dmx
dmx is an open-source, AI-native engineering harness that runs as an MCP server and wraps AI-driven development workflows in structured, versioned loops with human gates, validators, and persistent job state to make AI-assisted engineering reproducible and governed.
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Quick Overview
Best for: Software & Gaming
What it does
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Best fit
Software & Gaming
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dmx
dmx is an open-source engineering harness designed to add structure, governance, and reproducibility to AI-driven development workflows. It runs as an MCP server inside AI-enabled IDEs (like Cursor, Claude Code, or Copilot) and implements an "AI SDLC" framework of named, versioned phases (Plan, Build, Validate, Release) that include explicit human approval gates. dmx is aimed at engineering teams and developers who use AI to write code and want reliable, auditable workflows: loop configurations and a .dmx/ directory are committed to the repo, validators run at phase boundaries, and job state persists across sessions so teams can pick up work where they left off.
dmx is an open source AI-native engineering harness. It runs as an MCP server inside Cursor, Claude Code, Copilot, or any IDE that speaks the Model Context Protocol, and wraps your AI workflows in structured, verifiable loops.
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Claim this listing for $29Key Features
Named, versioned loops
Each phase of the AI SDLC is a named, versioned loop: an ordered sequence of skills that run, trigger validators, and wait for approval before advancing. Loop configs live in the repository and are versioned alongside code.
Human gates
The loop pauses at every phase boundary and waits for explicit human approval before the next phase runs; the model does not merge or advance the workflow on its own.
Validators with policy
At loop boundaries dmx runs validators against an explicit, version-controlled policy. Required checks block progress while optional checks warn.
Persistent project memory (.dmx/)
A committed .dmx/ directory stores project context — spec, plan, decisions, and job history — so sessions start with full context and teams stop re-explaining the project to the AI.
Persistent job state
Every loop run is a tracked job with a task ID and state that persists across sessions, allowing users to close the IDE and resume later exactly where they left off.
MCP server integration
dmx runs as an MCP server that you point your IDE at; it governs AI execution engines (Cursor, Claude Code, Copilot) rather than replacing them.
Progressive trust
Teams can deliberately relax human gates for loops whose history shows consistent validator success for a given task type; this is an explicit decision made by users.
Pricing
Current pricing details are not available from the vendor source.
Use Cases
Structured AI-assisted development
Enforce an AI SDLC (spec → plan → build → validate → release) so AI-generated code follows a repeatable, reviewable process with human approval at each gate.
Team governance and auditability
Commit loop configs and .dmx/ state to the repo, record validator results and gate approvals, and make AI-driven work auditable and reviewable by the team.
Resume interrupted work
Use persistent job state to close the IDE and later pick up a loop run exactly where it left off, preserving decisions and context.
Add governance to existing AI IDEs
Point an existing AI-enabled IDE at dmx as an MCP server to add sequencing, validators, and human gates on top of execution engines like Claude Code or Cursor.
Integrations
Claude Code
Execution engine integration — dmx governs and sequences work executed by Claude Code.
Cursor
Execution engine integration — dmx provides workflow structure and validators on top of Cursor.
Copilot
Execution engine integration — compatible as an AI execution engine when used with an MCP-capable IDE.
Model Context Protocol (MCP)
dmx runs as an MCP server; IDEs that speak the Model Context Protocol can point to dmx to obtain structured workflows.
uvx
Runtime used in the example MCP config to fetch and serve dmx on demand.
Benefits
Limitations
Frequently Asked Questions
What problem does dmx solve?
Why use dmx if I already use Claude Code or Cursor?
Getting Started
- 1 Add dmx to your IDE's MCP config (no separate install step).
- 2 Example: configure an MCP server entry that runs uvx to fetch and serve dmx (example MCP JSON shown on the page).
- 3 See MCP Server Setup and follow the Quick Start to run your first loop in your IDE.
Support
docs
Documentation and Quick Start guides available on the dmx site (Quick Start, Core Concepts, MCP Server Setup).
project repository
Links in the site header indicate GitHub and PyPI references for code and packages (visible in site navigation).
API
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