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Acontext vs pro-workflow

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 — One SQLite store under every Claude Code session: corrections become FTS5-searchable rules that auto-load, research grows persistent wikis, and 37 hook scripts add quality gates.

The curated verdict

Both turn corrections into rules the next run obeys; pro-workflow keeps them in one SQLite store under every Claude Code session with FTS5 search, acontext writes portable Markdown skills that survive a change of harness.

Acontextpro-workflow
Stars3.7k2.8k
Forks333272
LanguageJavaScriptJavaScript
LicenseApache-2.0
Last activity1 months ago7 days ago
Topicsmemory, skillscoding, memory
Curated connections116

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.

pro-workflow — the curator's take

The most complete attack on Claude Code amnesia we've mapped: correct it once and the correction becomes a durable, searchable rule; research lands in wikis that persist and even grow via an auto-research loop; hooks add git/secret guards and cost tracking. After 50 sessions the compounding is real. Two cautions: NO license file at review time (all rights reserved by default — same gap as this author's other tools), and 34 skills + 37 hooks is a lot of surface — adopt the memory core first, audit the hooks before letting them gate your commits.