Building Persistent Memory for AI Agents: A 4-Layer File-Based Architecture

Building AI agents that remember interactions across sessions is one of the most challenging aspects of LLM-based automation. Without persistent memory, every conversation starts from scratch, making agents feel forgetful and limiting their usefulness in real-world workflows.

After experimenting with various memory architectures (vector databases, Redis, SQL), I settled on a 4-layer file-based memory system that works seamlessly with ChatGPT, Claude, local LLMs, and even experimental frameworks like Agent Zero. This architecture gives agents true persistence without complex infrastructure.

The Problem: Stateless AI Agents

Most AI agent implementations today are stateless. When you call an LLM API, you get a response, but that context evaporates unless you explicitly pass it to the next call. This creates several problems:

• Repetitive work: The agent can't recall previous decisions
• Inconsistent behavior: Different responses to the same input
• Poor user experience: Feels like talking to a new person each time

I needed a solution that:

• Works offline (no cloud dependency)
• Is simple to implement
• Scales with the agent's complexity
• Maintains data integrity

The 4-Layer Memory Architecture

My solution organizes memory across four distinct layers, each serving a specific purpose:

agent_memory/
├── 1_short_term/       # Current session context
├── 2_working_memory/   # Active task state
├── 3_long_term/        # Persistent knowledge
│   ├── facts/
│   └── skills/
└── 4_metadata/         # Memory management

Let me explain each layer and how they work together.

Layer 1: Short-Term Memory (Current Session)

This is the agent's "working memory" for the current interaction. It stores:

• Last few user messages
• Agent's immediate responses
• Current conversation state

# Example short-term memory structure
short_term = {
    "session_id": "abc123",
    "timestamp": "2023-11-15T14:30:00",
    "messages": [
        {"role": "user", "content": "Write a blog post about AI memory"},
        {"role": "assistant", "content": "Here's an outline..."}
    ],
    "current_task": "drafting_blog_post"
}

Implementation tip: Limit this to 10-20 messages to prevent context overflow. Older messages get archived to long-term memory.

Layer 2: Working Memory (Active Tasks)

This layer tracks the agent's active processes and decisions. Think of it as the agent's "to-do list" and "decision log".

json
// Example working memory
{
  "active_tasks": [
    {
      "task_id": "task_456",
      "description": "Research AI memory architectures",
      "status": "in_progress",
      "created_at": "2023-11-15T14:35:00",
      "updated_at": "2023-11-15T14:40:00",
      "dependencies": ["task_123"],
      "output": {
        "notes": "Found 4 main approaches...",
        "references": ["paper1.pdf", "blog_post1.md"]
      }
    }
  ],
  "decisions": [
    {
      "decision_id": "dec_789",
      "question": "Should we use vector DB