* Fix agent loop continuing after cancellation in letta_agent_v3 Bug: When a run is cancelled, _check_run_cancellation() sets self.should_continue=False and returns early from _step(), but the outer for loop (line 245) continues to the next iteration, executing subsequent steps even though cancellation was requested. Symptom: User hits cancel during step 1, backend marks run as cancelled, but agent continues executing steps 2, 3, etc. Root cause: After the 'async for chunk in response' loop completes (line 255), there was no check of self.should_continue before continuing to the next iteration of the outer step loop. Fix: Added 'if not self.should_continue: break' check after the inner loop to exit the outer step loop when cancellation is detected. This makes v3 consistent with v2 which already had this check (line 306-307). 🐾 Generated with [Letta Code](https://letta.com) Co-authored-by: Letta <noreply@letta.com> * add integration tests * fix: misc fixes required to get cancellations to work on letta code localhost --------- Co-authored-by: Letta <noreply@letta.com> Co-authored-by: Sarah Wooders <sarahwooders@gmail.com>
191 lines
5.5 KiB
Python
191 lines
5.5 KiB
Python
import asyncio
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import json
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import os
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import threading
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from typing import Any, List
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import pytest
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from dotenv import load_dotenv
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from letta_client import AsyncLetta, MessageCreate
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from letta.log import get_logger
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from letta.schemas.agent import AgentState
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from letta.schemas.enums import AgentType, JobStatus
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from letta.schemas.llm_config import LLMConfig
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logger = get_logger(__name__)
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def get_llm_config(filename: str, llm_config_dir: str = "tests/configs/llm_model_configs") -> LLMConfig:
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filename = os.path.join(llm_config_dir, filename)
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with open(filename, "r") as f:
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config_data = json.load(f)
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llm_config = LLMConfig(**config_data)
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return llm_config
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all_configs = [
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"openai-gpt-4o-mini.json",
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"openai-o3.json",
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"openai-gpt-5.json",
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"claude-4-5-sonnet.json",
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"claude-4-1-opus.json",
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"gemini-2.5-flash.json",
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]
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requested = os.getenv("LLM_CONFIG_FILE")
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filenames = [requested] if requested else all_configs
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TESTED_LLM_CONFIGS: List[LLMConfig] = [get_llm_config(fn) for fn in filenames]
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def roll_dice(num_sides: int) -> int:
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"""
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Returns a random number between 1 and num_sides.
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Args:
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num_sides (int): The number of sides on the die.
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Returns:
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int: A random integer between 1 and num_sides, representing the die roll.
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"""
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import random
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return random.randint(1, num_sides)
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USER_MESSAGE_ROLL_DICE: List[MessageCreate] = [
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MessageCreate(
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role="user",
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content="This is an automated test message. Call the roll_dice tool with 16 sides and reply back to me with the outcome.",
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)
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]
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async def accumulate_chunks(chunks: Any) -> List[Any]:
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"""
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Accumulates chunks into a list of messages.
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"""
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messages = []
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current_message = None
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prev_message_type = None
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async for chunk in chunks:
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current_message_type = chunk.message_type
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if prev_message_type != current_message_type:
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messages.append(current_message)
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current_message = chunk
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else:
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current_message = chunk
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prev_message_type = current_message_type
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messages.append(current_message)
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return [m for m in messages if m is not None]
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async def cancel_run_after_delay(client: AsyncLetta, agent_id: str, delay: float = 0.5):
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await asyncio.sleep(delay)
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await client.agents.messages.cancel(agent_id=agent_id)
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@pytest.fixture(scope="function")
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def server_url() -> str:
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"""
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Provides the URL for the Letta server.
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If LETTA_SERVER_URL is not set, starts the server in a background thread
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and polls until it's accepting connections.
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"""
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def _run_server() -> None:
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load_dotenv()
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from letta.server.rest_api.app import start_server
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start_server(debug=True)
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url: str = os.getenv("LETTA_SERVER_URL", "http://localhost:8283")
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if not os.getenv("LETTA_SERVER_URL"):
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thread = threading.Thread(target=_run_server, daemon=True)
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thread.start()
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timeout_seconds = 30
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import time
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import httpx
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start_time = time.time()
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while time.time() - start_time < timeout_seconds:
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try:
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response = httpx.get(url + "/v1/health", timeout=1.0)
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if response.status_code == 200:
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break
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except Exception:
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pass
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time.sleep(0.5)
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else:
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raise TimeoutError(f"Server at {url} did not become ready in {timeout_seconds}s")
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return url
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@pytest.fixture(scope="function")
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async def client(server_url: str) -> AsyncLetta:
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"""
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Creates and returns an asynchronous Letta REST client for testing.
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"""
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client_instance = AsyncLetta(base_url=server_url)
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yield client_instance
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@pytest.fixture(scope="function")
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async def agent_state(client: AsyncLetta) -> AgentState:
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"""
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Creates and returns an agent state for testing with a pre-configured agent.
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The agent is configured with the roll_dice tool.
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"""
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dice_tool = await client.tools.upsert_from_function(func=roll_dice)
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agent_state_instance = await client.agents.create(
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agent_type=AgentType.letta_v1_agent,
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name="test_agent",
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include_base_tools=False,
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tool_ids=[dice_tool.id],
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model="openai/gpt-4o",
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embedding="openai/text-embedding-3-small",
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tags=["test"],
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)
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yield agent_state_instance
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await client.agents.delete(agent_state_instance.id)
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@pytest.mark.parametrize(
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"llm_config",
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TESTED_LLM_CONFIGS,
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ids=[c.model for c in TESTED_LLM_CONFIGS],
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)
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@pytest.mark.asyncio(loop_scope="function")
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async def test_background_streaming_cancellation(
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disable_e2b_api_key: Any,
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client: AsyncLetta,
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agent_state: AgentState,
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llm_config: LLMConfig,
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) -> None:
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agent_state = await client.agents.modify(agent_id=agent_state.id, llm_config=llm_config)
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delay = 5 if llm_config.model == "gpt-5" else 0.5
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_cancellation_task = asyncio.create_task(cancel_run_after_delay(client, agent_state.id, delay=delay))
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response = client.agents.messages.create_stream(
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agent_id=agent_state.id,
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messages=USER_MESSAGE_ROLL_DICE,
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stream_tokens=True,
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background=True,
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)
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messages = await accumulate_chunks(response)
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run_id = messages[0].run_id
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await _cancellation_task
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run = await client.runs.retrieve(run_id=run_id)
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assert run.status == JobStatus.cancelled
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response = client.runs.stream(run_id=run_id, starting_after=0)
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messages_from_stream = await accumulate_chunks(response)
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assert len(messages_from_stream) > 0
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