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ECC/commands/devfleet.md

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description
Orchestrate parallel Claude Code agents via Claude DevFleet — plan projects from natural language, dispatch agents in isolated worktrees, monitor progress, and read structured reports.

DevFleet — Multi-Agent Orchestration

Orchestrate parallel Claude Code agents via Claude DevFleet. Each agent runs in an isolated git worktree with full tooling.

Requires the DevFleet MCP server: claude mcp add devfleet --transport http http://localhost:18801/mcp

Flow

User describes project
  → plan_project(prompt) → mission DAG with dependencies
  → Show plan, get approval
  → dispatch_mission(M1) → Agent spawns in worktree
  → M1 completes → auto-merge → M2 auto-dispatches (depends_on M1)
  → M2 completes → auto-merge
  → get_report(M2) → files_changed, what_done, errors, next_steps
  → Report summary to user

Workflow

  1. Plan the project from the user's description:
mcp__devfleet__plan_project(prompt="<user's description>")

This returns a project with chained missions. Show the user:

  • Project name and ID
  • Each mission: title, type, dependencies
  • The dependency DAG (which missions block which)

  1. Wait for user approval before dispatching. Show the plan clearly.

  2. Dispatch the first mission (the one with empty depends_on):

mcp__devfleet__dispatch_mission(mission_id="<first_mission_id>")

The remaining missions auto-dispatch as their dependencies complete (because plan_project creates them with auto_dispatch=true). When manually creating missions with create_mission, you must explicitly set auto_dispatch=true for this behavior.

  1. Monitor progress — check what's running:
mcp__devfleet__get_dashboard()

Or check a specific mission:

mcp__devfleet__get_mission_status(mission_id="<id>")

Prefer polling with get_mission_status over wait_for_mission for long-running missions, so the user sees progress updates.

  1. Read the report for each completed mission:
mcp__devfleet__get_report(mission_id="<mission_id>")

Call this for every mission that reached a terminal state. Reports contain: files_changed, what_done, what_open, what_tested, what_untested, next_steps, errors_encountered.

All Available Tools

Tool Purpose
plan_project(prompt) AI breaks description into chained missions with auto_dispatch=true
create_project(name, path?, description?) Create a project manually, returns project_id
create_mission(project_id, title, prompt, depends_on?, auto_dispatch?) Add a mission. depends_on is a list of mission ID strings.
dispatch_mission(mission_id, model?, max_turns?) Start an agent
cancel_mission(mission_id) Stop a running agent
wait_for_mission(mission_id, timeout_seconds?) Block until done (prefer polling for long tasks)
get_mission_status(mission_id) Check progress without blocking
get_report(mission_id) Read structured report
get_dashboard() System overview
list_projects() Browse projects
list_missions(project_id, status?) List missions

Guidelines

  • Always confirm the plan before dispatching unless the user said "go ahead"
  • Include mission titles and IDs when reporting status
  • If a mission fails, read its report to understand errors before retrying
  • Agent concurrency is configurable (default: 3). Excess missions queue and auto-dispatch as slots free up. Check get_dashboard() for slot availability.
  • Dependencies form a DAG — never create circular dependencies
  • Each agent auto-merges its worktree on completion. If a merge conflict occurs, the changes remain on the worktree branch for manual resolution.