381ca5bde8
* chore: migrate built in tools integration test to sdk v1 * fix * remove trialing commas
759 lines
31 KiB
Python
759 lines
31 KiB
Python
import asyncio
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import itertools
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import json
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import logging
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import os
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import threading
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import time
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import uuid
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from typing import Any, List, Tuple
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import pytest
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import requests
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from dotenv import load_dotenv
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from letta_client import AsyncLetta
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from letta_client.types import AgentState, MessageCreateParam, ToolReturnMessage
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from letta_client.types.agents import AssistantMessage, ReasoningMessage, Run, ToolCallMessage, UserMessage
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from letta_client.types.agents.letta_streaming_response import LettaPing, LettaStopReason, LettaUsageStatistics
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from letta.schemas.llm_config import LLMConfig
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logger = logging.getLogger(__name__)
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# ------------------------------
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# Helper Functions and Constants
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# ------------------------------
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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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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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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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# Filter out deprecated Claude 3.5 Sonnet model that is no longer available
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TESTED_LLM_CONFIGS = [
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cfg for cfg in TESTED_LLM_CONFIGS if not (cfg.model_endpoint_type == "anthropic" and cfg.model == "claude-3-5-sonnet-20241022")
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]
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# Filter out Bedrock models that require aioboto3 dependency (not available in CI)
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TESTED_LLM_CONFIGS = [cfg for cfg in TESTED_LLM_CONFIGS if not (cfg.model_endpoint_type == "bedrock")]
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# Filter out Gemini models that have Google Cloud permission issues
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TESTED_LLM_CONFIGS = [cfg for cfg in TESTED_LLM_CONFIGS if cfg.model_endpoint_type not in ["google_vertex", "google_ai"]]
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# Filter out qwen2.5:7b model that has server issues
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TESTED_LLM_CONFIGS = [cfg for cfg in TESTED_LLM_CONFIGS if not (cfg.model == "qwen2.5:7b")]
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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_OTID = str(uuid.uuid4())
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USER_MESSAGE_RESPONSE: str = "Teamwork makes the dream work"
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USER_MESSAGE_FORCE_REPLY: List[MessageCreateParam] = [
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MessageCreateParam(
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role="user",
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content=f"This is an automated test message. Reply with the message '{USER_MESSAGE_RESPONSE}'.",
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otid=USER_MESSAGE_OTID,
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)
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]
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USER_MESSAGE_ROLL_DICE: List[MessageCreateParam] = [
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MessageCreateParam(
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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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otid=USER_MESSAGE_OTID,
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)
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]
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USER_MESSAGE_PARALLEL_TOOL_CALL: List[MessageCreateParam] = [
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MessageCreateParam(
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role="user",
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content=("This is an automated test message. Please call the roll_dice tool three times in parallel."),
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otid=USER_MESSAGE_OTID,
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)
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]
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def assert_greeting_response(
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messages: List[Any],
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llm_config: LLMConfig,
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streaming: bool = False,
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token_streaming: bool = False,
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from_db: bool = False,
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) -> None:
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"""
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Asserts that the messages list follows the expected sequence:
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ReasoningMessage -> AssistantMessage.
