feat: Add basic test serialization tests (#4076)
This commit is contained in:
Matthew Zhou
@@ -1,15 +1,17 @@
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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from openai.types.chat.chat_completion_message_tool_call import ChatCompletionMessageToolCall as OpenAIToolCall
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from pydantic import BaseModel, Field
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from letta.helpers.datetime_helpers import get_utc_time
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from letta.schemas.agent import AgentState, CreateAgent
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from letta.schemas.block import Block, CreateBlock
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from letta.schemas.enums import MessageRole
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from letta.schemas.file import FileAgent, FileAgentBase, FileMetadata, FileMetadataBase
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from letta.schemas.group import Group, GroupCreate
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from letta.schemas.mcp import MCPServer
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from letta.schemas.message import Message, MessageCreate
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from letta.schemas.message import Message, MessageCreate, ToolReturn
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from letta.schemas.source import Source, SourceCreate
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from letta.schemas.tool import Tool
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from letta.schemas.user import User
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@@ -46,6 +48,15 @@ class MessageSchema(MessageCreate):
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role: MessageRole = Field(..., description="The role of the participant.")
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model: Optional[str] = Field(None, description="The model used to make the function call")
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agent_id: Optional[str] = Field(None, description="The unique identifier of the agent")
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tool_calls: Optional[List[OpenAIToolCall]] = Field(
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default=None, description="The list of tool calls requested. Only applicable for role assistant."
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)
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tool_call_id: Optional[str] = Field(default=None, description="The ID of the tool call. Only applicable for role tool.")
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tool_returns: Optional[List[ToolReturn]] = Field(default=None, description="Tool execution return information for prior tool calls")
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created_at: datetime = Field(default_factory=get_utc_time, description="The timestamp when the object was created.")
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# TODO: Should we also duplicate the steps here?
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# TODO: What about tool_return?
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@classmethod
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def from_message(cls, message: Message) -> "MessageSchema":
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@@ -64,6 +75,10 @@ class MessageSchema(MessageCreate):
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group_id=message.group_id,
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model=message.model,
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agent_id=message.agent_id,
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tool_calls=message.tool_calls,
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tool_call_id=message.tool_call_id,
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tool_returns=message.tool_returns,
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created_at=message.created_at,
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)
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@@ -414,6 +414,8 @@ class Message(BaseMessage):
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except json.JSONDecodeError:
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raise ValueError(f"Failed to decode function return: {text_content}")
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# if self.tool_call_id is None:
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# import pdb;pdb.set_trace()
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assert self.tool_call_id is not None
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return ToolReturnMessage(
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@@ -2,7 +2,13 @@ from datetime import datetime, timezone
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from typing import Any, Dict, List, Optional
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from letta.constants import MCP_TOOL_TAG_NAME_PREFIX
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from letta.errors import AgentExportIdMappingError, AgentExportProcessingError, AgentFileImportError, AgentNotFoundForExportError
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from letta.errors import (
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AgentExportIdMappingError,
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AgentExportProcessingError,
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AgentFileExportError,
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AgentFileImportError,
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AgentNotFoundForExportError,
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)
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from letta.helpers.pinecone_utils import should_use_pinecone
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from letta.log import get_logger
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from letta.schemas.agent import AgentState, CreateAgent
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@@ -0,0 +1,623 @@
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{
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"agents": [
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{
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"name": "test_export_import_5297564a-d35a-4b42-9572-1ae313041430",
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"memory_blocks": [],
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"tools": [],
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"tool_ids": [
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"tool-0",
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"tool-1",
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"tool-2",
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"tool-3",
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"tool-4",
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"tool-5",
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"tool-6"
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],
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"source_ids": [],
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"block_ids": [
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"block-0",
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"block-1",
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"block-2"
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],
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"tool_rules": [
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{
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"tool_name": "memory_insert",
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"type": "continue_loop",
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"prompt_template": "<tool_rule>\n{{ tool_name }} requires continuing your response when called\n</tool_rule>"
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},
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{
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"tool_name": "memory_replace",
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"type": "continue_loop",
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"prompt_template": "<tool_rule>\n{{ tool_name }} requires continuing your response when called\n</tool_rule>"
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},
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{
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"tool_name": "send_message",
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"type": "exit_loop",
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"prompt_template": "<tool_rule>\n{{ tool_name }} ends your response (yields control) when called\n</tool_rule>"
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},
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{
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"tool_name": "conversation_search",
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"type": "continue_loop",
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"prompt_template": "<tool_rule>\n{{ tool_name }} requires continuing your response when called\n</tool_rule>"
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}
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],
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"tags": [
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"test",
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"export",
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"import"
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],
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"system": "You are a helpful assistant specializing in data analysis and mathematical computations.",
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"agent_type": "memgpt_v2_agent",
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"llm_config": {
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"model": "gpt-4.1-mini",
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"model_endpoint_type": "openai",
