autoharness vs hivemind
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. — 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 shares them across a team's agents via the cloud, autoharness keeps a self-maintaining local library for one user.
| autoharness | hivemind | |
|---|---|---|
| Stars | 11k | 1.6k |
| Forks | 608 | 112 |
| Language | Python | TypeScript |
| License | MIT | Apache-2.0 |
| Last activity | 2 days ago | 13 days ago |
| Topics | skills, coding | memory, coding |
| Curated connections | 5 | 7 |
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).
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.