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"""
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# Filter out LettaPing messages which are keep-alive messages for SSE streams
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messages = [
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msg for msg in messages if not (isinstance(msg, LettaPing) or (hasattr(msg, "message_type") and msg.message_type == "ping"))
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]
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expected_message_count_min, expected_message_count_max = get_expected_message_count_range(
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llm_config, streaming=streaming, from_db=from_db
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)
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assert expected_message_count_min <= len(messages) <= expected_message_count_max
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# User message if loaded from db
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index = 0
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if from_db:
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assert isinstance(messages[index], UserMessage)
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assert messages[index].otid == USER_MESSAGE_OTID
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index += 1
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# Reasoning message if reasoning enabled
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otid_suffix = 0
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try:
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if is_reasoner_model(llm_config):
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assert isinstance(messages[index], ReasoningMessage)
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assert messages[index].otid and messages[index].otid[-1] == str(otid_suffix)
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index += 1
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otid_suffix += 1
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except:
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# Reasoning is non-deterministic, so don't throw if missing
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pass
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# Assistant message
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assert isinstance(messages[index], AssistantMessage)
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if not token_streaming:
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assert "teamwork" in messages[index].content.lower()
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assert messages[index].otid and messages[index].otid[-1] == str(otid_suffix)
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index += 1
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otid_suffix += 1
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# Stop reason and usage statistics if streaming
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if streaming:
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assert isinstance(messages[index], LettaStopReason)
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assert messages[index].stop_reason == "end_turn"
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index += 1
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assert isinstance(messages[index], LettaUsageStatistics)
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assert messages[index].prompt_tokens > 0
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assert messages[index].completion_tokens > 0
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assert messages[index].total_tokens > 0
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assert messages[index].step_count > 0
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def assert_tool_call_response(
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messages: List[Any],
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llm_config: LLMConfig,
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streaming: bool = False,
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from_db: bool = False,
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with_cancellation: bool = False,
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) -> None:
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"""
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Asserts that the messages list follows the expected sequence:
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ReasoningMessage -> ToolCallMessage -> ToolReturnMessage ->
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ReasoningMessage -> AssistantMessage.
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"""
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# Filter out LettaPing messages which are keep-alive messages for SSE streams
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messages = [
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msg for msg in messages if not (isinstance(msg, LettaPing) or (hasattr(msg, "message_type") and msg.message_type == "ping"))
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]
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if not with_cancellation:
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expected_message_count_min, expected_message_count_max = get_expected_message_count_range(
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llm_config, tool_call=True, streaming=streaming, from_db=from_db
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)
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assert expected_message_count_min <= len(messages) <= expected_message_count_max
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# User message if loaded from db
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index = 0
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if from_db:
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assert isinstance(messages[index], UserMessage)
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assert messages[index].otid == USER_MESSAGE_OTID
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index += 1
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# Reasoning message if reasoning enabled
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otid_suffix = 0
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try:
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if is_reasoner_model(llm_config):
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assert isinstance(messages[index], ReasoningMessage)
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assert messages[index].otid and messages[index].otid[-1] == str(otid_suffix)
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index += 1
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otid_suffix += 1
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except:
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# Reasoning is non-deterministic, so don't throw if missing
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pass
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# Special case for claude-sonnet-4-5-20250929 and opus-4.1 which can generate an extra AssistantMessage before tool call
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if (
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(llm_config.model == "claude-sonnet-4-5-20250929" or llm_config.model.startswith("claude-opus-4-1"))
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and index < len(messages)
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and isinstance(messages[index], AssistantMessage)
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):
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# Skip the extra AssistantMessage and move to the next message
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index += 1
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otid_suffix += 1
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# Tool call message (may be skipped if cancelled early)
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if with_cancellation and isinstance(messages[index], AssistantMessage):
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# If cancelled early, model might respond with text instead of making tool call
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assert "roll" in messages[index].content.lower() or "die" in messages[index].content.lower()
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return # Skip tool call assertions for early cancellation
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assert isinstance(messages[index], ToolCallMessage)
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assert messages[index].otid and messages[index].otid[-1] == str(otid_suffix)
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index += 1
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# Tool return message
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assert isinstance(messages[index], ToolReturnMessage)
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assert messages[index].otid and messages[index].otid[-1] == str(otid_suffix)
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index += 1
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# Messages from second agent step if request has not been cancelled
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if not with_cancellation:
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# Reasoning message if reasoning enabled
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try:
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if is_reasoner_model(llm_config):
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assert isinstance(messages[index], ReasoningMessage)
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assert messages[index].otid and messages[index].otid[-1] == str(otid_suffix)
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index += 1
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otid_suffix += 1
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except:
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# Reasoning is non-deterministic, so don't throw if missing
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pass
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# Assistant message
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assert isinstance(messages[index], AssistantMessage)
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assert messages[index].otid and messages[index].otid[-1] == str(otid_suffix)
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index += 1
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# Stop reason and usage statistics if streaming
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if streaming:
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assert isinstance(messages[index], LettaStopReason)
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assert messages[index].stop_reason == ("cancelled" if with_cancellation else "end_turn")
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index += 1
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assert isinstance(messages[index], LettaUsageStatistics)
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assert messages[index].prompt_tokens > 0
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assert messages[index].completion_tokens > 0
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assert messages[index].total_tokens > 0
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assert messages[index].step_count > 0
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async def accumulate_chunks(chunks, verify_token_streaming: bool = False) -> List[Any]:
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"""
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Accumulates chunks into a list of messages.
