Acontext vs claude-reflect
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 — Claude Code plugin that learns from your corrections — hooks capture them in-session, /reflect syncs approved learnings to CLAUDE.md/AGENTS.md, /reflect-skills mines history into reusable commands.
claude-reflect does the same capture-corrections-into-CLAUDE.md/skills loop but is a Claude Code plugin with human-approved syncing; Acontext is framework-agnostic and automatic. Deep in Claude Code → reflect; multi-framework or building your own agent → Acontext.
| Acontext | claude-reflect | |
|---|---|---|
| Stars | 3.6k | 1.3k |
| Forks | 331 | 108 |
| Language | JavaScript | Python |
| License | Apache-2.0 | MIT |
| Last activity | 20 days ago | 4 months ago |
| Topics | memory, skills | coding, memory |
| Curated connections | 4 | 12 |
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.
claude-reflect — the curator's take
Install claude-reflect the third time you catch yourself typing the same correction into Claude Code. It's the pragmatic take on agent memory: no vector DB, no service — hooks queue corrections, you review, markdown files get smarter, and the AGENTS.md sync means Codex/Cursor/Aider benefit too. The /reflect-skills pattern-mining is the sleeper feature: 15 similar requests become one command. When NOT: if you expect actual memory infrastructure (semantic recall, knowledge graphs) — this is disciplined note-taking with AI triage, personal-scale by design. Everything lands via human review, which is a feature, not friction.