Context-Infrastructure: Persistent Memory Blueprint for AI Agents
Source: https://github.com/grapeot/context-infrastructure Author: grapeot Date: 2025
Summary
Year-long running reference implementation of persistent memory and context system for AI coding agents. Three-layer architecture: display layer (43 decision axioms, 25+ reusable skills), reusable layer (SOUL.md, USER.md templates), non-reusable layer (personal behavioral data). Goal: move beyond AI that “only speaks correct platitudes” to genuinely personalized context.
Key Claims
- Problem: AI produces “correct but generic responses” without personalized context
- Layer 1 (Display): 43 decision axioms + 25+ reusable skills — accumulated over a year
- Layer 2 (Reusable): SOUL.md (identity), USER.md (preferences), communication style, memory code
- Layer 3 (Non-reusable): Domain insights; must accumulate yourself; no shortcuts
- Immediate value: fill in USER.md for instant personalization
- True value: build your own behavioral dataset over time
Connection to Other Sources
Related to Karpathy LLM Wiki Pattern (same wiki-first approach) and Context Engineering. Also connects to Claude Code Spec Workflow.
Concepts
- Context Engineering — persistent context as personalization infrastructure
- Agent Memory — behavioral dataset as long-term memory
- Coding Agents — personalized context for coding agent workflows