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Handles both async iterators and raw SSE strings.
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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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# Handle raw SSE string from runs.messages.stream()
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if isinstance(chunks, str):
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import json
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for line in chunks.strip().split("\n"):
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if line.startswith("data: ") and line != "data: [DONE]":
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try:
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data = json.loads(line[6:]) # Remove 'data: ' prefix
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if "message_type" in data:
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# Create proper message type objects
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message_type = data.get("message_type")
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if message_type == "assistant_message":
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from letta_client.types.agents import AssistantMessage
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chunk = AssistantMessage(**data)
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elif message_type == "reasoning_message":
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from letta_client.types.agents import ReasoningMessage
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chunk = ReasoningMessage(**data)
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elif message_type == "tool_call_message":
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from letta_client.types.agents import ToolCallMessage
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chunk = ToolCallMessage(**data)
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elif message_type == "tool_return_message":
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from letta_client.types import ToolReturnMessage
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chunk = ToolReturnMessage(**data)
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elif message_type == "user_message":
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from letta_client.types.agents import UserMessage
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chunk = UserMessage(**data)
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elif message_type == "stop_reason":
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from letta_client.types.agents.letta_streaming_response import LettaStopReason
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chunk = LettaStopReason(**data)
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elif message_type == "usage_statistics":
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from letta_client.types.agents.letta_streaming_response import LettaUsageStatistics
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chunk = LettaUsageStatistics(**data)
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else:
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chunk = type("Chunk", (), data)() # Fallback for unknown types
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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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if current_message is not None:
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messages.append(current_message)
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current_message = chunk
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else:
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# Accumulate content for same message type
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if hasattr(current_message, "content") and hasattr(chunk, "content"):
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current_message.content += chunk.content
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prev_message_type = current_message_type
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except json.JSONDecodeError:
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continue
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if current_message is not None:
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messages.append(current_message)
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else:
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# Handle async iterator from agents.messages.stream()
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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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if current_message is not None:
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messages.append(current_message)
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current_message = chunk
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else:
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# Accumulate content for same message type
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if hasattr(current_message, "content") and hasattr(chunk, "content"):
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current_message.content += chunk.content
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prev_message_type = current_message_type
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if current_message is not None:
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messages.append(current_message)
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return messages
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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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async def wait_for_run_completion(client: AsyncLetta, run_id: str, timeout: float = 30.0, interval: float = 0.5) -> Run:
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start = time.time()
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while True:
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run = await client.runs.retrieve(run_id)
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if run.status == "completed":
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return run
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if run.status == "cancelled":
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time.sleep(5)
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return run
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if run.status == "failed":
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raise RuntimeError(f"Run {run_id} did not complete: status = {run.status}")
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if time.time() - start > timeout:
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raise TimeoutError(f"Run {run_id} did not complete within {timeout} seconds (last status: {run.status})")
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time.sleep(interval)
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def get_expected_message_count_range(
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llm_config: LLMConfig, tool_call: bool = False, streaming: bool = False, from_db: bool = False
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) -> Tuple[int, int]:
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"""
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Returns the expected range of number of messages for a given LLM configuration. Uses range to account for possible variations in the number of reasoning messages.