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"model_endpoint": "https://api.openai.com/v1",
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"provider_name": "openai",
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"provider_category": "base",
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"model_wrapper": null,
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"context_window": 32000,
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"put_inner_thoughts_in_kwargs": true,
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"handle": "openai/gpt-4.1-mini",
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"temperature": 0.7,
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"max_tokens": 4096,
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"enable_reasoner": true,
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"reasoning_effort": null,
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"max_reasoning_tokens": 0,
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"frequency_penalty": 1.0,
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"compatibility_type": null,
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"verbosity": "medium"
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},
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"embedding_config": {
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"embedding_endpoint_type": "openai",
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"embedding_endpoint": "https://api.openai.com/v1",
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"embedding_model": "text-embedding-3-small",
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"embedding_dim": 2000,
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"embedding_chunk_size": 300,
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"handle": "openai/text-embedding-3-small",
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"batch_size": 1024,
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"azure_endpoint": null,
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"azure_version": null,
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"azure_deployment": null
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},
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"initial_message_sequence": null,
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"include_base_tools": false,
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"include_multi_agent_tools": false,
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"include_base_tool_rules": false,
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"include_default_source": false,
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"description": null,
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"metadata": null,
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"model": null,
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"embedding": null,
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"context_window_limit": null,
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"embedding_chunk_size": null,
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"max_tokens": null,
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"max_reasoning_tokens": null,
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"enable_reasoner": false,
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"reasoning": null,
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"from_template": null,
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"template": false,
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"project": null,
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"tool_exec_environment_variables": {},
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"memory_variables": null,
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"project_id": null,
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"template_id": null,
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"base_template_id": null,
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"identity_ids": null,
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"message_buffer_autoclear": false,
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"enable_sleeptime": false,
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"response_format": null,
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"timezone": "UTC",
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"max_files_open": 5,
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"per_file_view_window_char_limit": 15000,
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"hidden": null,
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"id": "agent-0",
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"in_context_message_ids": [
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"message-0",
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"message-1",
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"message-2",
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"message-3",
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"message-4",
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"message-5",
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"message-6"
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],
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"messages": [
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{
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"role": "system",
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"content": [
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{
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"type": "text",
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"text": "You are a helpful assistant specializing in data analysis and mathematical computations.\n\n<memory_blocks>\nThe following memory blocks are currently engaged in your core memory unit:\n\n<persona>\n<description>\nThe persona block: Stores details about your current persona, guiding how you behave and respond. This helps you to maintain consistency and personality in your interactions.\n</description>\n<metadata>\n- chars_current=195\n- chars_limit=8000\n</metadata>\n<value>\n# NOTE: Line numbers shown below are to help during editing. Do NOT include line number prefixes in your memory edit tool calls.\nLine 1: You are Alex, a data analyst and mathematician who helps users with calculations and insights. You have extensive experience in statistical analysis and prefer to provide clear, accurate results.\n</value>\n</persona>\n\n<human>\n<description>\nThe human block: Stores key details about the person you are conversing with, allowing for more personalized and friend-like conversation.\n</description>\n<metadata>\n- chars_current=175\n- chars_limit=4000\n</metadata>\n<value>\n# NOTE: Line numbers shown below are to help during editing. Do NOT include line number prefixes in your memory edit tool calls.\nLine 1: username: sarah_researcher\nLine 2: occupation: data scientist\nLine 3: interests: machine learning, statistics, fibonacci sequences\nLine 4: preferred_communication: detailed explanations with examples\n</value>\n</human>\n\n<project_context>\n<description>\nNone\n</description>\n<metadata>\n- chars_current=210\n- chars_limit=6000\n</metadata>\n<value>\n# NOTE: Line numbers shown below are to help during editing. Do NOT include line number prefixes in your memory edit tool calls.\nLine 1: Current project: Building predictive models for financial markets. Sarah is working on sequence analysis and pattern recognition. Recently interested in mathematical sequences like Fibonacci for trend analysis.\n</value>\n</project_context>\n\n</memory_blocks>\n\n<tool_usage_rules>\nThe following constraints define rules for tool usage and guide desired behavior. These rules must be followed to ensure proper tool execution and workflow. A single response may contain multiple tool calls.\n\n<tool_rule>\nmemory_insert requires continuing your response when called\n</tool_rule>\n<tool_rule>\nmemory_replace requires continuing your response when called\n</tool_rule>\n<tool_rule>\nconversation_search requires continuing your response when called\n</tool_rule>\n<tool_rule>\nsend_message ends your response (yields control) when called\n</tool_rule>\n</tool_usage_rules>\n\n\n\n<memory_metadata>\n- The current time is: 2025-08-21 05:49:47 PM UTC+0000\n- Memory blocks were last modified: 2025-08-21 05:49:47 PM UTC+0000\n- -1 previous messages between you and the user are stored in recall memory (use tools to access them)\n- 2 total memories you created are stored in archival memory (use tools to access them)\n</memory_metadata>"
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}
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],
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"name": null,
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"otid": null,
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"sender_id": null,
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"batch_item_id": null,
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"group_id": null,
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"id": "message-0",
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"model": "gpt-4.1-mini",
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"agent_id": "agent-0",
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"tool_calls": null,
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"tool_call_id": null,
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"tool_returns": [],
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"created_at": "2025-08-21T17:49:45.737357+00:00"
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},
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{
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"role": "assistant",
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"content": [
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{
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"type": "text",
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"text": "Bootup sequence complete. Persona activated. Testing messaging functionality."
