SuperContext: A Persistent Memory Framework for AI Coding Agents

What SuperContext Solves

The developer built this after 1,500+ sessions and months of daily use across 60+ projects, getting tired of re-explaining their codebase every session. The core problem they identified: typical solutions involve making instruction files larger, but a 2,000-line CLAUDE.md eats context window space before questions are asked, and AI ends up ignoring half of it.

Architecture: Targeted Files Instead of Monolithic Docs

SuperContext takes the opposite approach with small, targeted files loaded only when relevant:

• Constitution (~200 lines, always loaded): Global rules, routing, preferences
• Living Memory (~50 lines, always loaded): Behavioral gotchas that prevent repeated mistakes
• Project Brains (loaded on entry): Per-project business rules, schemas, changelogs
• Knowledge Store (on demand): Searchable SQLite database for infrastructure, APIs, reference data
• Session Memory: Automatic conversation logging so your AI recalls past decisions

What's Included

The repository contains two main components:

• The full guide covering theory, architecture, anti-patterns, and tool-specific setup for Claude Code, Cursor, Copilot, Codex, Aider, and others
• An executable prompt that you hand to your AI with the instruction "run this" - it discovers your projects, migrates existing content, and builds the whole system in approximately 10 minutes with no manual setup

Development Context

The framework was developed while building construction management integrations (Vista, Procore, Monday.com), where getting context wrong means real production problems. The developer reports that with this system, their AI went from "helpful but forgetful" to genuinely knowing their systems.