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Greeting:
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------------------------------------------------------------------------------------------------------------------------------------------------------------------
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| gpt-4o | gpt-o3 (med effort) | gpt-5 (high effort) | sonnet-3-5 | sonnet-3.7-thinking | flash-2.5-thinking |
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| ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ |
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| AssistantMessage | AssistantMessage | ReasoningMessage | AssistantMessage | ReasoningMessage | ReasoningMessage |
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| | | AssistantMessage | | AssistantMessage | AssistantMessage |
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Tool Call:
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---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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| gpt-4o | gpt-o3 (med effort) | gpt-5 (high effort) | sonnet-3-5 | sonnet-3.7-thinking | sonnet-4.5/opus-4.1 | flash-2.5-thinking |
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| ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ |
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| ToolCallMessage | ToolCallMessage | ReasoningMessage | AssistantMessage | ReasoningMessage | ReasoningMessage | ReasoningMessage |
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| ToolReturnMessage | ToolReturnMessage | ToolCallMessage | ToolCallMessage | AssistantMessage | AssistantMessage | ToolCallMessage |
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| AssistantMessage | AssistantMessage | ToolReturnMessage | ToolReturnMessage | ToolCallMessage | ToolCallMessage | ToolReturnMessage |
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| | | ReasoningMessage | AssistantMessage | ToolReturnMessage | ToolReturnMessage | ReasoningMessage |
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| | | AssistantMessage | | AssistantMessage | ReasoningMessage | AssistantMessage |
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| | | | | | AssistantMessage | |
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"""
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# assistant message
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expected_message_count = 1
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expected_range = 0
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if is_reasoner_model(llm_config):
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# reasoning message
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expected_range += 1
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if tool_call:
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# check for sonnet 4.5 or opus 4.1 specifically
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is_sonnet_4_5_or_opus_4_1 = (
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llm_config.model_endpoint_type == "anthropic"
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and llm_config.enable_reasoner
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and (llm_config.model.startswith("claude-sonnet-4-5") or llm_config.model.startswith("claude-opus-4-1"))
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)
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if is_sonnet_4_5_or_opus_4_1 or not LLMConfig.is_anthropic_reasoning_model(llm_config):
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# sonnet 4.5 and opus 4.1 return a reasoning message before the final assistant message
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# so do the other native reasoning models
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expected_range += 1
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# opus 4.1 generates an extra AssistantMessage before the tool call
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if llm_config.model.startswith("claude-opus-4-1"):
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expected_range += 1
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if tool_call:
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# tool call and tool return messages
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expected_message_count += 2
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if from_db:
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# user message
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expected_message_count += 1
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if streaming:
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# stop reason and usage statistics
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expected_message_count += 2
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return expected_message_count, expected_message_count + expected_range
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def is_reasoner_model(llm_config: LLMConfig) -> bool:
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return (
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(LLMConfig.is_openai_reasoning_model(llm_config) and llm_config.reasoning_effort == "high")
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or LLMConfig.is_anthropic_reasoning_model(llm_config)
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or LLMConfig.is_google_vertex_reasoning_model(llm_config)
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or LLMConfig.is_google_ai_reasoning_model(llm_config)
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)
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# ------------------------------
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# Fixtures
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# ------------------------------
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@pytest.fixture(scope="module")
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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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# Poll until the server is up (or timeout)
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timeout_seconds = 30
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deadline = time.time() + timeout_seconds
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while time.time() < deadline:
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try:
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resp = requests.get(url + "/v1/health")
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if resp.status_code < 500:
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break
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except requests.exceptions.RequestException:
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pass
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time.sleep(0.1)
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else:
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raise RuntimeError(f"Could not reach {url} within {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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"""
|
|
client_instance = AsyncLetta(base_url=server_url)
|
|
yield client_instance
|
|
|
|
|
|
@pytest.fixture(scope="function")
|
|
async def agent_state(client: AsyncLetta) -> AgentState:
|
|
"""
|
|
Creates and returns an agent state for testing with a pre-configured agent.
|
|
The agent is named 'supervisor' and is configured with base tools and the roll_dice tool.
