dmx

dmx is an open-source orchestrator for AI-native engineering, built by DeepModel, Inc. and licensed under AGPL-3.0. It runs as an MCP server inside Cursor, Claude Code, GitHub Copilot, and Antigravity, adding a structured governance layer on top of whatever AI IDE you already use. The project implements the AI SDLC framework — a five-phase workflow (Spec → Plan → Build → Validate → Release) with explicit human control points at every phase boundary.

What It Is

dmx is an agent harness — a category of tooling that sits between the AI model and the developer's codebase, enforcing process rather than improving the model itself. Where tools like Cursor and Claude Code are execution engines optimized for speed, dmx adds the structure those tools lack: ordered phases, mandatory human gates, automated validators, and persistent project memory. The core insight is that faster execution of an unstructured process is still an unstructured process.

How the Loop Runtime Works

The central abstraction in dmx is the loop — a declarative YAML config that defines an ordered skill sequence, a validator policy, a human gate, and optional chaining to the next loop. Loops ship as bundled defaults and can be overridden per-project via .dmx/loops/{name}.yaml, which is committed to the repo and reviewed in PRs like any other code change.

• Foreground mode: developers invoke /dmx/* skills manually — they are the orchestrator.
• Background mode: the loop runtime runs the sequence, invokes validators, and pauses at human gates.
• Validators are plain Python functions at validators/{name}.py in the app repo. Required validators block loop advancement; optional validators warn. The policy is explicit and version-controlled.
• Persistent job state: every loop run writes to .dmx/loop-state.json (active pointer) and .dmx/jobs/{job_id}/ (per-run history). Close the IDE and resume tomorrow with /loop-continue.

The bundled SDLC pipeline chains five loops: spec → plan → dev → validate → release.

The Memory Bank

The .dmx/ directory is committed to the repository and serves as the project's shared memory. Every AI session — and every developer — starts from the same context without re-explaining the project. Key files include:

• projectbrief.md, productContext.md, systemPatterns.md, techContext.md — durable context updated when architecture or features change
• activeContext.md — a branch-local learning inbox promoted to durable files on commit or PR
• spec.md and tasks.md — branch-scoped artifacts created by the spec and plan loops
• loops/ — team-shared loop config overrides

A three-tier memory sync model keeps context current: /dmx/commit does a light sync, /dmx/create-pr does a full sync, and /dmx/update-memory does a deep reconciliation on demand.

Team and Monorepo Setup

dmx is designed for team use. One developer runs /dmx/init once per repository; every subsequent developer clones the repo and gets the full memory bank automatically. Each developer connects their IDE to dmx as an MCP server using the same uvx-based config — no per-developer secrets or configuration. Loop configs and custom validators live in the repo, versioned and reviewed like code. For monorepos, dmx initializes at the repository root with one .dmx/ memory bank per repo; teams scope each ticket explicitly in the spec to keep AI work bounded to the relevant services and directories.

Update: v0.3.1

The latest release is v0.3.1, published on 2026-08-29. The project reached its M1 milestone (tracked as issue #5), which delivered the full loop runtime: 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. The M1 milestone is covered by an end-to-end integration test suite that runs the full spec → plan → dev → validate → release pipeline through the real MCP tools. Upcoming roadmap items include a hosted team server (shared MCP endpoint, shared loops and rules) and a gateway for model governance and cost visibility.