A sophisticated multi-agent orchestration framework for automating complex task delegation, iterative learning, and multi-stage workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install multi-agent-orchestration
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install multi-agent-orchestration using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Colony Orchestration?
Colony Orchestration is a high-level management layer for Openclaw Skills that enables a decentralized yet coordinated ecosystem of specialized agents. It allows developers to move beyond simple prompts into complex process orchestration where agents like researchers, coders, and analysts collaborate on shared goals. By utilizing this skill, you can automate entire business cycles—from idea validation to product deployment—while maintaining a full audit trail and persistent agent memory.
The framework excels at handling non-linear workflows by providing specialized roles such as pincer for coding, scout for research, and forge for product management. It integrates deeply with Openclaw Skills to ensure that every agent learns from its mistakes through a persistent feedback loop, making your AI workforce more efficient over time.
Colony Orchestration Use Cases
- Managing end-to-end product launches with parallel build and copywriting stages.
- Automated content pipelines that handle research, drafting, and social media promotion.
- High-fidelity business idea validation through multi-agent analysis and financial modeling.
- Autonomous bug triage and resolution workflows with built-in QA verification.
- Deep customer segment research and synthesis for marketing strategy development.
How Colony Orchestration Works
- Dispatch a task using natural language to let the system auto-route the request to the most suitable specialized agent.
- Initiate multi-stage processes where context and outputs are automatically passed from one agent to the next in a defined lifecycle.
- Execute independent stages concurrently using parallel groups to optimize performance and reduce workflow latency.
- Review progress at human-in-the-loop checkpoints, allowing for manual approval or redirection of the process.
- Capture granular audit data and performance metrics to track token usage, success rates, and execution time.
- Provide feedback on completed tasks to update the persistent memory of specific agents, ensuring they evolve based on historical performance.
Colony Orchestration Setup
To get started with this orchestration layer for Openclaw Skills, navigate to the skill directory and install the necessary dependencies.
# Navigate to the skill folder
cd skills/colony
# Install required packages
npm install
# Run a test dispatch to verify agent routing
node scripts/colony.mjs dispatch "Research the current state of vector databases"
Colony Orchestration Data Schema & Taxonomy
The skill organizes its operations through a structured filesystem to ensure data persistence and cross-agent context sharing within Openclaw Skills.
| Path | Purpose |
|---|---|
colony/agents.yaml |
Configuration for agent roles, model selection, and keyword triggers. |
colony/processes.yaml |
Definitions for multi-stage workflows, input/output mappings, and checkpoints. |
colony/memory/ |
Per-agent Markdown files containing persistent lessons, patterns, and preferences. |
colony/audit/ |
JSONL event logs and aggregate statistics for performance monitoring. |
colony/context/ |
Run-specific directories containing output files like research.md or spec.md. |
colony/global-context.json |
Shared facts, project decisions, and user preferences accessible by all agents. |
Colony Orchestration Advanced Features
- Parallel Stage Execution: Group independent tasks to run concurrently, significantly speeding up complex processes.
- Persistent Agent Memory: Specialized memory files for each agent to store mistakes, patterns, and formatting preferences.
- Multi-Agent Retrospectives: Generate insights over 7-14 day periods to identify systemic failure patterns and optimization opportunities.
- Human-in-the-loop Checkpoints: Strategic pauses in automated workflows that require human approval before proceeding to high-stakes stages.
- Global Context Injection: A shared knowledge base that ensures all agents remain aligned with current project facts and architectural decisions.
- Integrated Notification System: Real-time alerts via openclaw cron wake for process completions, failures, or checkpoint requests.
SKILL.md
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