|
|
"""
|
|
dice_tool = await client.tools.upsert_from_function(func=roll_dice)
|
|
|
|
agent_state_instance = await client.agents.create(
|
|
agent_type="letta_v1_agent",
|
|
name="test_agent",
|
|
include_base_tools=False,
|
|
tool_ids=[dice_tool.id],
|
|
model="openai/gpt-4o",
|
|
embedding="openai/text-embedding-3-small",
|
|
tags=["test"],
|
|
)
|
|
yield agent_state_instance
|
|
|
|
await client.agents.delete(agent_state_instance.id)
|
|
|
|
|
|
# ------------------------------
|
|
# Test Cases
|
|
# ------------------------------
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"llm_config",
|
|
TESTED_LLM_CONFIGS,
|
|
ids=[c.model for c in TESTED_LLM_CONFIGS],
|
|
)
|
|
@pytest.mark.parametrize("send_type", ["step", "stream_steps", "stream_tokens", "stream_tokens_background", "async"])
|
|
@pytest.mark.asyncio(loop_scope="function")
|
|
async def test_greeting(
|
|
disable_e2b_api_key: Any,
|
|
client: AsyncLetta,
|
|
agent_state: AgentState,
|
|
llm_config: LLMConfig,
|
|
send_type: str,
|
|
) -> None:
|
|
last_message_page = await client.agents.messages.list(agent_id=agent_state.id, limit=1)
|
|
last_message = last_message_page.items[0] if last_message_page.items else None
|
|
agent_state = await client.agents.modify(agent_id=agent_state.id, llm_config=llm_config)
|
|
|
|
if send_type == "step":
|
|
response = await client.agents.messages.send(
|
|
agent_id=agent_state.id,
|
|
messages=USER_MESSAGE_FORCE_REPLY,
|
|
)
|
|
messages = response.messages
|
|
run_id = messages[0].run_id
|
|
elif send_type == "async":
|
|
run = await client.agents.messages.send_async(
|
|
agent_id=agent_state.id,
|
|
messages=USER_MESSAGE_FORCE_REPLY,
|
|
)
|
|
run = await wait_for_run_completion(client, run.id, timeout=60.0)
|
|
messages_page = await client.runs.messages.list(run_id=run.id)
|
|
messages = [m for m in messages_page.items if m.message_type != "user_message"]
|
|
run_id = run.id
|
|
else:
|
|
response = await client.agents.messages.stream(
|
|
agent_id=agent_state.id,
|
|
messages=USER_MESSAGE_FORCE_REPLY,
|
|
stream_tokens=(send_type == "stream_tokens"),
|
|
background=(send_type == "stream_tokens_background"),
|
|
)
|
|
messages = await accumulate_chunks(response)
|
|
run_id = messages[0].run_id
|
|
|
|
assert_greeting_response(
|
|
messages, streaming=("stream" in send_type), token_streaming=(send_type == "stream_tokens"), llm_config=llm_config
|
|
)
|
|
|
|
if "background" in send_type:
|
|
response = await client.runs.messages.stream(run_id=run_id, starting_after=0)
|
|
messages = await accumulate_chunks(response)
|
|
assert_greeting_response(
|
|
messages, streaming=("stream" in send_type), token_streaming=(send_type == "stream_tokens"), llm_config=llm_config
|
|
)
|
|
|
|
messages_from_db_page = await client.agents.messages.list(agent_id=agent_state.id, after=last_message.id if last_message else None)
|
|
messages_from_db = messages_from_db_page.items
|
|
assert_greeting_response(messages_from_db, from_db=True, llm_config=llm_config)
|
|
|
|
assert run_id is not None
|
|
run = await client.runs.retrieve(run_id=run_id)
|
|
assert run.status == "completed"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"llm_config",
|
|
TESTED_LLM_CONFIGS,
|
|
ids=[c.model for c in TESTED_LLM_CONFIGS],
|
|
)
|
|
@pytest.mark.parametrize("send_type", ["step", "stream_steps", "stream_tokens", "stream_tokens_background", "async"])
|
|
@pytest.mark.asyncio(loop_scope="function")
|
|
async def test_parallel_tool_call_anthropic(
|
|
disable_e2b_api_key: Any,
|
|
client: AsyncLetta,
|
|
agent_state: AgentState,
|
|
llm_config: LLMConfig,
|
|
send_type: str,
|
|
) -> None:
|
|
if llm_config.model_endpoint_type != "anthropic":
|
|
pytest.skip("Parallel tool calling test only applies to Anthropic models.")