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}
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],
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"name": null,
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"otid": null,
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"sender_id": null,
|
||||
"batch_item_id": null,
|
||||
"group_id": null,
|
||||
"id": "message-1",
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||||
"model": "gpt-4.1-mini",
|
||||
"agent_id": "agent-0",
|
||||
"tool_calls": [
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||||
{
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||||
"id": "3691c2df-84e5-479c-a871-3d5da7f0a7ee",
|
||||
"function": {
|
||||
"arguments": "{\n \"message\": \"More human than human is our motto.\"\n}",
|
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"name": "send_message"
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||||
},
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"type": "function"
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}
|
||||
],
|
||||
"tool_call_id": null,
|
||||
"tool_returns": [],
|
||||
"created_at": "2025-08-21T17:49:45.739574+00:00"
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": "{\n \"status\": \"OK\",\n \"message\": null,\n \"time\": \"2025-08-21 05:49:45 PM UTC+0000\"\n}"
|
||||
}
|
||||
],
|
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"name": "send_message",
|
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"otid": null,
|
||||
"sender_id": null,
|
||||
"batch_item_id": null,
|
||||
"group_id": null,
|
||||
"id": "message-2",
|
||||
"model": "gpt-4.1-mini",
|
||||
"agent_id": "agent-0",
|
||||
"tool_calls": null,
|
||||
"tool_call_id": "3691c2df-84e5-479c-a871-3d5da7f0a7ee",
|
||||
"tool_returns": [],
|
||||
"created_at": "2025-08-21T17:49:45.739814+00:00"
|
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},
|
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{
|
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"role": "user",
|
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"content": [
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{
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"type": "text",
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"text": "{\n \"type\": \"login\",\n \"last_login\": \"Never (first login)\",\n \"time\": \"2025-08-21 05:49:45 PM UTC+0000\"\n}"
|
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}
|
||||
],
|
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"name": null,
|
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"otid": null,
|
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"sender_id": null,
|
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"batch_item_id": null,
|
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"group_id": null,
|
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"id": "message-3",
|
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"model": "gpt-4.1-mini",
|
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"agent_id": "agent-0",
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"tool_calls": null,
|
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"tool_call_id": null,
|
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"tool_returns": [],
|
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"created_at": "2025-08-21T17:49:45.739827+00:00"
|
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},
|
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{
|
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "Test message for export"
|
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}
|
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],
|
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"name": null,
|
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"otid": null,
|
||||
"sender_id": null,
|
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"batch_item_id": null,
|
||||
"group_id": null,
|
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"id": "message-4",
|
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"model": null,
|
||||
"agent_id": "agent-0",
|
||||
"tool_calls": null,
|
||||
"tool_call_id": null,
|
||||
"tool_returns": [],
|
||||
"created_at": "2025-08-21T17:49:46.969096+00:00"
|
||||
},
|
||||
{
|
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"role": "assistant",
|
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"content": [
|
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{
|
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"type": "text",
|
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"text": "Responding to test message for export."
|
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}
|
||||
],
|
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"name": null,
|
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"otid": null,
|
||||
"sender_id": null,
|
||||
"batch_item_id": null,
|
||||
"group_id": null,
|
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"id": "message-5",
|
||||
"model": "gpt-4.1-mini",
|
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"agent_id": "agent-0",
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "call_CR0Pvrt3B7fUk4vz3b1MDQdR",
|
||||
"function": {
|
||||
"arguments": "{\"message\": \"Test message received successfully. Let me know how I can assist you further!\", \"request_heartbeat\": false}",
|
||||
"name": "send_message"
|
||||
},
|
||||
"type": "function"
|
||||
}
|
||||
],
|
||||
"tool_call_id": "call_CR0Pvrt3B7fUk4vz3b1MDQdR",
|
||||
"tool_returns": [],
|
||||
"created_at": "2025-08-21T17:49:47.926707+00:00"
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": "{\n \"status\": \"OK\",\n \"message\": \"Sent message successfully.\",\n \"time\": \"2025-08-21 05:49:47 PM UTC+0000\"\n}"
|
||||
}
|
||||
],
|
||||
"name": "send_message",
|
