GitHub
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
Beschreibung vom Entwickler (englisches Original)
Queue parallel-agent worktree branches, test together, serialize and recover Git pushes.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"mergetrain": {
"command": "uvx",
"args": [
"mergetrain",
"mcp"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add mergetrain -- uvx mergetrain mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"mergetrain": {
"command": "uvx",
"args": [
"mergetrain",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"mergetrain": {
"type": "stdio",
"command": "uvx",
"args": [
"mergetrain",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto yongjip bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (3.7.1) kam vor 12 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Python (uv) auf deinem Computer.
In derselben Kategorie: GitHub, Desktop Commander, Cloudflare MCP
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
MCP server for terminal commands, file operations, and process management
Cloudflare MCP servers
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
Safely integrate committed branches from parallel coding agents.
mergetrain is a local merge queue for parallel LLM agents. These coding agents use large language models and work in Git worktrees. mergetrain runs on your machine. A train is a group of queued task branches. A gate is a configured test or check. Each agent commits and enqueues its branch. One runner merges branches in enqueue order and runs gates on the combined result. It updates your Git refs atomically only after explicit approval.
mergetrain is designed for one operator managing local repositories.
There is no mergetrain account, hosted control plane, OAuth app, or product telemetry. Queue and runner state stay on your machine. Only your configured Git remote and trusted gate or verification commands may contact external services.
Default batch mode: passing work lands first. Work that needs a fix returns to its agent. Passing work can land while the repair continues.
Keep building during checks. Agents start their next task while mergetrain checks earlier work. This 14-second loop keeps the same four agents in view.
Agents can keep building in both modes. In ordered mode, an unresolved earlier submission holds later landings. Compare the two modes.
Pause or replay the demo · View the still image · Reproduce the continuous workflow
How this demo was made
This illustration follows recorded CLI results. The four lanes represent agent roles; letters identify work, not queue job IDs. The capture scripts agent-authored changes and operator approvals in a disposable local repository and bare remote. It does not launch an AI service.
Two fixture gate waits make the overlaps reproducible: E and F are committed during A and B validation, then G and H drafts start during E and F validation. All three deployments retain approval and the default recheck. A fresh clone of the final remote passes six tests.
G and H then refresh onto that result without losing their edits. I and J start there too. These four drafts remain unsubmitted. Your agents or operator create, fix, refresh and resubmit work; mergetrain checks and integrates it. The 14-second animation repeats this recorded window with condensed timing.
Worktrees let several agents edit one repository without sharing a checkout. They do not decide landing order, test the combined result, prevent push races, or tell you what happened if a laptop dies mid-push.
Without a shared integration workflow, the operator chooses branch order and resolves cross-branch failures. The operator also reruns tests and controls pushes. Repeating these actions can slow parallel work.
mergetrain records this workflow in a durable local queue:
agent branches → local queue → isolated integration worktree → combined gates
→ explicit approval → one atomic push → post-push verification
Use mergetrain when:
It is harness-agnostic: Codex, Claude Code, agy, scripts, and humans all use the same CLI and JSON contract.
Running several Claude Code or Codex sessions on one laptop? The one-laptop guide covers worktrees, unattended landing, heavy gates, sleep and quiet periods, a push guard, and outcome reports, each tested in disposable repositories.
You probably do not need mergetrain when:
For PR-first teams, use GitHub Merge Queue or GitLab Merge Trains. mergetrain is for local-agent, worktree-first integration, with or before a PR.
Start with `batch`, the default. It suits independent work from parallel agents. The runner combines the queued work available when it starts; it does not wait for every agent to finish. Compatible work can land while an agent repairs a failed submission.
Use ordered when each change needs its own landing and the original submission order must survive a failure and retry.
| Comparison | batch (default) | ordered (optional) |
|---|---|---|
| Agent work example | A fixes login, B adds search filters, C updates the admin screen | A, B, and C make compiler optimizations; measure each change against the previous landed result |
| Landing unit | Available submissions together | One submission at a time |
| If B fails | After isolating B and passing checks on A+C, A+C can land | A can land; C waits until B is repaired and lands, or is explicitly canceled |
B after retry | Joins the back of the queue | Keeps B's original position, before C |
Both modes let agents start their next task after handoff. Both keep deploy approval and the default pre-push recheck. Ordered processing can run checks more often because each submission lands separately. Queue order starts at submission, not when an agent starts work, and does not express code dependencies. Order ends at a successful Git landing. Post-push verification warnings remain visible while later work proceeds; an uncertain push still requires reconciliation.
Availability: ordered is available in 3.6.0 and newer. Keep the default batch for ordinary parallel agent work. See the agent work examples, configuration, and upgrade procedure.
Lease tokens fence concurrent and stale mergetrain runners. They do not intercept an arbitrary git push from a task agent that has shell access and an integration-branch credential. To make “one runner owns the push” an enforced property rather than a protocol assumption, use this topology:
task agents: commit + exact-SHA enqueue; no integration push credential
runner: separate deploy identity remote: protected integration branch; runner or reviewed PR path only
Without credential separation and remote protection, mergetrain still provides safe train assembly and recovery semantics, but it cannot prevent a participant from bypassing the queue. See the security boundary.
uvx mergetrain demo
The demo creates a disposable repository and local bare remote, then runs four real branches through merges in enqueue order, a combined-only gate failure, conflict attribution, and deployment of the compatible train. Use --keep to inspect the result afterward. Add --brief --pause to read each step before continuing. The extended workflow recording adds repair, work committed during validation, the next batch, and three verified local deployments.
Full CLI recording
# Install the machine-level CLI
uv tool install mergetrain # or: pipx install mergetrain
codex plugin marketplace add yongjip/mergetrain --ref main codex plugin add mergetrain@mergetrain
agy plugin install https://github.com/yongjip/mergetrain
For VS Code / GitHub Copilot or direct MCP setup, see the MCP installation and deployment-confirmation guide.
Initialize the repository:
cd /path/to/your/repo
mergetrain init --project my-app --write
Add at least one project gate to .mergetrain.yaml. A new configuration has gates: [], so it runs no project tests. For a Python project that uses pytest:
gates:
Use test commands for your project. Gates must leave the assembled checkout unchanged. init also creates AGENTS.mergetrain.md and CLAUDE.mergetrain.md. Link them from your existing AGENTS.md or CLAUDE.md. Commit and land the configuration and instructions through your normal integration path before creating task worktrees.
After the task agent commits a clean branch in its own worktree:
mergetrain status --json
mergetrain enqueue \ --task "add health check" \ --branch agent/health
The task agent stops after enqueueing. From the operator checkout, run these commands when validation and deployment are authorized:
mergetrain status
mergetrain deploy
For long-running gates, run mergetrain validate earlier; it never pushes and leaves one exact train Ready for the later deploy confirmation.
deploy names the configured atomic Git ref update; it does not imply an App Store, Kubernetes, or other provider release.
Agents commit and enqueue. One runner owns merge → test → push → verify. Unattended daemons process only jobs that a human explicitly enqueued with --auto. For manual jobs, daemon --validate-only can run merge and gates in the background, but it pauses at the validated-train approval boundary and never pushes.
See the quickstart for configuration, daemon, and multi-repository Hub setup.
Use a Git worktree merge queue when parallel Claude Code sessions or parallel Codex worktrees produce committed branches in the same repository. The plugin install guide connects either client to the queue. Task agents commit, enqueue, and stop. A separately authorized runner tests the combined branches and handles the push.
For Orca-managed worktrees, see the Orca setup and queue guide for tested terminal commands, shared queue behavior, validation, and repair. The Orca agent guide adds native Codex, Claude Code,
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