Orchex

Autopilot AI orchestration — describe your intent, orchex auto-generates a plan, parallelizes it into streams with file ownership enforcement, self-heals…

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What it can do

  • Init: Initialize a new orchestration for a feature. Creates .orchex/active/manifest.yaml with streams.
  • Add Stream: Add a new stream to the active orchestration.
  • Status: Get the current orchestration status and progress.

What data it sees

Do you need an account

No: the server works without sign-in

Autopilot AI orchestration — describe your intent, orchex auto-generates a plan, parallelizes it into streams with file ownership enforcement, self-heals failures, and routes across 6 LLM providers. Supports Claude, OpenAI, Gemini, DeepSeek, Ollama, and AWS Bedrock.

How to connect

How to connect Orchex to Claude

The server address to paste into Claude settings:

https://orchex--wundam.run.tools

  1. Open Claude (claude.ai or the desktop app).
  2. Go to Settings → Connectors.
  3. Click Add custom connector.
  4. Paste the server address copied below into Remote MCP server URL and click Add.
  5. In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect Orchex to ChatGPT

The server address to paste into ChatGPT settings:

https://orchex--wundam.run.tools

  1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
  2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
  3. Open Settings → Connectors and click Create.
  4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
  5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
  6. In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect Orchex to Cursor

The server address to paste into Cursor settings:

https://orchex--wundam.run.tools

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

  1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
  2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
  3. Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

Server tool list (12)

Raw names from tools/list. Only developers need these.

initInitialize a new orchestration for a feature. Creates .orchex/active/manifest.yaml with streams.
add_streamAdd a new stream to the active orchestration.
statusGet the current orchestration status and progress.
executeRun the orchestration. Each call executes one wave of streams in parallel via LLM API, applies artifacts to the codebase, and runs verification commands. Call repeatedly for each wave, or use auto mode.
completeMark a stream as complete, or archive the entire orchestration.
recoverRecover streams stuck in in_progress state or failed state. Auto-detects stuck streams and resets them to pending for retry, or skips them.
learnParse a planning document and generate stream definitions. Returns proposed streams for review before initialization.
init-planGenerate an annotated markdown plan template that teaches users how to structure documents for orchex learn. Creates a starter plan file with inline comments explaining what the learn pipeline parses.
autoGenerate a plan from user intent, preview streams, and optionally execute. Flow: gather context → LLM generates plan → learn pipeline → preview → approve → execute → report.
reset-learningReset learning data: thresholds, events, patterns, and/or reports.
rollback-streamRevert file changes made by a specific stream using git.
reloadRestart the MCP server to pick up rebuilt code. MCP client will reconnect automatically.