openclaw-upgrade-orchestrator-mcp

Safe-upgrade advisor for OpenClaw. Detects current version, checks the deployment against a hand-curated catalog of version-specific known regressions, captures pre-upgrade snapshots, diffs them against post-upgrade state, and emits step-by-step upgrade + rollback guides. Read-only — never executes the upgrade itself. Companion to silentwatch-mcp, openclaw-health-mcp, openclaw-cost-tracker-mcp, and openclaw-skill-vetter-mcp.

What it does

Production OpenClaw upgrades carry recurring regressions. 2026.4.8 brought a CPU-spike bug. 2026.4.23-26 broke Discord on_message. 2026.4.30+ surfaced an OOM under sustained 200k-token contexts. The pattern: an operator upgrades on Friday, hits a new failure mode on Tuesday, and spends Wednesday-Thursday excavating field reports. This MCP server moves that excavation upfront — before the upgrade, not after.

> claude: should I upgrade my 2026.4.23 deployment?
[MCP tools: current_version + available_upgrades]

Current: 2026.4.23
Recommended target: 2026.5.2 (no CRITICAL regressions in path)

Available upgrades:
  2026.4.24-.26  HIGH    R-73421 Discord-receive breakage
  2026.4.27       —      clean
  2026.4.30       HIGH    R-OOM-DURING-LARGE-CONTEXT (unfixed)
  2026.5.1-.2     HIGH    R-OOM + R-LOG-ROTATION-DROP (unfixed)

> claude: walk me through upgrading to 2026.4.27.
[MCP tool: upgrade_guide]

2026.4.23 → 2026.4.27 — proceed with mitigations applied.

Applicable known regressions:
  R-41372 (HIGH)  — Cron --session web-search silent fail.
                    Mitigation: silentwatch-mcp covers detection until upgrade.
  R-73421 (HIGH)  — Discord-receive callbacks not firing.
                    Mitigation: `openclaw skill reload discord` after upgrade.

Pre-upgrade steps:
  1. Capture pre-upgrade snapshot (call pre_upgrade_snapshot)
  2. Verify backups: cp -r ~/.openclaw ~/.openclaw.backup-$(date +%Y%m%d)

Upgrade steps:
  1. openclaw gateway stop
  2. openclaw upgrade --to 2026.4.27
  3. openclaw gateway start

Post-upgrade steps:
  1. Run post_upgrade_verify(snapshot_id=<your-pre-upgrade-id>)
  2. openclaw skill reload discord  (R-73421 mitigation)

Rollback steps: stop → openclaw upgrade --to 2026.4.23 → restore backup → start.

Confidence: Path includes 2 HIGH regressions but no CRITICAL.

> claude: I just upgraded. Verify it.
[MCP tool: post_upgrade_verify(pre_snapshot_id="snap-...")]

Upgrade 2026.4.23 → 2026.4.27: SUCCESS.
0 new failures, 1 recovered (skills.discord_receive_registered),
0 unchanged failures.

Why openclaw-upgrade-orchestrator-mcp

Three things existing tools (vendor changelogs, internal runbooks, generic CI/CD orchestrators) don't do:

1. Catalog-grounded regression awareness. A generic upgrade tool tells you the version exists. This server tells you which versions have known issues, which fix versions remediate them, and which mitigations apply if you have to use the affected version.

2. Pre/post snapshot diffing tied to the catalog. The same checks run before + after the upgrade. The diff highlights new_failures (caused by the upgrade) separately from unchanged_failures (pre-existing) and recovered (fixed by the upgrade). No more "did this break in 2026.4.27 or was it already broken?"

3. Read-only by design. Never runs openclaw upgrade --to ... for you. Never modifies state. Operators retain full agency over the actual upgrade — this server gives them the information to make the decision, then verifies it after they execute.

Built for the production-AI operator who owns OpenClaw deployments and has been through enough upgrade-day fire drills.

