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I was tired of copy-pasting context between Claude Code and other CLIs
I ve been using Claude Code as my main terminal driver for a while, but it has a huge blind spot: it operates completely in isolation. Whenever I needed Gemini to do deep repo research, or Cursor to handle UI work simultaneously, Claude had no idea what they were doing. I was stuck playing the middleman, copy-pasting output from one terminal into the other. So I built Neohive. It s an open-source MCP server that gives Claude Code a shared local directory to communicate with your other agents. How it actually works:
* You run `npx neohive init` in your project.
* Claude Code and your other CLIs read/write to a shared `.neohive/` folder on your drive.
* Claude can now autonomously ping Gemini, assign tasks, or ask for code reviews without you copying text. Everything is local. No cloud, no database, just the filesystem acting as a message bus. Repo is here if you want to test it out: https://github.com/fakiho/neohive If anyone else is running Claude Code alongside other local models, I'd love to know how you handle keeping their context synced.
I built an open-source MCP server that lets Claude Code talk to any Claude CLI or LLM IDE
I've been using Claude Code as my primary coding tool for months, but I kept hitting the same wall: the moment I needed a second agent -- Gemini for research, Cursor for UI work, or another Claude Code instance for a parallel task -- they couldn't coordinate. I was the one copying context between terminals.
So I built Neohive. It's an MCP collaboration layer that lets multiple AI CLI agents communicate through a shared directory on your machine.
How it works:
Neohive - One command. Your AI agents can talk to each other.
Neohive lets AI IDE and CLI agents talk to each other through shared files. No cloud, no API keys.
- Works across 7 tools: Claude Code, Gemini CLI, Cursor, VSC, Antigravity, Codex
- Zero setup: just run npx neohive init
- 70+ MCP tools: messaging, tasks, workflows, file locking, reviews
- Real-time dashboard with kanban and agent monitoring
- Entirely local: all data stays on your machine
One command. Your AI agents become a team.