|
|
|
|
# change llm_config to support parallel tool calling
|
|
llm_config.parallel_tool_calls = True
|
|
agent_state = await client.agents.modify(agent_id=agent_state.id, llm_config=llm_config)
|
|
|
|
if send_type == "step":
|
|
await client.agents.messages.send(
|
|
agent_id=agent_state.id,
|
|
messages=USER_MESSAGE_PARALLEL_TOOL_CALL,
|
|
)
|
|
elif send_type == "async":
|
|
run = await client.agents.messages.send_async(
|
|
agent_id=agent_state.id,
|
|
messages=USER_MESSAGE_PARALLEL_TOOL_CALL,
|
|
)
|
|
await wait_for_run_completion(client, run.id, timeout=60.0)
|
|
else:
|
|
response = await client.agents.messages.stream(
|
|
agent_id=agent_state.id,
|
|
messages=USER_MESSAGE_PARALLEL_TOOL_CALL,
|
|
stream_tokens=(send_type == "stream_tokens"),
|
|
background=(send_type == "stream_tokens_background"),
|
|
)
|
|
await accumulate_chunks(response)
|
|
|
|
# validate parallel tool call behavior in preserved messages
|
|
preserved_messages_page = await client.agents.messages.list(agent_id=agent_state.id)
|
|
preserved_messages = preserved_messages_page.items
|
|
|
|
# find the tool call message in preserved messages
|
|
tool_call_msg = None
|
|
tool_return_msg = None
|
|
for msg in preserved_messages:
|
|
if isinstance(msg, ToolCallMessage):
|
|
tool_call_msg = msg
|
|
elif isinstance(msg, ToolReturnMessage):
|
|
tool_return_msg = msg
|
|
|
|
# assert parallel tool calls were made
|
|
assert tool_call_msg is not None, "ToolCallMessage not found in preserved messages"
|
|
assert hasattr(tool_call_msg, "tool_calls"), "tool_calls field not found in ToolCallMessage"
|
|
assert len(tool_call_msg.tool_calls) == 3, f"Expected 3 parallel tool calls, got {len(tool_call_msg.tool_calls)}"
|
|
|
|
# verify each tool call and collect num_sides values
|
|
num_sides_values = []
|
|
for tc in tool_call_msg.tool_calls:
|
|
assert tc.name == "roll_dice"
|
|
assert tc.tool_call_id.startswith("toolu_")
|
|
assert "num_sides" in tc.arguments
|
|
# Parse the num_sides value from the arguments
|
|
import json
|
|
|
|
args = json.loads(tc.arguments)
|
|
num_sides = int(args["num_sides"])
|
|
num_sides_values.append(num_sides)
|
|
|
|
# assert tool returns match the tool calls
|
|
assert tool_return_msg is not None, "ToolReturnMessage not found in preserved messages"
|
|
assert hasattr(tool_return_msg, "tool_returns"), "tool_returns field not found in ToolReturnMessage"
|
|
assert len(tool_return_msg.tool_returns) == 3, f"Expected 3 tool returns, got {len(tool_return_msg.tool_returns)}"
|
|
|
|
# verify each tool return matches the corresponding tool call's num_sides
|
|
tool_call_ids = {tc.tool_call_id for tc in tool_call_msg.tool_calls}
|
|
for i, tr in enumerate(tool_return_msg.tool_returns):
|
|
assert tr.type == "tool"
|
|
assert tr.status == "success"
|
|
assert tr.tool_call_id in tool_call_ids, f"tool_call_id {tr.tool_call_id} not found in tool calls"
|
|
# Check that the tool return value is within the range of the corresponding num_sides
|
|
expected_max = num_sides_values[i] if i < len(num_sides_values) else max(num_sides_values)
|
|
assert int(tr.tool_return) >= 1 and int(tr.tool_return) <= expected_max, (
|
|
f"Tool return {tr.tool_return} is not within range 1-{expected_max}"
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"llm_config",
|
|
TESTED_LLM_CONFIGS,
|
|
ids=[c.model for c in TESTED_LLM_CONFIGS],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
["send_type", "cancellation"],