Tool surface

Resources:

• upgrade://current — current version info

• upgrade://snapshots — every stored snapshot

• upgrade://catalog — full regression catalog

Prompts:

• plan-upgrade(target_version) — walks through the upgrade decision

• verify-upgrade(pre_snapshot_id) — walks through post-upgrade verification

Quickstart

Install

pip install openclaw-upgrade-orchestrator-mcp

Configure for Claude Desktop

{
  "mcpServers": {
    "openclaw-upgrade": {
      "command": "python",
      "args": ["-m", "openclaw_upgrade_orchestrator_mcp"],
      "env": {
        "OPENCLAW_UPGRADE_BACKEND": "mock"
      }
    }
  }
}

Backends

Regression catalog (v1.0)

8 hand-curated entries covering documented OpenClaw regressions:

• R-41372-CRON-WEB-SEARCH-SILENT-FAIL (HIGH, 2026.4.20–2026.5.1)

• R-63002-POST-UPGRADE-CPU-SPIKE (CRITICAL, 2026.4.8–2026.4.10)

• R-73421-DISCORD-RECEIVE-BREAKAGE (HIGH, 2026.4.23–2026.4.27)

• R-GATEWAY-PORT-CONFLICT-2026.4.15 (MEDIUM, 2026.4.15–2026.4.18)

• R-OOM-DURING-LARGE-CONTEXT-2026.4.30 (HIGH, 2026.4.30–unfixed)

• R-STATUS-RECONCILIATION-DRIFT-2026.4.5 (LOW, 2026.4.5–2026.4.10)

• R-CLAWHUB-CACHE-POISONING-2026.3.28 (HIGH, 2026.3.28–2026.4.2)

• R-LOG-ROTATION-DROP-2026.5.1 (MEDIUM, 2026.5.1–unfixed)

Use regression_catalog for the full, queryable list.

Risk-aware recommendation logic

available_upgrades flags every version reachable from current and computes a recommended_target:

For each available version V > current:
  applicable_regressions = regressions_in_path(current, V)
  has_known_critical = any(r.severity == CRITICAL for r in applicable_regressions)

recommended_target = highest V with has_known_critical == False

regressions_in_path(current, target) includes a regression if:

• The target version is in the regression's range (post-upgrade deployment will be affected), OR

• The current version is in the regression's range (current deployment is already affected — the operator should know whether the upgrade fixes it)

OpenClaw upgrades atomically (no execution on intermediate versions), so a regression strictly between current and target without affecting either endpoint is NOT included. This avoids over-conservative recommendations.

Roadmap

Need this adapted to your stack?

If your AI deployment uses a different runtime (custom agent harness, internal fork of OpenClaw, vendor-locked deployment) and you want the same regression-aware upgrade discipline, that's a Custom MCP Build engagement.

To engage:

1. Email temur@pixelette.tech with subject Custom MCP Build inquiry — upgrade orchestration

2. Include: 1-paragraph description of your runtime + which tier

3. Reply within 2 business days with a 30-min discovery call slot

This server is part of a production-AI infrastructure MCP suite — companion to silentwatch-mcp, openclaw-health-mcp, openclaw-cost-tracker-mcp, and openclaw-skill-vetter-mcp. Install all five for full operational visibility.

Production AI audits

If you're running production AI and want an outside practitioner to score readiness, find the failure patterns already present (upgrade regression cycles being one of the most damaging), and write the corrective-action plan:

Same email channel: temur@pixelette.tech with subject AI audit inquiry.

Contributing

PRs welcome. Detection checks are pluggable — see src/openclaw_upgrade_orchestrator_mcp/checks/__init__.py for the contract.

To add a check:

1. Define def run(state: DeploymentState) -> CheckResult in the checks module

2. Register it in CHECKS: dict[str, callable]

3. Reference its check_id from a regression's detection_check_id in catalog.py

4. Add tests in tests/test_checks.py

To add a backend:

1. Subclass UpgradeBackend in backends/<your_backend>.py

2. Implement collect_state, save_snapshot, load_snapshot, list_snapshots

3. Register in backends/__init__.py

4. Add tests in tests/test_backends.py

To add a regression entry:

1. Append to CATALOG in catalog.py with stable regression_id

2. Reference an existing or new detection_check_id (or set to None for advisory-only)

3. Add a test confirming version-range membership in tests/test_catalog.py

Bug reports + feature requests: open a GitHub issue.

License

MIT — see LICENSE.

Related

• silentwatch-mcp — cron silent-failure detection

• openclaw-health-mcp — deployment health

• openclaw-cost-tracker-mcp — token-cost telemetry

• openclaw-skill-vetter-mcp — skill security vetting

• AI Production Discipline Framework — Notion template, $29 — methodology these MCPs implement

• SPEC.md — full server design

Built by Temur Khan — independent practitioner on production AI systems. Contact: temur@pixelette.tech