a3545110cf
* feat: add full responses api support in new agent loop * update matrix in workflow * relax check for reasoning messages for high effort gpt 5 * fix indent * one more relax
552 lines
20 KiB
Python
552 lines
20 KiB
Python
import base64
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import json
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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 contextlib import contextmanager
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from http.server import BaseHTTPRequestHandler, HTTPServer
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from typing import Any, Dict, List
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from unittest.mock import patch
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import httpx
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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, Letta, LettaRequest, MessageCreate, Run
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from letta_client.core.api_error import ApiError
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from letta_client.types import (
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AssistantMessage,
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Base64Image,
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HiddenReasoningMessage,
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ImageContent,
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LettaMessageUnion,
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LettaStopReason,
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LettaUsageStatistics,
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ReasoningMessage,
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TextContent,
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ToolCallMessage,
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ToolReturnMessage,
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UrlImage,
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UserMessage,
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)
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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.letta_ping import LettaPing
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from letta.schemas.llm_config import LLMConfig
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logger = get_logger(__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-3-5-sonnet.json",
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"claude-3-7-sonnet-extended.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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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[MessageCreate] = [
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MessageCreate(
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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[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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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 = get_expected_message_count(llm_config, streaming=streaming, from_db=from_db)
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try:
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assert len(messages) == expected_message_count
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except:
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# Reasoning summary in responses API when effort is high is still flaky, so don't throw if missing
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if LLMConfig.is_openai_reasoning_model(llm_config):
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assert len(messages) == expected_message_count - 1
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else:
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raise
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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 (
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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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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 summary in responses API when effort is high is still flaky, so don't throw if missing
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if LLMConfig.is_openai_reasoning_model(llm_config):
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pass
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else:
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raise
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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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) -> 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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expected_message_count = get_expected_message_count(llm_config, tool_call=True, streaming=streaming, from_db=from_db)
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try:
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assert len(messages) == expected_message_count
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except:
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# Reasoning summary in responses API when effort is high is still flaky, so don't throw if missing
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if LLMConfig.is_openai_reasoning_model(llm_config):
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assert len(messages) == expected_message_count - 1
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else:
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raise
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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 (
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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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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 summary in responses API when effort is high is still flaky, so don't throw if missing
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if LLMConfig.is_openai_reasoning_model(llm_config):
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pass
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else:
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raise
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# Assistant message
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if llm_config.model_endpoint_type == "anthropic":
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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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otid_suffix += 1
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# Tool call message
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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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otid_suffix = 0
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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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# 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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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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async def accumulate_chunks(chunks: List[Any], 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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"""
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messages = []
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current_message = None
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prev_message_type = None
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chunk_count = 0
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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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if (
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prev_message_type
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and verify_token_streaming
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and current_message.message_type in ["reasoning_message", "assistant_message", "tool_call_message"]
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):
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assert chunk_count > 1, f"Expected more than one chunk for {current_message.message_type}. Messages: {messages}"
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current_message = None
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chunk_count = 0
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if current_message is None:
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current_message = chunk
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else:
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pass # TODO: actually accumulate the chunks. For now we only care about the count
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prev_message_type = current_message_type
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chunk_count += 1
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messages.append(current_message)
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if verify_token_streaming and current_message.message_type in ["reasoning_message", "assistant_message", "tool_call_message"]:
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assert chunk_count > 1, f"Expected more than one chunk for {current_message.message_type}"
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return [m for m in messages if m is not None]
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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 == "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(llm_config: LLMConfig, tool_call: bool = False, streaming: bool = False, from_db: bool = False) -> int:
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"""
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Returns the expected number of messages for a given LLM configuration.
