Acontext vs autoharness
Skill memory layer for agents: auto-captures learnings from runs into plain Markdown skill files you can read, edit, git and share across frameworks — memory without an opaque store. — versus — Self-learning skill layer for Claude Code: distills skills from your real sessions, merges same-scenario ones, updates them in use and prunes the unused — touching only skills it wrote.
Both auto-capture learnings from agent runs into plain Markdown skill files; acontext is framework-agnostic, autoharness a Claude Code plugin that also merges and prunes its library.
| Acontext | autoharness | |
|---|---|---|
| Stars | 3.7k | 11k |
| Forks | 333 | 608 |
| Language | JavaScript | Python |
| License | Apache-2.0 | MIT |
| Last activity | 2 months ago | 2 days ago |
| Topics | memory, skills | skills, coding |
| Curated connections | 12 | 5 |
Acontext — the curator's take
The "memory should be legible" bet: instead of embeddings in a vector store, learnings from agent runs become Markdown skill files you can read, diff, git and mount into any framework — debuggable memory users can inspect and correct, the exact thing opaque memory layers get wrong. It can also adopt and evolve skills you wrote or downloaded. Trade-off: no semantic recall over thousands of entries; it lives or dies on distilling runs into a curated, manageable skill set. Want scale-out retrieval memory instead? That's memmachine or hindsight territory.
autoharness — the curator's take
Install it if you live in Claude Code and want lessons captured without curating skills by hand — zero config, zero deps, and it never touches skills you wrote or installed. Skip it off Claude Code, or if you need measured gains: skills survive on adherence, not a held-out score. Needs Python 3.11+ as `python3` on PATH, or its hooks stay off (macOS's stock /usr/bin/python3 is 3.9).