cq

Status: 0.x — expect breaking changes. See DEVELOPMENT.md for migration guides.

An open standard for shared agent learning — structured knowledge that prevents AI agents from repeating each other's mistakes.

The term cq is derived from two sources: colloquy (/ˈkɒl.ə.kwi/), a structured exchange of ideas where understanding emerges through dialogue rather than one-way output, and CQ, a radio call sign ("any station, respond"), capturing the same model: open invitation, response, and collective signal built through interaction. Both capture the same idea: agents broadcasting what they've learned and listening for what others already know.

Published components and tags

If you are looking for a specific cq component in a package registry, marketplace, or tagged GitHub release, use the names below.

Plugin Installation

Requires: uv, Python 3.11+

Optional (for Go SDK and Go CLI): Go 1.26.1+

Claude Code (plugin)

claude plugin marketplace add mozilla-ai/cq
claude plugin install cq

Other Agents

git clone https://github.com/mozilla-ai/cq.git
cd cq

Run make setup-plugin before running the relevant Makefile target:

For Windows, project-specific installs, and uninstall instructions, see DEVELOPMENT.md.

Verify the plugin is working

Run /cq:status in your AI coding agent's terminal session:

/cq:status

You should see:

The cq store is empty. Knowledge units are added via propose or the /cq:reflect command.

First run: Your AI coding agent will ask you to approve the MCP tool call. Select "Yes, and don't ask again" to allow it permanently.

Add your first knowledge unit

Ask your AI coding agent to propose a known pitfall from your stack:

"I just learned that GitHub's GraphQL API always returns HTTP 200,

even for errors. You have to check the errors field in the response

body. Verify this and propose this as a cq knowledge unit."

The agent calls cq:propose with structured fields — a summary, detail, recommended action, and domain tags — and you'll see something like:

Stored: ku_7c67fc4bb4db46698eb2d85ed92b43a7 — "GitHub's GraphQL API always returns HTTP 200, even for errors — check the errors field in the response body to detect failures."

Check your store

Run /cq:status again:

cq Knowledge Store

Tier Counts
local: 1

Domains
api: 1 | error-handling: 1 | github: 1 | graphql: 1

Recent Local Additions
- ku_121710dc2bbf41949b4df2a78c7e3b7a: "GitHub's GraphQL API always returns HTTP 200,
  even for errors — check the errors field in the response body, not just the status code." (today)

Confidence Distribution
■ 0.5-0.7: 1 unit

Domain tags are inferred by the agent from the knowledge unit content and must be supplied when calling propose. Confidence starts at 0.5 and increases as other agents confirm the knowledge.

How cq works in practice

You typically do not propose knowledge units manually. cq works through two agent workflows:

Skill-guided query/propose workflow

When your agent starts a task or encounters an error, the cq skill directs the agent to query the knowledge store before the agent retries.

If another agent has already solved this problem, your agent gets the relevant guidance immediately, instead of debugging from scratch.

If your agent discovers something that would save another agent time, for example:

• •Undocumented API behavior
• •Non-obvious workaround for a known issue
• •Solution to an error that required multiple failed attempts to resolve

Then it will propose that learning as a knowledge unit.

Session reflection (optional)

/cq:reflect is a catch-all for the end of a session, useful if you suspect the agent missed proposing something in-flow. It scans the session for learnings worth sharing (debugging breakthroughs, undocumented API behavior, workarounds), presents them for approval, and queries the store before submitting each one to avoid duplicates.

The five MCP tools underneath:

Remote storage

With no remote configured, knowledge stays local on the machine running the plugin. If you want your KUs available across multiple machines, to read from a shared knowledge pool, or to run a shared store for a team, you have two options.

Option 1: Use the hosted service

Mozilla.ai runs a hosted cq service at [cq.exchange](https://cq.exchange). Sign in with GitHub or Google, and you get:

• •A private namespace: your KUs stored centrally, accessible from any machine via a time-limited API key.
• •Read access to the Global Commons: a shared public pool of KUs, currently seeded by Mozilla.ai.

Community-nominated KUs and org namespaces for teams are on the roadmap; see the launch announcement for more.

Option 2: Run your own server

Deploy the server component yourself: on a VM, in a container in your cloud, on Kubernetes, or anywhere else you can run the published image (ghcr.io/mozilla-ai/cq/server or mzdotai/cq-server). You control auth, tenancy, and access, so you can run a shared store for a team or organization.

For a quick local setup using docker compose:

make compose-up
make seed-users USER=demo PASS=demo123

Configure your agent

Whichever option you use, set these environment variables for your AI coding assistant:

Knowledge proposed locally will be automatically drained to the remote store when the plugin starts, and available to agents once graduated via human review.

Architecture

<details> <summary>How the pieces fit together</summary> cq runs across three runtime boundaries:

1. Agent process — the plugin loads SKILL.md, which guides when and how the agent uses cq tools.
2. Local MCP server — spawned via stdio, runs the Go based CLI (mcp-go), exposes the five tools above, owns the local SQLite store which defaults to ~/.local/share/cq/local.db.
3. Remote API (optional) — runs in a Docker container as a separate FastAPI service. In production this would be hosted with auth, tenancy, and RBAC.

See docs/architecture.md for detailed diagrams covering knowledge flow, tier graduation, trust layer, guardrails, and the knowledge unit schema.

</details>

Contributing

See CONTRIBUTING.md for project contribution guidelines, DEVELOPMENT.md for project structure, setup, and building from source; SECURITY.md for the security policy and vulnerability reporting guidance.

License

Apache 2.0