Agent Lifecycle

Manage the lifecycle of autonomous agents and their skills. Version configurations, plan upgrades, track retirement, and maintain change history across agent...

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.