Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
A multi-agent orchestration framework built on LangGraph, CrewAI, OpenAI Agents SDK, and Pydantic AI. It runs a persistent 4-member agent council with 5-layer memory, 4-tier model fallback, and a self-improvement engine that scans for updated AI patterns nightly. Security runs through 17 named gates covering non-harm, consent, reversibility, and similar constraints.
Bundles orchestration, multi-layer memory, model fallback, and security constraints into one package, avoiding the need to wire separate libraries together for each concern.
When to use it
- Running persistent background agents that monitor events overnight
- Building pipelines with automatic model fallback when an LLM API goes down
- Enforcing ethical guardrails on agent actions in production workloads
- Managing agent memory across sessions without excessive token costs
- Coordinating specialized sub-agents on long-running research tasks
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Research 2026 AI framework trends and save findings for later
- 1Activate the persistent Council via Council().activate()
- 2Spawn a Specialist agent with role set to 'researcher' and the target task defined
- 3Agent executes the research task, reading from and writing to the 5-layer memory stack
- 4Dharmic gates validate each action for consent, reversibility, and non-harm before execution
- 5Results are stored in Mem0 and Zep layers for retrieval in future sessions
Research findings returned and persisted across memory layers, accessible in subsequent agent runs without re-running the task
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.