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.