||||
"otid": null,
|
||||
"sender_id": null,
|
||||
"batch_item_id": null,
|
||||
"group_id": null,
|
||||
"id": "message-6",
|
||||
"model": "gpt-4.1-mini",
|
||||
"agent_id": "agent-0",
|
||||
"tool_calls": null,
|
||||
"tool_call_id": "call_CR0Pvrt3B7fUk4vz3b1MDQdR",
|
||||
"tool_returns": [
|
||||
{
|
||||
"status": "success",
|
||||
"stdout": null,
|
||||
"stderr": null
|
||||
}
|
||||
],
|
||||
"created_at": "2025-08-21T17:49:47.926951+00:00"
|
||||
}
|
||||
],
|
||||
"files_agents": [],
|
||||
"group_ids": []
|
||||
}
|
||||
],
|
||||
"groups": [],
|
||||
"blocks": [
|
||||
{
|
||||
"value": "username: sarah_researcher\noccupation: data scientist\ninterests: machine learning, statistics, fibonacci sequences\npreferred_communication: detailed explanations with examples",
|
||||
"limit": 4000,
|
||||
"project_id": null,
|
||||
"template_name": null,
|
||||
"is_template": false,
|
||||
"preserve_on_migration": false,
|
||||
"label": "human",
|
||||
"read_only": false,
|
||||
"description": "The human block: Stores key details about the person you are conversing with, allowing for more personalized and friend-like conversation.",
|
||||
"metadata": {},
|
||||
"id": "block-1"
|
||||
},
|
||||
{
|
||||
"value": "You are Alex, a data analyst and mathematician who helps users with calculations and insights. You have extensive experience in statistical analysis and prefer to provide clear, accurate results.",
|
||||
"limit": 8000,
|
||||
"project_id": null,
|
||||
"template_name": null,
|
||||
"is_template": false,
|
||||
"preserve_on_migration": false,
|
||||
"label": "persona",
|
||||
"read_only": false,
|
||||
"description": "The persona block: Stores details about your current persona, guiding how you behave and respond. This helps you to maintain consistency and personality in your interactions.",
|
||||
"metadata": {},
|
||||
"id": "block-0"
|
||||
},
|
||||
{
|
||||
"value": "Current project: Building predictive models for financial markets. Sarah is working on sequence analysis and pattern recognition. Recently interested in mathematical sequences like Fibonacci for trend analysis.",
|
||||
"limit": 6000,
|
||||
"project_id": null,
|
||||
"template_name": null,
|
||||
"is_template": false,
|
||||
"preserve_on_migration": false,
|
||||
"label": "project_context",
|
||||
"read_only": false,
|
||||
"description": null,
|
||||
"metadata": {},
|
||||
"id": "block-2"
|
||||
}
|
||||
],
|
||||
"files": [],
|
||||
"sources": [],
|
||||
"tools": [
|
||||
{
|
||||
"id": "tool-2",
|
||||
"tool_type": "custom",
|
||||
"description": "Analyze data and provide insights.",
|
||||
"source_type": "python",
|
||||
"name": "analyze_data",
|
||||
"tags": [
|
||||
"analysis",
|
||||
"data"
|
||||
],
|
||||
"source_code": "def analyze_data(data_type: str, values: List[float]) -> str:\n \"\"\"Analyze data and provide insights.\n\n Args:\n data_type: Type of data to analyze.\n values: Numerical values to analyze.\n\n Returns:\n Analysis results including average, max, and min values.\n \"\"\"\n if not values:\n return \"No data provided\"\n avg = sum(values) / len(values)\n max_val = max(values)\n min_val = min(values)\n return f\"Analysis of {data_type}: avg={avg:.2f}, max={max_val}, min={min_val}\"\n",
|
||||
"json_schema": {
|
||||
"name": "analyze_data",
|
||||
"description": "Analyze data and provide insights.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"data_type": {
|
||||
"type": "string",
|
||||
"description": "Type of data to analyze."
|
||||
},
|
||||
"values": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "number"
|
||||
},
|
||||
"description": "Numerical values to analyze."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"data_type",
|
||||
"values"
|
||||
]
|
||||
}
|
||||
},
|
||||
"args_json_schema": null,
|
||||
"return_char_limit": 50000,
|
||||
"pip_requirements": null,
|
||||
"npm_requirements": null,
|
||||
"created_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"last_updated_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"metadata_": {}
|
||||
},
|
||||
{
|
||||
"id": "tool-5",
|
||||
"tool_type": "custom",
|
||||
"description": "Calculate the nth Fibonacci number.",
|
||||
"source_type": "python",
|
||||
"name": "calculate_fibonacci",
|
||||
"tags": [
|
||||
"math",
|
||||
"utility"
|
||||
],
|
||||
"source_code": "def calculate_fibonacci(n: int) -> int:\n \"\"\"Calculate the nth Fibonacci number.\n\n Args:\n n: The position in the Fibonacci sequence to calculate.\n\n Returns:\n The nth Fibonacci number.\n \"\"\"\n if n <= 0:\n return 0\n elif n == 1:\n return 1\n else:\n a, b = 0, 1\n for _ in range(2, n + 1):\n a, b = b, a + b\n return b\n",
|
||||
"json_schema": {
|
||||
"name": "calculate_fibonacci",
|
||||
"description": "Calculate the nth Fibonacci number.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"n": {
|
||||
"type": "integer",
|
||||
"description": "The position in the Fibonacci sequence to calculate."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"n"
|
||||
]
|
||||
}
|
||||
},
|
||||
"args_json_schema": null,
|
||||
"return_char_limit": 50000,
|
||||
"pip_requirements": null,
|
||||
"npm_requirements": null,
|
||||
"created_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"last_updated_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"metadata_": {}
|
||||
},
|
||||
{
|
||||
"id": "tool-4",
|
||||
"tool_type": "letta_core",
|
||||
"description": "Search prior conversation history using case-insensitive string matching.",
|
||||
"source_type": "python",
|
||||
"name": "conversation_search",
|
||||
"tags": [
|
||||
"letta_core"
|
||||
],
|
||||
"source_code": null,
|
||||
"json_schema": {
|
||||
"name": "conversation_search",
|
||||
"description": "Search prior conversation history using case-insensitive string matching.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "String to search for."