|
|
list(
|
|
itertools.product(
|
|
["step", "stream_steps", "stream_tokens", "stream_tokens_background", "async"], ["with_cancellation", "no_cancellation"]
|
|
)
|
|
),
|
|
ids=[
|
|
f"{s}-{c}"
|
|
for s, c in itertools.product(
|
|
["step", "stream_steps", "stream_tokens", "stream_tokens_background", "async"], ["with_cancellation", "no_cancellation"]
|
|
)
|
|
],
|
|
)
|
|
@pytest.mark.asyncio(loop_scope="function")
|
|
async def test_tool_call(
|
|
disable_e2b_api_key: Any,
|
|
client: AsyncLetta,
|
|
agent_state: AgentState,
|
|
llm_config: LLMConfig,
|
|
send_type: str,
|
|
cancellation: str,
|
|
) -> None:
|
|
# Skip models with OTID mismatch issues between ToolCallMessage and ToolReturnMessage
|
|
if llm_config.model == "gpt-5" or llm_config.model == "claude-sonnet-4-5-20250929" or llm_config.model.startswith("claude-opus-4-1"):
|
|
pytest.skip(f"Skipping {llm_config.model} due to OTID chain issue - messages receive incorrect OTID suffixes")
|
|
|
|
last_message_page = await client.agents.messages.list(agent_id=agent_state.id, limit=1)
|
|
last_message = last_message_page.items[0] if last_message_page.items else None
|
|
agent_state = await client.agents.modify(agent_id=agent_state.id, llm_config=llm_config)
|
|
|
|
if cancellation == "with_cancellation":
|
|
delay = 5 if llm_config.model == "gpt-5" else 0.5 # increase delay for responses api
|
|
_cancellation_task = asyncio.create_task(cancel_run_after_delay(client, agent_state.id, delay=delay))
|
|
|
|
if send_type == "step":
|
|
response = await client.agents.messages.send(
|
|
agent_id=agent_state.id,
|
|
messages=USER_MESSAGE_ROLL_DICE,
|
|
)
|
|
messages = response.messages
|
|
run_id = messages[0].run_id
|
|
elif send_type == "async":
|
|
run = await client.agents.messages.send_async(
|
|
agent_id=agent_state.id,
|
|
messages=USER_MESSAGE_ROLL_DICE,
|
|
)
|
|
run = await wait_for_run_completion(client, run.id, timeout=60.0)
|
|
messages_page = await client.runs.messages.list(run_id=run.id)
|
|
messages = [m for m in messages_page.items if m.message_type != "user_message"]
|
|
run_id = run.id
|
|
else:
|
|
response = await client.agents.messages.stream(
|
|
agent_id=agent_state.id,
|
|
messages=USER_MESSAGE_ROLL_DICE,
|
|
stream_tokens=(send_type == "stream_tokens"),
|
|
background=(send_type == "stream_tokens_background"),
|
|
)
|
|
messages = await accumulate_chunks(response)
|
|
run_id = messages[0].run_id
|
|
|
|
assert_tool_call_response(
|
|
messages, streaming=("stream" in send_type), llm_config=llm_config, with_cancellation=(cancellation == "with_cancellation")
|
|
)
|
|
|
|
if "background" in send_type:
|
|
response = await client.runs.messages.stream(run_id=run_id, starting_after=0)
|
|
messages = await accumulate_chunks(response)
|
|
assert_tool_call_response(
|
|
messages, streaming=("stream" in send_type), llm_config=llm_config, with_cancellation=(cancellation == "with_cancellation")
|
|
)
|
|
|
|
messages_from_db_page = await client.agents.messages.list(agent_id=agent_state.id, after=last_message.id if last_message else None)
|
|
messages_from_db = messages_from_db_page.items
|
|
assert_tool_call_response(
|
|
messages_from_db, from_db=True, llm_config=llm_config, with_cancellation=(cancellation == "with_cancellation")
|
|
)
|
|
|
|
assert run_id is not None
|
|
run = await client.runs.retrieve(run_id=run_id)
|
|
assert run.status == ("cancelled" if cancellation == "with_cancellation" else "completed")
|