AI Memory System: Building Personal AI Workflow Automation

Project Type: Personal Infrastructure | AI Implementation
Status: Active (2023-Present)
Tech Stack: JSONL, MCP, Cross-platform (Claude, ChatGPT, OpenWebUI/Ollama)

The Problem: Context Loss

If you use AI regularly, you've hit this wall: every new conversation starts fresh.

You spend 30 minutes explaining your project's architecture to ChatGPT. It gives brilliant insights. Next day, you start a new chat... and it has no memory of yesterday's conversation. You're back to square one.

The frustration:

• Re-explaining the same context across multiple sessions
• Losing valuable insights buried in old chat histories
• Manually copy-pasting previous conversations
• Different AI platforms with zero shared context
• Projects spanning weeks where context evaporates

The real cost: Not just time—it's the cognitive overhead of being the "system memory" for AI conversations.

The Solution: AI Memory Ledger

The AI Memory System is a personal knowledge management infrastructure that maintains context across AI conversations and platforms.

How It Works

Core Concept: A single memory.jsonl file acts as a "ledger" of project context.

Entry Structure:

{
  "id": "mem-2024-03-15-001",
  "timestamp": "2024-03-15T10:30:00-07:00",
  "projects": ["MikeCareer", "VelocityPartners"],
  "author": "claude",
  "type": "decision",
  "summary": "Chose JSONL over database for memory system",
  "details": "JSONL provides cross-AI compatibility...",
  "tags": ["architecture", "technical_decision"]
}

Entry Types: decision, milestone, insight, resource, context, note, todo

Cross-Platform Integration

Claude: Uses MCP (Model Context Protocol) for filesystem access
ChatGPT: Custom GPT with memory file access instructions
OpenWebUI/Ollama: Local LLM reads memory file for RAG

The Results

Time Savings:

• Eliminated 10-15 minutes of context re-explanation per session
• Reduced cognitive overhead
• Faster iteration on complex projects

Cross-Platform Context:

• Start in Claude, continue in ChatGPT, finish in local LLM
• All platforms share the same project context

Knowledge Retention:

• Decisions documented with rationale
• Insights captured before they're forgotten
• Project history preserved across months

Technical Highlights

Why JSONL?

• Cross-platform compatibility
• Human-readable (can grep or edit directly)
• Simple beats complex for personal infrastructure
• Industry standard for this use case

Challenges:

• ChatGPT format quirks (requires post-processing)
• MCP connectivity issues occasionally
• Not foolproof, but delivers core value

Why This Matters

Recognition: I'm in the top 1% of ChatGPT users and top 3% for conversation volume (2025 ChatGPT Year-in-Review). The AI Memory System is how I maintain that level of productivity.

Skills Demonstrated:

• Cross-platform AI integration
• Structured data design
• File-based ledger systems
• Context engineering
• Real-world AI workflow optimization

Part of OfflineAI Infrastructure:

• AI Memory System (this project)
• Local LLM Setup (Ollama, Qwen 2.5:14B, OpenWebUI)
• RAG Knowledge Base
• MCP Integration

Related Projects:

• Local LLM Setup
• NeighborhoodShare
• All Projects