AI Context Files

Overview

A framework and methodology for externalizing LLM agent knowledge into structured, persistent context files — decoupling knowledge from any single conversation or agent session so it compounds over time.

Problem

LLM agents are stateless. Every new session starts blank. Long conversations degrade quality. Context windows are limited. The real value — decisions, research, project state — is lost when the conversation ends.

Approach

Core Principle

Agents are disposable, context is not. The value lives in the accumulated context files, not in the chat history.

The Pattern

1. Before a session — Load relevant context files into a fresh agent
2. During a session — Work with the agent, make decisions, generate ideas
3. After a session — Distill key outputs back into context files for next time

Key Properties

• Context files as persistent memory — decisions, research, ideas, and project state stored in plain files
• Load on demand — fresh agent loads only what’s relevant, avoiding context window bloat
• Fights agent degradation — fresh agents with clean context avoid long-conversation coherence loss
• Agent-agnostic — plain text works with any LLM, any tool, any platform
• Human-readable and version-controllable — git-friendly, editable, collaborative
• Compound knowledge over time — each session distills findings back, so the next session starts smarter

Open Questions

• What’s the optimal structure/format for context files?
• How to automate the “distill back” step?
• How to handle context file discovery and relevance ranking?
• Tooling for loading context files into different LLM platforms?

References

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