mergetrain 0.1.0

A local-first merge-and-deploy queue for coding-agent worktrees

mergetrain

A local-first merge-and-deploy queue for coding-agent worktrees.

mergetrain keeps its queue, coordination, merge assembly, and gate execution on your machine. Coding agents commit in separate worktrees; one local runner serializes their branches, validates the exact train, and pushes only after explicit approval. No hosted merge-queue service or CI provider is required.

Local-first, not local-only. Queue state, locking, train assembly, and gates stay local. Configured Git remotes and post-deploy verification may still use external services.

Status: alpha (v0.1.0). The core is implemented and tested; interfaces may still change. Built to scratch my own itch first — published in case it scratches yours too.

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The problem

When several Codex/Claude/LLM sessions work on the same repo at the same time, each in its own worktree and branch, a few things break down:

• It's unclear what order branches should land on the deploy branch.
• If an agent runs git push itself, sessions overwrite each other or ship unverified combinations.
• Each branch passes its own tests, but the merge of several branches in sequence can still be broken.
• Conflicts, stale locks, duplicate enqueues, and "is the daemon allowed to deploy this?" all become judgment calls — and you do not want an LLM guessing at those.

Hosted merge queues (GitHub Merge Queue, GitLab Merge Trains, Mergify, Aviator, bors) solve a related problem, but they are PR-first, remote-CI-first, and platform-first. mergetrain is for the other workflow: local-agent, worktree-first, deploy-branch-first.

How it works

  agent A ─┐
  agent B ─┼─▶  mergetrain queue (SQLite)  ─▶  one runner (lock)
  agent C ─┘                                      │
                                                  ▼
                          fresh integration worktree @ origin/main
                                merge A → B → C  (the train)
                                          │
                            gates (diff-check, tests, scans…)
                                          │
                              git push --atomic  →  deploy refs
                                          │
                                  post-push verify hooks

Agents commit their work and enqueue a branch. They never push deploy refs themselves. A single runner (or unattended daemon) claims the queue, builds a throwaway integration worktree on top of your integration branch, merges the queued branches in FIFO order, runs your gates once over the whole train, and only then pushes — atomically — to your deploy refs. Every important state is readable as JSON so an agent can follow the result instead of inferring it.

Quickstart

# Install the public alpha
python -m pip install mergetrain

# 1. Scaffold config + agent docs in your repo
mergetrain init --project my-app --write

# 2. An agent finishes work, commits, and enqueues its branch
mergetrain enqueue --task "add health check" --branch agent/health --capture-sha

# 3. See the queue and lock state (machine-readable)
mergetrain status --json

# 4. Watch the queue and runner locally (read-only)
mergetrain dashboard

# 5. Validate the whole train without shipping
mergetrain run-batch --validate-only

# 6. Ship — explicit, never implicit
mergetrain run-batch --deploy

For an unreleased source checkout, use python -m pip install -e . instead.

The dashboard is served at http://127.0.0.1:8765/. It streams structured runner phases, heartbeat freshness, job order, blocked reasons, recent activity, the exact current gate and command template, and the next safe action. CONNECTED describes the browser's data stream; RUNNER ACTIVE separately describes the process that owns the train. It has no mutation endpoints or deploy controls.

Validation records an exact train identity, including every task HEAD and the integration base used for the check. The later deploy reassembles that same train on the current integration ref, reruns all gates, and refuses changed task branches. Newly queued work is not silently added to the approved train.

Every agent-facing command is non-interactive and requires explicit intent: --validate-only or --deploy, never a bare run-batch.

Core concepts

• Job — one task branch waiting in the queue, with the SHAs captured at enqueue time.
• Validated train — an exact, deployable group of jobs that passed gates together and is waiting for explicit deploy approval.
• Runner lock — gives every claim a unique lease token, heartbeats through long-running commands, and prevents a stale runner from overwriting a newer owner.
• Integration worktree — a disposable, detached Git worktree built on your integration ref. The runner merges here, so agents never checkout or push the deploy branch.
• Gate — a verification command (diff-check, tests, secret-scan…) run once over the assembled train before push. A gate failure means nothing ships.
• Verify hook — a command run after push to confirm the deploy is live.
• Auto job — a job enqueued with --auto, the only kind the unattended daemon will touch. Manual jobs are left for a human-initiated runner.

Full reference in docs/design.md and the CLI reference.

When to use mergetrain

Your workflow is…	Use
PR-first, remote-CI-first, hosted platform	GitHub / GitLab merge queue, Mergify, Aviator
Local agents in worktrees shipping to a deploy branch	mergetrain

mergetrain is not a general-purpose job queue (it won't replace Celery/RQ/Sidekiq), a CI provider, or a deploy provider. The core is provider-neutral: your push targets, test commands, and deploy checks live in config, not in mergetrain.

Configuration

A single .mergetrain.yaml at your repo root holds all policy. The core stays neutral; you bring the commands.

project:
  name: my-app

git:
  remote: origin
  integration_branch: main
  push_refs: [main]          # atomic push targets on deploy

queue:
  lock_ttl_minutes: 30
  heartbeat_interval_seconds: 10
  command_timeout_seconds: 3600

gates:
  - name: diff-check
    run: git diff --check ${integration_ref}..HEAD
  - name: tests
    run: python -m pytest

deploy:
  verify:
    - name: live-health
      run: curl -fsS https://example.invalid/health

See the config reference for the full schema, placeholders, and environment variables.

For AI agents

mergetrain is designed so an agent can operate it from a short contract and JSON output, without guessing:

1. Work on a task-specific branch in its own worktree.
2. Commit before enqueuing.
3. Never push deploy refs directly.
4. Read mergetrain doctor --json / status --json before acting.
5. Use --auto only after explicit human approval for unattended deploys.
6. Let one runner or daemon own merge → test → push → verify.
7. Fix blocked/failed work on the owning branch and enqueue a fresh clean job.

mergetrain init writes AGENTS.mergetrain.md / CLAUDE.mergetrain.md so your agents pick this up automatically.

Documentation

• Quickstart · Install
• CLI reference — every command and flag
• Config reference — .mergetrain.yaml schema, placeholders, env vars
• Design & architecture — the model, data model, and safety guarantees
• Daemon · Failure modes — operating it day to day
• Manage from your phone — drive mergetrain via Cowork Dispatch
• Agent contract — the rules agents follow
• Security · Adapter pattern · Development · Release

Status

v0.1.0, alpha. The core — queue, runner lock, merge train, gates, atomic push, auto-only daemon, JSON doctor/status, and the local read-only dashboard — is implemented with a passing test suite. Built for my own multi-agent workflow first; issues and ideas welcome. Review your config trust boundary, gate commands, and secret handling before enabling unattended deploys — see security.

License

Released under the MIT License.