dmx

The missing orchestrator for AI-native engineering.

Most teams building with AI run into the same problems:

• Workflow lives in chat history. No process, nothing that persists, nothing you can hand off.
• Every developer is using AI differently. Different tools, different prompts, different output.
• Results are unpredictable. Brilliant one session, wrong the next.
• No shared context. Every session starts from scratch, every developer builds their own mental model.
• Missing context causes drift. The same problem solved three ways in the same codebase.

The AI SDLC is the framework we built to fix this: start with a spec, build in phases, verify output, keep context in the repo. dmx is the orchestrator that makes the AI SDLC executable.

dmx runs as an MCP server inside Cursor, Claude Code, GitHub Copilot, and Antigravity. It gives you AI skills that govern the full engineering lifecycle — from first spec to production release — as structured loops. Each loop has a skill sequence, a shared memory context, and a validator. A loop without a validator is just a script.

/dmx/create-ticket         # describe the work → spec → branch
/dmx/plan                  # spec → phased task list
/dmx/implement-next-phase  # build next phase, stop, wait for review
/dmx/validate              # quality gate: spec, security, coverage
/dmx/create-pr             # sync memory, draft PR body, open PR
/dmx/create-release        # tag and publish the release

Every command stops and waits. You review, decide, and move forward. Project context lives in .dmx/ — committed to the repo, read by every AI session.

When a skill sequence is trusted, formalize it as a loop — declarative YAML config, automated validators, and persisted state:

/run-loop spec             # start the spec loop (ticket → branch → spec)
/loop-continue             # resume after a human gate

Loop runtime

Loops are declarative configs that run an ordered skill sequence with durable state and automated validation. Default configs ship with dmx; teams override via .dmx/loops/{name}.yaml.

Foreground is running /dmx/* skills manually — you are the orchestrator. Background is the loop runtime: you define the sequence, write validators that encode your judgment, and review the artifact. Trust is earned by validators, not a config flag.

Bundled loops for the SDLC pipeline:

Each loop config defines a goal state, optional repeat_until condition, validators, human gate policy, and on_complete chaining. Run state is written to .dmx/jobs/{job_id}/{loop_name}-{task_id}.json — there's no separate active-run pointer; the active run is derived by scanning a job's state files for the one non-terminal (pending/running/paused/iterating) entry, keyed off the current branch/ticket. This keeps loop state isolated per branch: pausing work on one branch and running a loop on another can't corrupt or lose either one's state.

Validators are plain Python functions at validators/{name}.py in the app repo (bundled fallbacks ship with dmx). The orchestrator invokes them via subprocess after all skills complete — the coding agent runs skills; validators run deterministically.

Status: M1 is complete (#5) — config, state persistence, MCP orchestration (run_loop, loop_advance, loop_continue), human-gate sequencing, validator execution, the policy engine, repeat_until iteration, on_complete auto-chaining, and loop-level memory hooks are all implemented and covered by an end-to-end integration test suite that runs the full spec → plan → dev → validate → release pipeline through the real MCP tools.

Loop-level memory hooks: before the first skill runs, the runtime surfaces activeContext.md's Open Learnings / Open Decisions to the agent; when a loop finishes (complete, paused for validator review, or iterating), it appends a one-line breadcrumb to Session Notes. This is a deterministic log entry, not judgment — promoting it into durable knowledge is still /dmx/update-memory's job.

Roadmap

• Full lifecycle workflow — spec, plan, build, validate, release
• .dmx/ memory bank — shared project context committed to the repo
• Loop runtime — background execution engine with validators, policy, repeat_until, and autonomous chaining (#5)
• Team server — hosted MCP endpoint, shared loops and rules across the team
• Gateway — model governance, cost visibility, autonomous background execution

Get started

{
  "mcpServers": {
    "dmx": {
      "command": "uvx",
      "args": ["--from", "deepmodel-dmx", "dmx", "serve"]
    }
  }
}

Add to your IDE config (Claude Code, Copilot, Antigravity ↓). Then follow Your first project to initialize and run the full loop on a new repo.

Your first project

A brand-new repo, from /dmx/init through the first merged PR. You review at every gate; /dmx/loop-continue is how you move forward.

