Hivemind: Shared Memory for AI Agents
4.0Hivemind provides shared memory for AI agents like Claude Code, OpenClaw, Codex, Cursor, Hermes, and pi. Install via npm to enable seamless collaboration.
About
As artificial intelligence advances at breakneck speed, coordinating multiple AI assistants has become the norm—yet the problem of information silos continues to frustrate developers. Hivemind was built to solve this: it is a shared memory system designed specifically for AI agents. With a single command, you can give mainstream AI assistants like Claude Code, OpenClaw, Codex, Cursor, Hermes, and pi a unified, persistent memory space. After installing via npm, Hivemind automatically detects every assistant on your machine, allowing them to share context and avoid redundant work, dramatically improving overall efficiency.
Key Features
Pricing & Fees
- Pricing: Three plans — Basic at $0/month (pay as you go, $15 credits included, no card required), Team at $99/seat/month, and Enterprise with custom pricing.
- Free tier: The Basic plan includes 150k traces and 1.5M queries per month, with unlimited seats, before overage charges apply.
- Trial: The Team plan offers a 7-day free trial for per-seat collaboration features.
- Usage limits: Team includes 1M traces and 10M queries per seat per month; beyond included amounts, usage costs $0.25 per 1k traces and 10k queries.
- Enterprise: Custom plan that runs inside your own VPC, with SAML SSO, SOC 2, HIPAA compliance, a named support engineer, and unlimited seats.
Who It's For
Hivemind is built for AI developers and researchers who need multiple models to collaborate on complex tasks while keeping context coherent; software engineers who use several coding assistants like Claude Code or Cursor daily and want to cut down on redundant input; technical team leads looking to unify their team's AI tool knowledge base for better collaboration; AI tool enthusiasts who love experimenting with new assistants but don't want to deal with fragmented memory; and automation workflow builders who need different agents to hand off tasks in end-to-end pipelines.