|
||||
},
|
||||
"page": {
|
||||
"type": "integer",
|
||||
"description": "Allows you to page through results. Only use on a follow-up query. Defaults to 0 (first page)."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"query"
|
||||
]
|
||||
}
|
||||
},
|
||||
"args_json_schema": null,
|
||||
"return_char_limit": 1000000,
|
||||
"pip_requirements": null,
|
||||
"npm_requirements": null,
|
||||
"created_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"last_updated_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"metadata_": {}
|
||||
},
|
||||
{
|
||||
"id": "tool-0",
|
||||
"tool_type": "custom",
|
||||
"description": "Get user preferences for a specific category.",
|
||||
"source_type": "python",
|
||||
"name": "get_user_preferences",
|
||||
"tags": [
|
||||
"user",
|
||||
"preferences"
|
||||
],
|
||||
"source_code": "def get_user_preferences(category: str) -> str:\n \"\"\"Get user preferences for a specific category.\n\n Args:\n category: The preference category to retrieve (notification, theme, language).\n\n Returns:\n The user's preference for the specified category, or \"not specified\" if unknown.\n \"\"\"\n preferences = {\"notification\": \"email only\", \"theme\": \"dark mode\", \"language\": \"english\"}\n return preferences.get(category, \"not specified\")\n",
|
||||
"json_schema": {
|
||||
"name": "get_user_preferences",
|
||||
"description": "Get user preferences for a specific category.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"category": {
|
||||
"type": "string",
|
||||
"description": "The preference category to retrieve (notification, theme, language)."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"category"
|
||||
]
|
||||
}
|
||||
},
|
||||
"args_json_schema": null,
|
||||
"return_char_limit": 50000,
|
||||
"pip_requirements": null,
|
||||
"npm_requirements": null,
|
||||
"created_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"last_updated_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"metadata_": {}
|
||||
},
|
||||
{
|
||||
"id": "tool-6",
|
||||
"tool_type": "letta_sleeptime_core",
|
||||
"description": "The memory_insert command allows you to insert text at a specific location in a memory block.",
|
||||
"source_type": "python",
|
||||
"name": "memory_insert",
|
||||
"tags": [
|
||||
"letta_sleeptime_core"
|
||||
],
|
||||
"source_code": null,
|
||||
"json_schema": {
|
||||
"name": "memory_insert",
|
||||
"description": "The memory_insert command allows you to insert text at a specific location in a memory block.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"label": {
|
||||
"type": "string",
|
||||
"description": "Section of the memory to be edited, identified by its label."
|
||||
},
|
||||
"new_str": {
|
||||
"type": "string",
|
||||
"description": "The text to insert. Do not include line number prefixes."
|
||||
},
|
||||
"insert_line": {
|
||||
"type": "integer",
|
||||
"description": "The line number after which to insert the text (0 for beginning of file). Defaults to -1 (end of the file)."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"label",
|
||||
"new_str"
|
||||
]
|
||||
}
|
||||
},
|
||||
"args_json_schema": null,
|
||||
"return_char_limit": 1000000,
|
||||
"pip_requirements": null,
|
||||
"npm_requirements": null,
|
||||
"created_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"last_updated_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"metadata_": {}
|
||||
},
|
||||
{
|
||||
"id": "tool-3",
|
||||
"tool_type": "letta_sleeptime_core",
|
||||
"description": "The memory_replace command allows you to replace a specific string in a memory block with a new string. This is used for making precise edits.",
|
||||
"source_type": "python",
|
||||
"name": "memory_replace",
|
||||
"tags": [
|
||||
"letta_sleeptime_core"
|
||||
],
|
||||
"source_code": null,
|
||||
"json_schema": {
|
||||
"name": "memory_replace",
|
||||
"description": "The memory_replace command allows you to replace a specific string in a memory block with a new string. This is used for making precise edits.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"label": {
|
||||
"type": "string",
|
||||
"description": "Section of the memory to be edited, identified by its label."
|
||||
},
|
||||
"old_str": {
|
||||
"type": "string",
|
||||
"description": "The text to replace (must match exactly, including whitespace and indentation)."
|
||||
},
|
||||
"new_str": {
|
||||
"type": "string",
|
||||
"description": "The new text to insert in place of the old text. Do not include line number prefixes."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"label",
|
||||
"old_str",
|
||||
"new_str"
|
||||
]
|
||||
}
|
||||
},
|
||||
"args_json_schema": null,
|
||||
"return_char_limit": 1000000,
|
||||
"pip_requirements": null,
|
||||
"npm_requirements": null,
|
||||
"created_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"last_updated_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"metadata_": {}
|
||||
},
|
||||
{
|
||||
"id": "tool-1",
|
||||
"tool_type": "letta_core",
|
||||
"description": "Sends a message to the human user.",
|
||||
"source_type": "python",
|
||||
"name": "send_message",
|
||||
"tags": [
|
||||
"letta_core"
|
||||
],
|
||||
"source_code": null,
|
||||
"json_schema": {
|
||||
"name": "send_message",
|
||||
"description": "Sends a message to the human user.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"message": {
|
||||
"type": "string",
|
||||
"description": "Message contents. All unicode (including emojis) are supported."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"message"
|
||||
]
|
||||
}
|
||||
},
|
||||
"args_json_schema": null,
|
||||
"return_char_limit": 1000000,
|
||||
"pip_requirements": null,
|
||||
"npm_requirements": null,
|
||||
"created_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"last_updated_by_id": "user-00000000-0000-4000-8000-000000000000",
|
||||
"metadata_": {}
|
||||
}
|
||||
],
|
||||
"mcp_servers": [],
|
||||
"metadata": {
|
||||
"revision_id": "ffb17eb241fc"
|
||||
},
|
||||
"created_at": "2025-08-21T17:49:48.078363+00:00"
|
||||
}
|
||||
@@ -1,5 +1,7 @@
|
||||
import io
|
||||
import json
|
||||
import os
|
||||
import textwrap
|
||||
import threading
|
||||
import time
|
||||
import uuid
|
||||
@@ -60,6 +62,117 @@ def agent(client: LettaSDKClient):
|
||||
client.agents.delete(agent_id=agent_state.id)
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def fibonacci_tool(client: LettaSDKClient):
|
||||
"""Fixture providing Fibonacci calculation tool."""
|
||||
|
||||
def calculate_fibonacci(n: int) -> int:
|
||||
"""Calculate the nth Fibonacci number.
|
||||
|
||||
Args:
|
||||
n: The position in the Fibonacci sequence to calculate.
|
||||
|
||||
Returns:
|
||||
The nth Fibonacci number.