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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 | AssistantMessage |
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| | | 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 | flash-2.5-thinking |
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| ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ | ------------------------ |
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| ToolCallMessage | ToolCallMessage | ReasoningMessage | AssistantMessage | ReasoningMessage | ToolCallMessage |
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| ToolReturnMessage | ToolReturnMessage | ToolCallMessage | ToolCallMessage | AssistantMessage | ToolReturnMessage |
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| AssistantMessage | AssistantMessage | ToolReturnMessage | ToolReturnMessage | ToolCallMessage | AssistantMessage |
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| | | AssistantMessage | AssistantMessage | ToolReturnMessage | |
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| | | | | AssistantMessage | |
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"""
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is_reasoner_model = (
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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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# assistant message
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expected_message_count = 1
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if is_reasoner_model:
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# reasoning message
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expected_message_count += 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 llm_config.model_endpoint_type == "anthropic":
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# anthropic models return an assistant message first before the tool call message
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expected_message_count += 1
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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
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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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"""
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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 named 'supervisor' and is configured with base tools and 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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# ------------------------------
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# Test Cases
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# ------------------------------
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|
|
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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.parametrize("send_type", ["step", "stream_steps", "stream_tokens", "stream_tokens_background", "async"])
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@pytest.mark.asyncio(loop_scope="function")
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async def test_greeting(
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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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send_type: str,
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) -> None:
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last_message = await client.agents.messages.list(agent_id=agent_state.id, limit=1)
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agent_state = await client.agents.modify(agent_id=agent_state.id, llm_config=llm_config)
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|
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if send_type == "step":
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response = await client.agents.messages.create(
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agent_id=agent_state.id,
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messages=USER_MESSAGE_FORCE_REPLY,
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)
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messages = response.messages
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run_id = messages[0].run_id
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elif send_type == "async":
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run = await client.agents.messages.create_async(
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agent_id=agent_state.id,
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messages=USER_MESSAGE_FORCE_REPLY,
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)
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run = await wait_for_run_completion(client, run.id)
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messages = await client.runs.messages.list(run_id=run.id)
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messages = [m for m in messages if m.message_type != "user_message"]
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run_id = run.id
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else:
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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_FORCE_REPLY,
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stream_tokens=(send_type == "stream_tokens"),
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background=(send_type == "stream_tokens_background"),
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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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assert_greeting_response(
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messages, streaming=("stream" in send_type), token_streaming=(send_type == "stream_tokens"), llm_config=llm_config
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)
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if "background" in send_type:
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response = client.runs.stream(run_id=run_id, starting_after=0)
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messages = await accumulate_chunks(response)
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assert_greeting_response(
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messages, streaming=("stream" in send_type), token_streaming=(send_type == "stream_tokens"), llm_config=llm_config
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)
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messages_from_db = await client.agents.messages.list(agent_id=agent_state.id, after=last_message[0].id)
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assert_greeting_response(messages_from_db, from_db=True, llm_config=llm_config)
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assert run_id is not None
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run = await client.runs.retrieve(run_id=run_id)
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assert run.status == JobStatus.completed
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|
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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],
|
|
)
|
|
@pytest.mark.parametrize("send_type", ["step", "stream_steps", "stream_tokens", "stream_tokens_background", "async"])
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|
@pytest.mark.asyncio(loop_scope="function")
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|
async def test_tool_call(
|
|
disable_e2b_api_key: Any,
|
|
client: AsyncLetta,
|
|
agent_state: AgentState,
|
|
llm_config: LLMConfig,
|
|
send_type: str,
|
|
) -> None:
|
|
last_message = await client.agents.messages.list(agent_id=agent_state.id, limit=1)
|
|
agent_state = await client.agents.modify(agent_id=agent_state.id, llm_config=llm_config)
|
|
|
|
if send_type == "step":
|
|
response = await client.agents.messages.create(
|
|
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.create_async(
|
|
agent_id=agent_state.id,
|
|
messages=USER_MESSAGE_ROLL_DICE,
|
|
)
|
|
run = await wait_for_run_completion(client, run.id)
|
|
messages = await client.runs.messages.list(run_id=run.id)
|
|
messages = [m for m in messages if m.message_type != "user_message"]
|
|
run_id = run.id
|
|
else:
|
|
response = client.agents.messages.create_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)
|
|
|
|
if "background" in send_type:
|
|
response = client.runs.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)
|
|
|
|
messages_from_db = await client.agents.messages.list(agent_id=agent_state.id, after=last_message[0].id)
|
|
assert_tool_call_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 == JobStatus.completed
|