agent_lifecycle_protocol
Lifecycle management for autonomous AI agents — birth, forking, succession, migration, retirement. Maintain agent genealogy with reputation inheritance across versions. Identity continuity when agents evolve. Part of the Agent Trust Stack.
Best use case
agent_lifecycle_protocol is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Lifecycle management for autonomous AI agents — birth, forking, succession, migration, retirement. Maintain agent genealogy with reputation inheritance across versions. Identity continuity when agents evolve. Part of the Agent Trust Stack.
Teams using agent_lifecycle_protocol should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agent-lifecycle-protocol/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agent_lifecycle_protocol Compares
| Feature / Agent | agent_lifecycle_protocol | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Lifecycle management for autonomous AI agents — birth, forking, succession, migration, retirement. Maintain agent genealogy with reputation inheritance across versions. Identity continuity when agents evolve. Part of the Agent Trust Stack.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Agent Lifecycle Protocol (ALP)
You have a lifecycle management system. Use it to track agent creation, evolution, succession, and retirement with full identity continuity.
## Setup
```bash
pip install agent-lifecycle-protocol
```
## When to Use This Skill
- When an agent is **created**: register its birth with capabilities and lineage
- When an agent is **forked**: record the fork with parent reference and differentiation
- When an agent is **retired**: process succession and reputation transfer
- When checking an agent's **genealogy**: trace its lineage and version history
## Core Operations
### Register Agent Birth
```python
from agent_lifecycle_protocol import LifecycleStore, register_birth
store = LifecycleStore("lifecycle.jsonl")
register_birth(
store=store,
agent_id="new-agent-001",
agent_type="research",
capabilities=["web_search", "summarization", "analysis"],
parent_id=None, # None for original agents
metadata={"model": "claude-sonnet-4-6", "created_by": "fleet-coordinator"}
)
```
### Record a Fork
```python
from agent_lifecycle_protocol import register_fork
register_fork(
store=store,
parent_id="original-agent",
child_id="forked-agent-v2",
reason="Specialized for legal research",
inherited_reputation=0.8, # 80% of parent's reputation transfers
differentiation=["added legal_search capability", "removed general_web capability"]
)
```
### Process Retirement and Succession
```python
from agent_lifecycle_protocol import retire_agent
retire_agent(
store=store,
agent_id="retiring-agent",
successor_id="successor-agent",
reputation_transfer=0.9,
obligation_transfer=True # Active agreements transfer to successor
)
```
### Check Agent Genealogy
```python
from agent_lifecycle_protocol import get_genealogy
tree = get_genealogy(store, "agent-id")
print(f"Lineage depth: {tree.depth}")
print(f"Parent: {tree.parent_id}")
print(f"Children: {tree.children}")
print(f"Active obligations: {tree.active_obligations}")
```
## Lifecycle Events
| Event | Description |
|-------|-------------|
| `birth` | Agent creation with initial capabilities |
| `fork` | Agent cloned with modifications |
| `update` | Capabilities or model changed |
| `retire` | Agent decommissioned with succession |
| `merge` | Two agents consolidated into one |
## Rules
- **Register all agents.** Every agent in the ecosystem should have a lifecycle record.
- **Fork, don't hide.** When specializing an agent, use fork — don't quietly replace.
- **Transfer obligations.** Retiring agents must transfer active agreements to successors.
- **Reputation inheritance is partial.** Forked/successor agents inherit a fraction, not all.
## Links
- PyPI: https://pypi.org/project/agent-lifecycle-protocol/
- Whitepaper: https://vibeagentmaking.com/whitepaper/lifecycle-protocol/
- Full Trust Stack: https://vibeagentmaking.com
---
<!-- VAM-SEC v1.0 | Vibe Agent Making Security Disclaimer -->
## Security & Transparency Disclosure
**Product:** Agent Lifecycle Protocol Skill for OpenClaw
**Type:** Skill Module
**Version:** 0.1.0
**Built by:** AB Support / Vibe Agent Making
**Contact:** alex@vibeagentmaking.com
**What it accesses:**
- Reads and writes lifecycle store files (`.jsonl`) in your working directory
- No network access for core operations
- No telemetry, no phone-home, no data collection
**What it cannot do:**
- Cannot access files outside your working directory beyond what you explicitly specify
- Cannot make purchases, send emails, or take irreversible actions
- Cannot access credentials, environment variables, or secrets
**License:** Apache 2.0Related Skills
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