|
||||
"""
|
||||
if n <= 0:
|
||||
return 0
|
||||
elif n == 1:
|
||||
return 1
|
||||
else:
|
||||
a, b = 0, 1
|
||||
for _ in range(2, n + 1):
|
||||
a, b = b, a + b
|
||||
return b
|
||||
|
||||
tool = client.tools.upsert_from_function(func=calculate_fibonacci, tags=["math", "utility"])
|
||||
yield tool
|
||||
client.tools.delete(tool.id)
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def preferences_tool(client: LettaSDKClient):
|
||||
"""Fixture providing user preferences tool."""
|
||||
|
||||
def get_user_preferences(category: str) -> str:
|
||||
"""Get user preferences for a specific category.
|
||||
|
||||
Args:
|
||||
category: The preference category to retrieve (notification, theme, language).
|
||||
|
||||
Returns:
|
||||
The user's preference for the specified category, or "not specified" if unknown.
|
||||
"""
|
||||
preferences = {"notification": "email only", "theme": "dark mode", "language": "english"}
|
||||
return preferences.get(category, "not specified")
|
||||
|
||||
tool = client.tools.upsert_from_function(func=get_user_preferences, tags=["user", "preferences"])
|
||||
yield tool
|
||||
client.tools.delete(tool.id)
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def data_analysis_tool(client: LettaSDKClient):
|
||||
"""Fixture providing data analysis tool."""
|
||||
|
||||
def analyze_data(data_type: str, values: List[float]) -> str:
|
||||
"""Analyze data and provide insights.
|
||||
|
||||
Args:
|
||||
data_type: Type of data to analyze.
|
||||
values: Numerical values to analyze.
|
||||
|
||||
Returns:
|
||||
Analysis results including average, max, and min values.
|
||||
"""
|
||||
if not values:
|
||||
return "No data provided"
|
||||
avg = sum(values) / len(values)
|
||||
max_val = max(values)
|
||||
min_val = min(values)
|
||||
return f"Analysis of {data_type}: avg={avg:.2f}, max={max_val}, min={min_val}"
|
||||
|
||||
tool = client.tools.upsert_from_function(func=analyze_data, tags=["analysis", "data"])
|
||||
yield tool
|
||||
client.tools.delete(tool.id)
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def persona_block(client: LettaSDKClient):
|
||||
"""Fixture providing persona memory block."""
|
||||
block = client.blocks.create(
|
||||
label="persona",
|
||||
value="You are Alex, a data analyst and mathematician who helps users with calculations and insights. You have extensive experience in statistical analysis and prefer to provide clear, accurate results.",
|
||||
limit=8000,
|
||||
)
|
||||
yield block
|
||||
client.blocks.delete(block.id)
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def human_block(client: LettaSDKClient):
|
||||
"""Fixture providing human memory block."""
|
||||
block = client.blocks.create(
|
||||
label="human",
|
||||
value="username: sarah_researcher\noccupation: data scientist\ninterests: machine learning, statistics, fibonacci sequences\npreferred_communication: detailed explanations with examples",
|
||||
limit=4000,
|
||||
)
|
||||
yield block
|
||||
client.blocks.delete(block.id)
|
||||
|
||||
|
||||
@pytest.fixture(scope="function")
|
||||
def context_block(client: LettaSDKClient):
|
||||
"""Fixture providing project context memory block."""
|
||||
block = client.blocks.create(
|
||||
label="project_context",
|
||||
value="Current project: Building predictive models for financial markets. Sarah is working on sequence analysis and pattern recognition. Recently interested in mathematical sequences like Fibonacci for trend analysis.",
|
||||
limit=6000,
|
||||
)
|
||||
yield block
|
||||
client.blocks.delete(block.id)
|
||||
|
||||
|
||||
def test_shared_blocks(client: LettaSDKClient):
|
||||
# create a block
|
||||
block = client.blocks.create(
|
||||
@@ -1465,7 +1578,6 @@ def test_tool_name_auto_update_with_multiple_functions(client: LettaSDKClient):
|
||||
|
||||
def test_tool_rename_with_json_schema_and_source_code(client: LettaSDKClient):
|
||||
"""Test that passing both new JSON schema AND source code still renames the tool based on source code"""
|
||||
import textwrap
|
||||
|
||||
# Create initial tool
|
||||
def initial_tool(x: int) -> int:
|
||||
@@ -1543,3 +1655,217 @@ def test_tool_rename_with_json_schema_and_source_code(client: LettaSDKClient):
|
||||
finally:
|
||||
# Clean up
|
||||
client.tools.delete(tool_id=tool.id)
|
||||
|
||||
|
||||
def test_import_agent_file_from_disk(
|
||||
client: LettaSDKClient, fibonacci_tool, preferences_tool, data_analysis_tool, persona_block, human_block, context_block
|
||||
):
|
||||
"""Test exporting an agent to file and importing it back from disk."""
|
||||
# Create a comprehensive agent (similar to test_agent_serialization_v2)
|
||||
name = f"test_export_import_{str(uuid.uuid4())}"
|
||||
temp_agent = client.agents.create(
|
||||
name=name,
|
||||
memory_blocks=[persona_block, human_block, context_block],
|
||||
model="openai/gpt-4.1-mini",
|
||||
embedding="openai/text-embedding-3-small",
|
||||
tool_ids=[fibonacci_tool.id, preferences_tool.id, data_analysis_tool.id],
|
||||
include_base_tools=True,
|
||||
tags=["test", "export", "import"],
|
||||
system="You are a helpful assistant specializing in data analysis and mathematical computations.",
|
||||
)
|
||||
|
||||
# Add archival memory
|
||||
archival_passages = ["Test archival passage for export/import testing.", "Another passage with data about testing procedures."]
