agent-beacon vs hivemind
Cross-harness memory for coding agents: captures sessions from Claude Code, Cursor, Codex and 20+ harnesses, distills reviewed knowledge over MCP/skills, and forwards telemetry to SIEMs. — 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 promise: what one agent learned should not be relearned by the next. hivemind turns team traces into cloud-backed executable skills; Beacon keeps a local-first JSONL record and distills reviewed knowledge.
| agent-beacon | hivemind | |
|---|---|---|
| Stars | 1.7k | 1.6k |
| Forks | 145 | 110 |
| Language | Go | TypeScript |
| License | MIT | Apache-2.0 |
| Last activity | today | today |
| Topics | memory, coding, security | memory, coding |
| Curated connections | 3 | 6 |
agent-beacon — the curator's take
Two jobs in one endpoint agent, and it pays to know which one you want. The memory side captures every session across Claude Code, Codex, Cursor, OpenCode and 20+ harnesses, replays them exactly, and turns fixes and conventions into reviewed knowledge future agents pull through MCP or Agent Skills. The security side normalizes the same traces into an OpenTelemetry event model and ships them to Splunk, Sentinel, CrowdStrike LogScale and friends, with MSI, .deb/.rpm and MDM installs. Strong fit when a team runs many harnesses and wants one record for both recall and audit. Watch the default: interactive setup preselects hosted Beacon Managed forwarding (Local is an explicit opt-out), so choose deliberately on machines with sensitive code. For solo, single-harness recall, claude-mem or deja-vu is less machinery.
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.