The Problem with Traditional AI Memory
Most agent memory systems fall into three categories — vector stores, knowledge graphs, and plain-text memory (used by products like Mem0 and Letta). They all share common limitations:- Vector stores encode memory as embeddings you can’t inspect, edit, or version control
- Knowledge graphs require complex schemas upfront and are hard to modify once built
- Plain-text files are readable and filesystem-native, but lack organization and indexing — agents struggle to quickly find what they need as memory grows
- Common pain point: you can’t inspect what your agent actually learned, control how memory is structured, or integrate it into your existing dev workflow
Skill Memory — A Different Approach
Acontext organizes agent memory as skills — file collections where each skill has:- A SKILL.md that defines its schema, purpose, and guidelines
- Data files in plain markdown, organized by the skill’s own rules
daily-logs— one file per day, capturing what happeneduser-general-facts— one file per topic, storing user preferences- Domain SOPs — structured procedures the agent learned from past runs
Three Advantages
| Advantage | What it means |
|---|---|
| Filesystem-compatible | Skills are real files — mount in sandboxes, sync to disk, version control, cat and grep them |
| Configurable | Each SKILL.md defines its own schema — you control how memory is organized, not the platform |
| Human-friendly | Plain markdown files — read, edit, review, and audit your agent’s memory directly |
Comparison
| Skill Memory (Acontext) | Vector Store | Knowledge Graph | Plain-text Files | |
|---|---|---|---|---|
| Storage format | Markdown files | Embeddings | Nodes & edges | Text files |
| Human-readable | Yes | No | Partially | Partially |
| Configurable schema | Yes (SKILL.md) | No | Complex upfront | No |
| Filesystem-native | Yes | No | No | Yes |
| Version controllable | Yes | No | No | No |