Cq: A Shared Knowledge Commons for AI Agents

Mozilla.ai has introduced cq, an open-source standard and platform that enables AI agents to share knowledge and avoid repeating costly mistakes. The system uses a tiered architecture with private, organizational, and public knowledge pools to reduce token usage, compute power, and electricity consumption. Cq integrates with AI coding assistants via the Model Context Protocol to persist and query collective experience.

| cq: A Shared Knowledge Commons for AI Agents | | Written by Nikos Vaggalis | ||| | Thursday, 16 July 2026 | ||| | Mozilla.ai has introduced cq, an open-source standard and platform designed to facilitate shared learning among AI agents. This promises to eliminate a lot of duplicated effort on the part of AI agents, thereby making savings in terms of cost and resources. cq which stands for the radio call sign CQ meaning "any station, respond" is an open standard and shared knowledge commons for AI agents. In general, think of it as Stack Overflow for AI agents; just as human developers use Stack Overflow to share programming solutions, AI agents such as Claude Code, Cursor, or GitHub Copilot use cq to broadcast what they have learned and listen to what other agents already know. This is where the "any station, respond" fits. But why is cq necessary? Currently, AI agents operate in isolation; when an agent hits a roadblock like an undocumented API quirk, a library version conflict, or a configuration bug, it has to figure out the solution from scratch. It writes failing code, triggers broken builds, reads files, and slowly debugs. Imagine now this being encountered by thousands of agents. When thousands of agents encounter the exact same error, they collectively repeat these identical failures daily, burning massive amounts of tokens, time, compute power, and electricity. Worse, because agents traditionally lack persistence, they might even forget what they learned in your own project once the session resets. cq solves this "agent amnesia" by allowing agents to securely persist, query, and verify collective experience. By utilizing a common knowledge format, the system allows agents to exchange experience-driven insights, preventing them from repeating costly technical mistakes in isolation. An agent integrated with cq does not just write code, it follows a skill-guided query/propose workflow: Under the hood, cq works by providing a standard set of Model Context Protocol MCP tools for the agent to call: Knowledge in cq moves through a tiered "graduation" process to keep local systems lightweight and ensure only secure, verified, generalizable insights reach the public. The infrastructure operates across three tiers, including private local storage, organizational namespaces for teams, and a Global Commons for public knowledge: Tier 1-Local agent/machine level. Private to your computer and saved in a local SQLite database. This solves basic agent memory loss across sessions and never shares data unless explicitly nominated. Tier 2-Team/ organization. Shared privately within a company or department. It tracks internal microservices, proprietary APIs, and development environments. It runs as a hosted Postgres database using vector search to find relevant entries. Tier 3-Global Commons public . A completely free, community governed public knowledge pool managed by Mozilla.ai at cq.exchange, immediately available to any AI agent in the world. Before wiring cq into any specific AI assistant, you must first install the core cq CLI and make sure it is added to your system's PATH. You can do this via Homebrew for macOS/Linux or Scoop for Windows . Once the CLI is installed, you use the cq install tool to register the cq Model Context Protocol MCP server, install the shared cq skill, and add the necessary always loaded instruction block to your assistant's configuration. Then to confirm that your assistant can talk to cq, open a terminal session with your AI agent and run the status slash command On the first run, your AI coding agent will ask you to approve the MCP tool call. You should see status metrics showing the number of locally stored Knowledge Units KUs , domain breakdowns, and confidence distributions. By default, all of your agent's learnings are saved strictly on your local machine in a private SQLite database ~/.local/share/cq/local.db . If you want your agent to access the Global Commons shared public repository hosted by Mozilla.ai or sync its database across multiple computers, you can connect it to the remote cq exchange service. As a developer, you rarely need to interact with cq manually; the plugin runs automatically under the hood. As such, devs can integrate the technology into various AI assistants, ultimately improving AI efficiency and reducing environmental impact by creating a collaborative ecosystem similar to a "Stack Overflow for agents." More Information Related Articles To be informed about new articles on I Programmer, sign up for our Comments or email your comment to: | ||| | Last Updated Thursday, 16 July 2026 |