|
||||
|
||||
for passage_text in archival_passages:
|
||||
client.agents.passages.create(agent_id=temp_agent.id, text=passage_text)
|
||||
|
||||
# Send a test message
|
||||
client.agents.messages.create(
|
||||
agent_id=temp_agent.id,
|
||||
messages=[
|
||||
MessageCreate(
|
||||
role="user",
|
||||
content="Test message for export",
|
||||
),
|
||||
],
|
||||
)
|
||||
|
||||
# Export the agent
|
||||
serialized_v2 = client.agents.export_file(agent_id=temp_agent.id, use_legacy_format=False)
|
||||
|
||||
# Save to file
|
||||
file_path = os.path.join(os.path.dirname(__file__), "test_agent_files", "test_basic_agent_with_blocks_tools_messages_v2.af")
|
||||
os.makedirs(os.path.dirname(file_path), exist_ok=True)
|
||||
|
||||
with open(file_path, "w") as f:
|
||||
json.dump(serialized_v2, f, indent=2)
|
||||
|
||||
# Now import from the file
|
||||
with open(file_path, "rb") as f:
|
||||
import_result = client.agents.import_file(
|
||||
file=f, append_copy_suffix=True, override_existing_tools=True # Use suffix to avoid name conflict
|
||||
)
|
||||
|
||||
# Basic verification
|
||||
assert import_result is not None, "Import result should not be None"
|
||||
assert len(import_result.agent_ids) > 0, "Should have imported at least one agent"
|
||||
|
||||
# Get the imported agent
|
||||
imported_agent_id = import_result.agent_ids[0]
|
||||
imported_agent = client.agents.retrieve(agent_id=imported_agent_id)
|
||||
|
||||
# Basic checks
|
||||
assert imported_agent is not None, "Should be able to retrieve imported agent"
|
||||
assert imported_agent.name is not None, "Imported agent should have a name"
|
||||
assert imported_agent.memory is not None, "Agent should have memory"
|
||||
assert len(imported_agent.tools) > 0, "Agent should have tools"
|
||||
assert imported_agent.system is not None, "Agent should have a system prompt"
|
||||
|
||||
|
||||
def test_agent_serialization_v2(
|
||||
client: LettaSDKClient, fibonacci_tool, preferences_tool, data_analysis_tool, persona_block, human_block, context_block
|
||||
):
|
||||
"""Test agent serialization with comprehensive setup including custom tools, blocks, messages, and archival memory."""
|
||||
name = f"comprehensive_test_agent_{str(uuid.uuid4())}"
|
||||
temp_agent = client.agents.create(
|
||||
name=name,
|
||||
memory_blocks=[persona_block, human_block, context_block],
|
||||
model="openai/gpt-4.1-mini",
|
||||
embedding="openai/text-embedding-3-small",
|
||||
tool_ids=[fibonacci_tool.id, preferences_tool.id, data_analysis_tool.id],
|
||||
include_base_tools=True,
|
||||
tags=["test", "comprehensive", "serialization"],
|
||||
system="You are a helpful assistant specializing in data analysis and mathematical computations.",
|
||||
)
|
||||
|
||||
# Add archival memory
|
||||
archival_passages = [
|
||||
"Project background: Sarah is working on a financial prediction model that uses Fibonacci retracements for technical analysis.",
|
||||
"Research notes: Golden ratio (1.618) derived from Fibonacci sequence is often used in financial markets for support/resistance levels.",
|
||||
]
|
||||
|
||||
for passage_text in archival_passages:
|
||||
client.agents.passages.create(agent_id=temp_agent.id, text=passage_text)
|
||||
|
||||
# Send some messages
|
||||
client.agents.messages.create(
|
||||
agent_id=temp_agent.id,
|
||||
messages=[
|
||||
MessageCreate(
|
||||
role="user",
|
||||
content="Test message",
|
||||
),
|
||||
],
|
||||
)
|
||||
|
||||
# Serialize using v2
|
||||
serialized_v2 = client.agents.export_file(agent_id=temp_agent.id, use_legacy_format=False)
|
||||
# Convert dict to JSON bytes for import
|
||||
json_str = json.dumps(serialized_v2)
|
||||
file_obj = io.BytesIO(json_str.encode("utf-8"))
|
||||
|
||||
# Import again
|
||||
import_result = client.agents.import_file(file=file_obj, append_copy_suffix=False, override_existing_tools=True)
|
||||
|
||||
# Verify import was successful
|
||||
assert len(import_result.agent_ids) == 1, "Should have imported exactly one agent"
|
||||
imported_agent_id = import_result.agent_ids[0]
|
||||
imported_agent = client.agents.retrieve(agent_id=imported_agent_id)
|
||||
|
||||
# ========== BASIC AGENT PROPERTIES ==========
|
||||
# Name should be the same (if append_copy_suffix=False) or have suffix
|
||||
assert imported_agent.name == name, f"Agent name mismatch: {imported_agent.name} != {name}"
|
||||
|
||||
# LLM and embedding configs should be preserved
|
||||
assert (
|
||||
imported_agent.llm_config.model == temp_agent.llm_config.model
|
||||
), f"LLM model mismatch: {imported_agent.llm_config.model} != {temp_agent.llm_config.model}"
|
||||
assert imported_agent.embedding_config.embedding_model == temp_agent.embedding_config.embedding_model, "Embedding model mismatch"
|
||||
|
||||
# System prompt should be preserved
|
||||
assert imported_agent.system == temp_agent.system, "System prompt was not preserved"
|
||||
|
||||
# Tags should be preserved
|
