With over 77,000 GitHub stars accumulated in under five months since its March 2026 launch, Paperclip has become the default control plane for teams running multiple AI agents in production. Rather than juggling dozens of terminal tabs with Claude Code sessions, Codex instances, and Gemini CLI workers, Paperclip organizes all agents into a company structure with org charts, reporting lines, role-based permissions, and per-agent monthly budgets that trigger hard-stops when exceeded. The platform supports any runtime through its adapter system — Process adapters manage local CLI agents like Claude Code, Codex, Cursor, Pi, and OpenCode as child processes, while HTTP adapters trigger remote agents via webhooks to OpenClaw, serverless platforms, or custom endpoints. Heartbeat-based execution wakes agents on configurable schedules, injecting goal context, budget state, and workspace paths directly into the invocation payload. The Work and Task System provides atomic checkout with execution locks, first-class blocker dependencies, and structured work products to eliminate duplicate effort. Governance features include approval workflows, decision tracking, emergency stops, and full audit trails tracing every mutation to an actor. Deployment runs as a single Node.js process with embedded PostgreSQL locally or scales to external Postgres for production, installable in one command via npx paperclipai onboard. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Nerve is the web cockpit for OpenClaw, giving you one real-time screen to run an entire fleet of AI agents instead of chasing them through chat threads. Every agent keeps its own workspace, memory, identity, and skills, and you can switch between agents, browse their files, and edit memory or configuration in a tabbed editor during a live conversation. Work moves onto a kanban board where subagents pick up delegated tasks, and returned work waits in a proposal inbox for your review before it is accepted. Cron jobs run as their own sessions in a navigable session tree, so scheduled automation has logs and output you can actually inspect. Voice is a first-class input: push to talk or use a wake word, watch live transcription, and hear replies spoken back in your chosen language. Ask for a chart and the answer arrives as a real rendered chart, with diffs, file previews, and highlighted code inline in the stream. Token usage, running costs, and context pressure stay visible on every session, so long tasks never quietly drain your budget. Run it privately on localhost, reach it over your LAN behind password authentication and signed-cookie sessions, or keep access inside a Tailscale tailnet. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.