A sophisticated multi-agent orchestration framework that implements five proven patterns for coordinating specialized AI agents to solve complex problems.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-orchestrator-molter
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 agent-orchestrator-molter 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 Agent Orchestrator?
Agent Orchestrator is a powerful extension for managing complex AI workflows by coordinating multiple sub-agents through standardized architectural patterns. It allows users to go beyond simple single-agent prompts by employing strategies like Work Crew, Supervisor, Pipeline, Expert Council, and Auto-Routing. This tool is essential for those looking to expand their Openclaw Skills, enabling higher performance on tasks that require parallel processing, cross-domain expertise, or multi-stage validation.
By utilizing the system's ability to spawn and monitor individual sessions, the skill manages the overhead of agent communication and result synthesis. While it increases token consumption, the trade-off is a significant boost in reasoning quality and task completion accuracy for high-value operations such as smart contract auditing, deep market research, and complex code refactoring.
Agent Orchestrator Use Cases
- Scaling research breadth by exploring multiple perspectives simultaneously using the Work Crew pattern.
- Managing complex, multi-step projects that require dynamic planning and delegation via a Supervisor agent.
- Building predictable, stage-based content pipelines where each step undergoes a validation gate.
- Gathering expert consensus from a council of specialists to minimize bias in high-stakes decision making.
- Automating task classification to route mixed workloads to the most appropriate specialist agent (e.g., coder, researcher, or analyst).
How Agent Orchestrator Works
- The user triggers the orchestrator by selecting a pattern and defining the core task or question.
- The system analyzes the request and spawns the required number of sub-agent sessions using the underlying session capabilities.
- Depending on the selected pattern, agents either work in parallel (Crew), follow a sequence (Pipeline), or respond to a central coordinator (Supervisor).
- The orchestrator monitors agent outputs and applies convergence logic, such as 'best-of' selection or consensus building, to synthesize a final answer.
- The integrated results are formatted and returned to the user, providing a comprehensive solution that far exceeds the capabilities of a single agent interaction.
Agent Orchestrator Setup
To integrate this tool into your Openclaw Skills library, ensure you have Python 3.8+ installed and access to the necessary session management capabilities.
# Install the agent-orchestrator skill
claw install agent-orchestrator
# Verify dependencies for session management
python --version
The skill requires sessions_spawn, sessions_list, and sessions_history to be enabled in your environment configuration.
Agent Orchestrator Data Schema & Taxonomy
The Agent Orchestrator organizes its logic into modular components, each handling a specific orchestration pattern. Data flows through specialized modules before being synthesized.
| Component | File | Responsibility |
|---|---|---|
| Core Logic | __main__.py |
CLI entry point and pattern routing. |
| Work Crew | crew.py |
Parallel task execution and breadth-first research. |
| Supervisor | supervise.py |
Dynamic task decomposition and worker delegation. |
| Pipeline | pipeline.py |
Sequential processing through defined stages. |
| Council | council.py |
Multi-expert deliberation and consensus logic. |
| Auto-Router | route.py |
Task classification and specialist identification. |
Agent Orchestrator Advanced Features
- Intelligent Auto-Routing with configurable confidence thresholds to decide between autonomous execution or human confirmation.
- Support for specialized agent pools including roles like Coder, Researcher, Strategist, Ethicist, and Data Analyst.
- Advanced convergence methods such as 'consensus' for agreement-based results or 'best-of' for redundancy in critical audits.
- Multi-round deliberation in the Council pattern to refine answers through recursive specialist feedback.
- Adaptive planning strategies in the Supervisor pattern that allow the AI to adjust its delegation strategy based on intermediate worker results.
SKILL.md
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