Before you start

• A GitHub repo cloned locally with an origin remote. The spec loop creates the feature branch on GitHub from origin/{branch_base} (usually main or master), even if you track work in Jira or use no ticketing system.
• The dmx MCP server added to your IDE (install guide).
• The GitHub MCP server (user-github) authenticated. /dmx/init probes it before writing any files.
• The Atlassian MCP server only if you will choose Jira at init.

1. Initialize

On the integration branch (main or master):

/dmx/init

Choose a workflow — sdlc (this walkthrough) or freestyle (no process enforced) — and a ticketing system: none, github-issues, or jira. Re-run /dmx/init at any time to switch workflow or ticketing; existing memory bank files with content are left intact.

Init writes .dmx/config.md and the memory bank (projectbrief.md, productContext.md, systemPatterns.md, techContext.md, activeContext.md). Open a new chat so the IDE rules take effect.

If you already have a docs/ folder, init reads it when populating the memory bank. Writing the requirement first is slightly better; either order works.

2. Write the product requirement

Create docs/requirements.md at the repo root and put the product or feature description there. You can paste the requirement into chat later instead, but a file is easier to review and reuse.

docs/requirements.md is a convention, not a dmx artifact. The spec loop uses whatever you point it at.

3. Commit and push

The spec loop must start from the configured integration branch, and it creates the remote feature branch from whatever is already on origin. Commit .dmx/, docs/, and any files init wrote, then push:

git add .
git commit -m "chore: initialize dmx and capture product requirements"
git push -u origin HEAD

Stay on main or master.

4. Start the spec loop

In the same message as the command, point at the requirement file or paste it:

/dmx/run-loop spec

See docs/requirements.md

This creates a GitHub issue or Jira ticket (if you configured one), cuts a feature branch from origin/{branch_base}, checks it out, and writes .dmx/spec.md. Then it pauses.

5. Answer the spec

Open .dmx/spec.md. Fill in Technical Approach if it is incomplete, and answer every question. Empty answers or placeholders (TBD, TODO) fail the spec validator.

/dmx/loop-continue

On success the runtime auto-chains to the plan loop.

6. Review the plan

plan writes .dmx/tasks.md with phases and tasks, then pauses. Edit freely — this is the implementation contract.

/dmx/loop-continue

That starts the dev loop.

7. Build phase by phase

Each dev cycle:

1. Implements the next unchecked phase in tasks.md, then pauses.
2. Review the diff.
3. /dmx/loop-continue runs commit only. It does not push.
4. Review the commit.
5. /dmx/loop-continue again — validators run, then either the next phase starts or the loop chains to validate.

Repeat until every phase is checked off.

8. Validate and open the PR

After the last phase, validate runs the quality gate and pauses. Review the report, then /dmx/loop-continue. On success the release loop runs create-pr: it pushes the feature branch and opens the pull request.

9. Merge and close

Review the PR and merge it. Then:

/dmx/close-ticket

That closes the ticket, comments the PR link, and deletes the feature branch locally and on origin. /dmx/loop-continue after merge is not a close path — the release loop ends when the PR exists.

You can run the same steps by hand (/dmx/create-ticket, /dmx/plan, /dmx/implement-next-phase, …) instead of loops. See the skill catalog below.

Learn more

• When Is a Loop Ready to Run Without You? — the thinking behind dmx
• AI SDLC — the open framework dmx implements

{
  "mcpServers": {
    "dmx": {
      "command": "uvx",
      "args": ["--from", "deepmodel-dmx", "dmx", "serve"]
    }
  }
}

{
  "mcpServers": {
    "dmx": {
      "command": "uvx",
      "args": ["--from", "deepmodel-dmx", "dmx", "serve"]
    }
  }
}

{
  "mcp": {
    "servers": {
      "dmx": {
        "command": "uvx",
        "args": ["--from", "deepmodel-dmx", "dmx", "serve"]
      }
    }
  }
}

{
  "mcpServers": {
    "dmx": {
      "command": "uvx",
      "args": ["--from", "deepmodel-dmx", "dmx", "serve"]
    }
  }
}

/dmx/create-ticket