streaming_service = StreamingService(server)
|
|
|
|
request = LettaStreamingRequest(
|
|
messages=[MessageCreate(role=MessageRole.user, content="Hello")],
|
|
max_steps=1,
|
|
stream_tokens=True,
|
|
background=False,
|
|
)
|
|
|
|
async def mock_stream_raises_generic_error(*args, **kwargs):
|
|
raise RuntimeError("Unexpected internal error")
|
|
yield
|
|
|
|
with patch.object(LettaAgentV3, "stream", mock_stream_raises_generic_error):
|
|
run, stream_response = await streaming_service.create_agent_stream(
|
|
agent_id=test_agent_with_tool.id,
|
|
actor=default_user,
|
|
request=request,
|
|
run_type="test_generic_error",
|
|
)
|
|
|
|
async for _ in stream_response.body_iterator:
|
|
pass
|
|
|
|
await asyncio.sleep(0.2)
|
|
|
|
fetched_run = await server.run_manager.get_run_by_id(run.id, actor=default_user)
|
|
|
|
assert fetched_run.status == RunStatus.failed
|
|
assert fetched_run.metadata is not None, "Run metadata should contain error info for generic exception"
|
|
assert "error" in fetched_run.metadata
|
|
assert fetched_run.metadata["error"]["error_type"] == "internal_error"
|