Acontext vs hivemind
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 — Activeloop's shared brain for agent TEAMS: traces from Claude Code, Codex, Cursor & co become reusable skills every teammate's agent can execute — cloud-backed, 25% cheaper on LoCoMo.
Both turn agent traces into reusable skills. Hivemind is cloud-backed and team-oriented — one teammate's fix becomes everyone's skill; Acontext is local plain-file skill memory you git and share yourself. Managed team brain vs portable files.
| Acontext | hivemind | |
|---|---|---|
| Stars | 3.6k | 1.5k |
| Forks | 331 | 95 |
| Language | JavaScript | TypeScript |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 20 days ago | 4 days ago |
| Topics | memory, skills | memory, coding |
| Curated connections | 4 | 3 |
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.
hivemind — the curator's take
The pitch is the org-level version of agent memory: one engineer's agent figures out the tricky migration on Monday, every agent on the team executes the pattern Tuesday. Auto-learning from traces across seven agent hosts, with real LoCoMo receipts (25% cheaper, 1.7x fewer tokens vs no shared memory). Reach for it when the unit of learning is the team, not the seat. NOT local-first: cloud-backed on Deeplake is the architecture AND the business model (Activeloop, YC) — traces of your engineers' sessions leave the machine, so clear it with whoever owns your IP policy before the whole team wires in.