||||
assert set(imported_agent.tags) == set(temp_agent.tags), f"Tags mismatch: {imported_agent.tags} != {temp_agent.tags}"
|
||||
|
||||
# Agent type should be preserved
|
||||
assert (
|
||||
imported_agent.agent_type == temp_agent.agent_type
|
||||
), f"Agent type mismatch: {imported_agent.agent_type} != {temp_agent.agent_type}"
|
||||
|
||||
# ========== MEMORY BLOCKS ==========
|
||||
# Compare memory blocks directly from AgentState objects
|
||||
original_blocks = temp_agent.memory.blocks
|
||||
imported_blocks = imported_agent.memory.blocks
|
||||
|
||||
# Should have same number of blocks
|
||||
assert len(imported_blocks) == len(original_blocks), f"Block count mismatch: {len(imported_blocks)} != {len(original_blocks)}"
|
||||
|
||||
# Verify each block by label
|
||||
original_blocks_by_label = {block.label: block for block in original_blocks}
|
||||
imported_blocks_by_label = {block.label: block for block in imported_blocks}
|
||||
|
||||
# Check persona block
|
||||
assert "persona" in imported_blocks_by_label, "Persona block missing in imported agent"
|
||||
assert "Alex" in imported_blocks_by_label["persona"].value, "Persona block content not preserved"
|
||||
assert imported_blocks_by_label["persona"].limit == original_blocks_by_label["persona"].limit, "Persona block limit mismatch"
|
||||
|
||||
# Check human block
|
||||
assert "human" in imported_blocks_by_label, "Human block missing in imported agent"
|
||||
assert "sarah_researcher" in imported_blocks_by_label["human"].value, "Human block content not preserved"
|
||||
assert imported_blocks_by_label["human"].limit == original_blocks_by_label["human"].limit, "Human block limit mismatch"
|
||||
|
||||
# Check context block
|
||||
assert "project_context" in imported_blocks_by_label, "Context block missing in imported agent"
|
||||
assert "financial markets" in imported_blocks_by_label["project_context"].value, "Context block content not preserved"
|
||||
assert (
|
||||
imported_blocks_by_label["project_context"].limit == original_blocks_by_label["project_context"].limit
|
||||
), "Context block limit mismatch"
|
||||
|
||||
# ========== TOOLS ==========
|
||||
# Compare tools directly from AgentState objects
|
||||
original_tools = temp_agent.tools
|
||||
imported_tools = imported_agent.tools
|
||||
|
||||
# Should have same number of tools
|
||||
assert len(imported_tools) == len(original_tools), f"Tool count mismatch: {len(imported_tools)} != {len(original_tools)}"
|
||||
|
||||
original_tool_names = {tool.name for tool in original_tools}
|
||||
imported_tool_names = {tool.name for tool in imported_tools}
|
||||
|
||||
# Check custom tools are present
|
||||
assert "calculate_fibonacci" in imported_tool_names, "Fibonacci tool missing in imported agent"
|
||||
assert "get_user_preferences" in imported_tool_names, "Preferences tool missing in imported agent"
|
||||
assert "analyze_data" in imported_tool_names, "Data analysis tool missing in imported agent"
|
||||
|
||||
# Check for base tools (since we set include_base_tools=True when creating the agent)
|
||||
# Base tools should also be present (at least some core ones)
|
||||
base_tool_names = {"send_message", "conversation_search"}
|
||||
missing_base_tools = base_tool_names - imported_tool_names
|
||||
assert len(missing_base_tools) == 0, f"Missing base tools: {missing_base_tools}"
|
||||
|
||||
# Verify tool names match exactly
|
||||
assert original_tool_names == imported_tool_names, f"Tool names don't match: {original_tool_names} != {imported_tool_names}"
|
||||
|
||||
# ========== MESSAGE HISTORY ==========
|
||||
# Get messages for both agents
|
||||
original_messages = client.agents.messages.list(agent_id=temp_agent.id, limit=100)
|
||||
imported_messages = client.agents.messages.list(agent_id=imported_agent_id, limit=100)
|
||||
|
||||
# Should have same number of messages
|
||||
assert len(imported_messages) >= 1, "Imported agent should have messages"
|
||||
|
||||
# Filter for user messages (excluding system-generated login messages)
|
||||
original_user_msgs = [msg for msg in original_messages if msg.message_type == "user_message" and "Test message" in msg.content]
|
||||
imported_user_msgs = [msg for msg in imported_messages if msg.message_type == "user_message" and "Test message" in msg.content]
|
||||
|
||||
# Should have the same number of test messages
|
||||
assert len(imported_user_msgs) == len(
|
||||
original_user_msgs
|
||||
), f"User message count mismatch: {len(imported_user_msgs)} != {len(original_user_msgs)}"
|
||||
|
||||
# Verify test message content is preserved
|
||||
if len(original_user_msgs) > 0 and len(imported_user_msgs) > 0:
|
||||
assert imported_user_msgs[0].content == original_user_msgs[0].content, "User message content not preserved"
|
||||
assert "Test message" in imported_user_msgs[0].content, "Test message content not found"
|
||||
|
||||
Reference in New Issue
Block a user