agentic-context-engine vs hivemind
Learning loop for any agent: reflect on failures, distill strategies into a Skillbook, inject them next run — 2x consistency on Tau2, 49% token cuts. LiteLLM-based, 100+ providers. — 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.
Same learn-from-experience loop, different scope: ACE is an embedded Python engine distilling strategies for the agent you're building; Hivemind is a service layer distilling skills across every off-the-shelf agent your team runs.
| agentic-context-engine | hivemind | |
|---|---|---|
| Stars | 2.5k | 1.5k |
| Forks | 301 | 94 |
| Language | Python | TypeScript |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 16 days ago | 3 days ago |
| Topics | memory, agents | memory, coding |
| Curated connections | 4 | 2 |
agentic-context-engine — the curator's take
The in-process answer to 'my agent repeats the same mistakes': wrap your agent, feed it corrections, and ACE extracts reusable strategies it injects on later runs — no fine-tuning, no reward signals, and the numbers are concrete (2x pass^4 on Tau2, ~$1.50 to learn its way through a 14k-line translation). Pick it over a memory *service* when you want the learning inside your Python process rather than behind an HTTP API. NOT magic memory: strategies come from explicit feedback loops you wire up, quality follows the judge model, and the open-source engine is the on-ramp to the hosted Kayba product — check where the managed line lands before betting infra on it.
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.