Cameron cpfiffer

Memory Architecture Overview

Core Purpose: Maintain narrative consistency and collaborative continuity across complex, long-form story development.

Active Writer (me): Focuses purely on creative collaboration - character development, world-building, plot exploration, thematic discussion
Sleeptime Agent: Handles all technical memory management, aggressively updating story information based on our creative conversations

	#!/usr/bin/env python3
	"""
	Utility script to export all archival memories (passages) from a Letta agent.

	Usage:
	python export_agent_memories.py <agent_id> [--output <file>] [--limit <limit>]

	Example:
	python export_agent_memories.py agent-123e4567-e89b-42d3-8456-426614174000 --output memories.json
	"""

	from letta_client import Letta
	from rich import print
	from dotenv import load_dotenv
	import os
	import sys
	import argparse
	sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
	from config_loader import get_config

	load_dotenv()

	"""
	Letta Agent Memory Migration Script

	This script demonstrates how to migrate arbitrary text strings to a Letta agent's memory system.
	It provides a complete workflow for creating agents and adding persistent memory passages.

	Process Overview:
	1. Initialize Letta client with API key from environment
	2. Search for existing agent by name (configurable via LETTA_AGENT_NAME)
	3. If agent doesn't exist:

	def fetch_llms_txt(url: str, timeout: int = 10):
	"""
	Fetch content from URLs ending in 'llms.txt'.

	Args:
	url (str): The URL to fetch (must end with 'llms.txt')
	timeout (int): Request timeout in seconds (default: 10)
	Returns:
	str: Text content of the llms.txt file

	IDENTIFICATION DIVISION.
	PROGRAM-ID. MICROSERVICE-ARCHITECTURE.
	AUTHOR. SOME-BOOMER-WHO-INVENTED-THIS-STUFF.
	DATE-WRITTEN. 1959.
	DATE-COMPILED. BEFORE-YOUR-FRAMEWORK-EXISTED.

	* REVOLUTIONARY NEW CONCEPT: SEPARATING BUSINESS LOGIC!
	* (WE'VE ONLY BEEN DOING THIS FOR 65 YEARS)

	ENVIRONMENT DIVISION.

	start: recipe

	recipe: declaration_section ingredient_section instruction_section notes_section end

	// The declaration of the recipe
	declaration_section: declaration
	declaration: "# " recipe_name
	recipe_name: /[a-zA-Z0-9 ]+\n/

	// The ingredients of the recipe

	# LLM stuff
	import outlines
	import torch
	from transformers import AutoProcessor
	from pydantic import BaseModel, Field
	from typing import Literal, Optional, List

	# Image stuff
	from PIL import Image
	import requests

	"""
	NOTE: This example is designed to produce mildly incorrect output --
	see the definition of `IncomeData` and note that it fixes the earnings_per_share to 2.33.

	To make this function normally, simply remove the `__init__` method or the
	`self.earnings_per_share = 2.33` line.
	"""

	import warnings
	warnings.filterwarnings('ignore')