johnalbertini14-glitch Agent Lifecycle Manager
Track your agent's evolution from deployment to retirement. Version configurations, plan skill upgrades, and maintain a complete change history.
Why This Exists
Agents evolve constantly — new skills installed, old ones retired, configurations changed, models swapped. Without lifecycle tracking, you cannot answer: "What was my agent running last Tuesday?" or "What changed when things broke?"
Commands
Snapshot current agent state
python3 {baseDir}/scripts/lifecycle.py snapshot --name "pre-upgrade"
Compare two snapshots
python3 {baseDir}/scripts/lifecycle.py diff --from "pre-upgrade" --to "post-upgrade"
List all snapshots
python3 {baseDir}/scripts/lifecycle.py list
Rollback to a snapshot
python3 {baseDir}/scripts/lifecycle.py rollback --to "pre-upgrade" --dry-run
Track a skill retirement
python3 {baseDir}/scripts/lifecycle.py retire --skill old-skill --reason "Replaced by new-skill v2"
View change history
python3 {baseDir}/scripts/lifecycle.py history --limit 20
What It Tracks
- Installed skills: Name, version, install date, last used
- Configuration state: Environment vars, model assignments, feature flags
- Change events: Installs, updates, removals, config changes
- Retirement log: Why skills were removed, what replaced them
- Snapshots: Point-in-time captures of full agent state
Data Storage
Lifecycle data is stored in ~/.openclaw/lifecycle/ as JSON files.